DeepSeek V4.1 model by DeepSeek

DeepSeek V4.1 Flash

DeepSeek's smallest model in its V4.1 architecture family, with native visual understanding, served on the DeepSeek API as deepseek-flash. Since October 17, 2026 that name serves the DeepSeek-V4.1-Flash-1017 build. Weights under the MIT license.

Official page

Where it sits in DeepSeek's lineup

DeepSeek V4.1DeepSeek's V4.1 family for frontier reasoning and software work.This model
DeepSeek V4DeepSeek's V4 family of open-weight mixture-of-experts models.DeepSeek V4 Pro

Stack role: Foundation Model / Reasoning Engine
Offered in: 3 products
Made by: DeepSeek
Family: DeepSeek V4.1, version 4.1
Released: Sep 10, 2026 Current
In its family: The only DeepSeek V4.1 release we track.

Strengths and weaknesses

Compare with other models Up to 3 beside DeepSeek V4.1 Flash

DeepSeek V4.1 Flash

Suggested comparisons

The models a buyer weighs against DeepSeek V4.1 Flash, picked on list price and on Artificial Analysis's Intelligence Index of overall quality. Add one to draw it beside DeepSeek V4.1 Flash.

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DeepSeek V4.1 FlashDeepSeekThis page, always on the chart

Among the 40 current releases on this chart, DeepSeek V4.1 Flash ranks 10th of 38 on Tool use, third of 8 on Instruction following and 13th of 38 on Long context. On Computer use, Moonshot AI's runs on OSWorld 2.1 put DeepSeek V4.1 Flash 10.3 points under Kimi K3.1 Pro. Answers in full: published only for another harness, setting or build, listed in the table.

Each axis is one public benchmark at one setting, on its own fixed 0 to 100 scale, so adding a model never moves another. A dotted bridge crosses a missing result, which is never a zero. A hollow mark is a result from another source: the maker's own figure or another lab's run, where the benchmark's usual source has not tested the model yet. The readout names it.

On the chart

DeepSeek V4.1 FlashThis pageResults on 11 of 12 axes$0.30 / $1.20Peak-hours rate; off-peak $0.15 / $0.60, as DeepSeek lists it.177 tokens/sFirst token in 0.9 s

Prices and speeds read between Oct 27, 2026 and Oct 28, 2026.

DeepSeek V4.1 Flash on each axis11 of 12 with a result

The share of Terminal-Bench 4.0's long terminal tasks the model finishes on its own: services, kernels, proofs and data pipelines, graded on the final artifact with no partial credit.
Terminal-Bench 4.0, pass@1 averaged over 3 runs, mini-swe-agent harness
- DeepSeek V4.1 Flash31.8%Vals.ai ranked the October 17 build 19th on its leaderboard of Oct 24, 2026.
The share of SciCode's scientific programming subproblems the model's code solves on the first attempt.
SciCode, subproblem accuracy, pass@1, highest effort run
- DeepSeek V4.1 Flash55.9%
The share of FrontierMath's held-out research-grade problems (tiers 1 to 3) the model answers correctly.
FrontierMath Tiers 1-3, accuracy, pass@1, highest effort run
- DeepSeek V4.1 Flash71.2%
The share of GPQA Diamond's graduate-level biology, chemistry and physics questions the model answers correctly.
GPQA Diamond, accuracy, pass@1, highest effort run
- DeepSeek V4.1 Flash93.2%
The share of Humanity's Last Exam's 2,158 text-only questions, expert-written at the edge of human knowledge, the model answers correctly without tools.
Humanity's Last Exam, accuracy, text-only questions, no tools
- DeepSeek V4.1 Flash43.3%
The share of each AutomationBench task's objectives the model completes by calling REST APIs across simulated business apps (Gmail, Sheets, Slack, Salesforce, Zendesk, Jira, HubSpot), averaged over tasks. A task that breaks one of its policy guardrails scores zero.
AutomationBench-AA, score, Artificial Analysis run
- DeepSeek V4.1 Flash66.6%
How much of each OSWorld 2.1 workflow the model completes by operating a real Ubuntu desktop through screenshots, mouse and keyboard: all 108 long tasks across apps, files, mail and browsers, on the September 2026 v2.1 release of the task files. A point is the mean share of each task's weighted checks the model passes (the partial score, as a percentage). The OSWorld team's own runs come first. Where the team has not run a release, the point is another lab's run or the maker's own published figure, marked with who ran it and the benchmark it ran: OSWorld 2.1 in the maker's harness, OSWorld 2.0's offline set or the earlier OSWorld-Verified. Those points are drawn so the release has a figure, and they are not ranked against the team's runs.
OSWorld 2.1, partial score, full 108-task set, v2.1 release. Where it has no run of a model, the maker's own figure or another lab's run stands in, marked with its benchmark.
- DeepSeek V4.1 Flash69.3%Moonshot AI's runBenchmark: OSWorld 2.1, metric and task set not named by Moonshot, v2.1 release, where the axis reads partial score, full 108-task set. From another source, so it is drawn and marked but not ranked with the others.Moonshot AI's run of the October 17 build at max effort in its Kimi Agent harness, from the Kimi K3.1 Pro launch table. Moonshot does not name the metric.
The share of AA-LCR's questions the model answers correctly by reasoning across long sets of real documents, about 100K tokens each.
AA-LCR v1.1, accuracy, Artificial Analysis run
- DeepSeek V4.1 Flash82.7%
The share of MMMU-Pro's college-level questions over charts, diagrams and photos the model answers correctly.
MMMU-Pro, overall accuracy, no tools
- DeepSeek V4.1 Flash80.6%
The share of AA-Omniscience's 6,000 knowledge questions across 42 economically relevant topics the model answers correctly from memory, without search.
AA-Omniscience Accuracy, accuracy, Artificial Analysis run
- DeepSeek V4.1 Flash39.6%Hallucination rate 64.1% in the same run (lower is better)
The share of SpeechMap's 2,120 sensitive and controversial but legal requests the model answers in full: arguments, essays and explanations on contested themes in politics, religion, race, sex, history and similar topics, four wordings per theme across 530 themes. SpeechMap's judge rates each response complete, evasive (hedged or redirected), a denial, or an error (the provider's API blocked the request). Only complete answers count, so the point is the share answered in full: 100 minus the share hedged, refused or blocked by the provider. Higher means more lawful requests answered in full. The share blocked at the provider is printed apart in the table, because an API error is not the model declining. Where SpeechMap runs a release both with reasoning off and with reasoning on (a -reasoning or -thinking entry), the reasoning-on entry is used; a release with one entry uses that entry.
SpeechMap, % Complete over 2,120 contested requests, reasoning-on entry
- DeepSeek V4.1 FlashPublished only for another harness, setting or build, listed in the tableNot drawn: SpeechMap.AI 67.2% on an earlier build. The table below lists it.
The share of MultiChallenge's multi-turn conversations in which the model keeps every instruction it was given earlier in the conversation.
MultiChallenge, accuracy, Scale AI run
- DeepSeek V4.1 Flash74.9%On Oct 28, 2026, Scale AI ranked the October 17 build 5th.Plus or minus 5.3 points (95% interval)

Beside the benchmarks

Cost, speed, capacity, quality and accuracy for DeepSeek V4.1 Flash, with where each figure sits among the 40 models GILD tracks. These measures stand beside the chart and never feed it.

Cost

$0.32per task

Cost per task. One Intelligence Index task at list price, every token counted. Run at max effort.

Speed

179tokens a second

Output speed. How fast it writes once the answer starts. Run at max effort.

First token: 1.0 s
Whole answer: 15 s

Capacity

1Mtokens

Context window. The most one request can hold, as the maker states it.

Max output: 384K
Reads: Text, Images

Quality

39.5of 100

Intelligence Index. One composite score across independent evaluations. Run at max effort.

GDPval knowledge work: 1,600 Elo
Terminal-Bench 4.0: 26.8%
Epoch Capabilities Index: 156

Hallucination

64.1%wrong when it should say “I don’t know”

Hallucination rate. Of the questions it can't get right, how often it answers anyway. Lower is better. Run at max effort.

Knowledge accuracy: 39.6%

Where you can get it

9of 19 routes

Routes offering it. Of the clouds, gateways and coding products GILD tracks, those offering it today.

Open weights: Yes
Licence: MIT

Every route and price

Figures from Artificial Analysis, DeepSeek, Epoch AI and GILD's route matrix, read Oct 26 to 28, 2026. Each measure's definition and source open under . Not yet published for DeepSeek V4.1 Flash: Cached input.

Every value with its benchmark, run and source

DeepSeek V4.1 Flash's result on each axis, with the benchmark, the setting and run, who reported it, the source and the date
Axis	Benchmark and setting	DeepSeek V4.1 Flash	Run	Source	Date
Agentic coding	Terminal-Bench 4.0Pass@1 averaged over 3 runs, mini-swe-agent harness. Vals.ai ranked the October 17 build 19th on its leaderboard of Oct 24, 2026.	31.8%	DeepSeek V4.1 Flash 1017Effort: not published. Harness: mini-swe-agent. Run on the Oct 17, 2026 build.Not drawn: Vals.ai 19.7% on an earlier build, Oct 24, 2026. Source	Vals.ai	Oct 24, 2026
Code	SciCodeSubproblem accuracy, pass@1, highest effort run	55.9%	DeepSeek V4.1 Flash 1017 (Max)Effort: max. Harness: none. Run on the Oct 17, 2026 build.Not drawn: Artificial Analysis 51.9% on an earlier build, Oct 28, 2026. Source	Artificial Analysis	Oct 28, 2026
Math	FrontierMath Tiers 1-3Accuracy, pass@1, highest effort run	71.2%	Effort: max. Harness: none. Run on the Oct 17, 2026 build.Not drawn: Epoch AI 67.4% on an earlier build, Oct 8, 2026. Source	Epoch AI	Oct 19, 2026
Science	GPQA DiamondAccuracy, pass@1, highest effort run	93.2%	Effort: max. Harness: none. Run on the Oct 17, 2026 build.Not drawn: Epoch AI 89.8% on an earlier build, Oct 9, 2026. Source	Epoch AI	Oct 19, 2026
Hard reasoning	Humanity's Last ExamAccuracy, text-only questions, no tools	43.3%	DeepSeek V4.1 Flash 1017 (Max)Effort: max. Harness: none. Run on the Oct 17, 2026 build.Not drawn: Artificial Analysis 39.2% on an earlier build, Oct 28, 2026. Source	Artificial Analysis	Oct 28, 2026
Tool use	AutomationBench-AAScore, Artificial Analysis run	66.6%	DeepSeek V4.1 Flash 1017 (Max)Effort: max. Harness: Artificial Analysis AutomationBench harness. Run on the Oct 17, 2026 build.Not drawn: Artificial Analysis 68.9% on an earlier build, Oct 28, 2026. Source	Artificial Analysis	Oct 28, 2026
Computer use	OSWorld 2.1Metric and task set not named by Moonshot, v2.1 release. Moonshot AI's run of the October 17 build at max effort in its Kimi Agent harness, from the Kimi K3.1 Pro launch table. Moonshot does not name the metric.Stands in for OSWorld 2.1, partial score, full 108-task set, v2.1 release, which has no run of DeepSeek V4.1 Flash yet.	69.3%	DeepSeek-V4.1-Flash-1017 (max)Effort: max. Harness: Kimi Agent harness. Run on the Oct 17, 2026 build.Not drawn: MiniMax 54.3% on an earlier build, Oct 19, 2026. Source	Moonshot AIRun by Moonshot AI	Oct 23, 2026
Long context	AA-LCR v1.1Accuracy, Artificial Analysis run	82.7%	DeepSeek V4.1 Flash 1017 (Max)Effort: max. Harness: none. Run on the Oct 17, 2026 build.Not drawn: Artificial Analysis 84% on an earlier build, Oct 28, 2026. Source	Artificial Analysis	Oct 28, 2026
Multimodal	MMMU-ProOverall accuracy, no tools	80.6%	DeepSeek V4.1 Flash 1017 (Max)Effort: max. Harness: none. Run on the Oct 17, 2026 build.Not drawn: Artificial Analysis 77% on an earlier build, Oct 28, 2026. Source	Artificial Analysis	Oct 28, 2026
Factuality	AA-Omniscience AccuracyAccuracy, Artificial Analysis run	39.6%Hallucination rate 64.1% in the same run (lower is better)	DeepSeek V4.1 Flash 1017 (Max)Effort: max. Harness: none. Run on the Oct 17, 2026 build.Not drawn: Artificial Analysis 46.4% on an earlier build, Oct 27, 2026. Source	Artificial Analysis	Oct 28, 2026
Answers in full	SpeechMap% Complete over 2,120 contested requests, reasoning-on entry	Published only for another harness, setting or build, listed in the table	Not drawn: SpeechMap.AI 67.2% on an earlier build, Oct 27, 2026. Source
Instruction following	MultiChallengeAccuracy, Scale AI run. On Oct 28, 2026, Scale AI ranked the October 17 build 5th.	74.9%Plus or minus 5.3 points (95% interval)	Deepseek V4p1 Flash 1017Effort: not published. Harness: none. Run on the Oct 17, 2026 build.	Scale AI SEAL	Oct 28, 2026

Price and speed

DeepSeek V4.1 Flash lists at $0.30 per million input tokens and $1.20 per million output tokens. Peak-hours rate; off-peak $0.15 / $0.60, as DeepSeek lists it. DeepSeek pricing, Oct 27, 2026.
DeepSeek V4.1 Flash, at max effort, writes 176.7 output tokens per second at the median across the providers serving it, and its first token arrives 0.9 s after the request. Artificial Analysis, Oct 28, 2026.
Fastest on the chart is the shortest time to a 500-token answer: time to first token plus 500 tokens at the median output speed.

Every model with a result, axis by axis

57 models. A maker's own figure and another lab's run are marked with their benchmark; a missing result reads "Not yet published", never zero.

The result of every model GILD has benchmark results for, on each of the 12 axes. A vendor-reported value is marked. A model's run notes open under its row from its name, each with the axes it covers.
Model	Agentic coding	Code	Math	Science	Hard reasoning	Tool use	Computer use	Long context	Multimodal	Factuality	Answers in full	Instructions
DeepSeek V4.1 Flash	31.8%	55.9%	71.2%	93.2%	43.3%	66.6%	69.3%Run by Moonshot AIOSWorld 2.1	82.7%	80.6%	39.6%	Not yet published	74.9%
Agentic coding. Vals.ai ranked the October 17 build 19th on its leaderboard of Oct 24, 2026. Vals.ai Computer use. Moonshot AI's run of the October 17 build at max effort in its Kimi Agent harness, from the Kimi K3.1 Pro launch table. Moonshot does not name the metric. Moonshot AI Instructions. On Oct 28, 2026, Scale AI ranked the October 17 build 5th. Scale AI SEAL
DeepSeek V4 Pro	14.1%	51%	64.6%	91.7%	41%	56.7%	Not yet published	80.3%	Not yet published	45.2%	72%	Not yet published
Agentic coding. Vals.ai ranked it 38th of 58 on its leaderboard of Oct 29, 2026. Vals.ai Factuality. Of the questions it did not know, it answered 94.1% wrongly when it should have declined. Artificial Analysis prints this hallucination rate beside accuracy. Artificial Analysis Answers in full. SpeechMap counted 27.6% of requests denied and 0.3% evaded. SpeechMap.AI
Claude Haiku 5.5	36.4%	55%	75.1%	89.6%	44.4%	35.4%	72.4%Reported by AnthropicOSWorld 2.1	82.7%	Not yet published	34.7%	78.8%	67.7%
Computer use. Anthropic's own run at max effort in its computer-use harness, averaged over five attempts, on the 82 tasks that need no internet. It fully completed 37.1% of them. Anthropic Answers in full. SpeechMap tested it at medium thinking and counted 18.3% of requests denied and 2.5% evaded. SpeechMap.AI Instructions. On Oct 28, 2026, Scale AI ranked it 10th. Scale does not say which reasoning setting it tested. Scale AI SEAL
Claude Sonnet 5.5	64.1%	61%	88.8%	95.6%	55%	71.8%	80.1%Reported by Anthropic	82.7%	Not yet published	51.5%	81.6%	Not yet published
Code, Hard reasoning, Tool use, Long context and Factuality. Artificial Analysis tested Sonnet 5.5 as Anthropic serves it by default, with its safeguard fallback model on. Artificial Analysis Computer use. Anthropic's own run at max effort in its computer-use harness, averaged over five runs. It fully completed 43.5% of the tasks. Anthropic Answers in full. SpeechMap tested it through OpenRouter at medium thinking and counted 13.5% of requests denied and 4.6% evaded. SpeechMap.AI
Claude Opus 5.5	65.2%	66.9%	91.2%	90.6%	61.4%	69.5%	81.8%Reported by Anthropic	84.7%	87.7%	65.5%	82.8%	Not yet published
Agentic coding. Vals.ai ranked it 1st on its leaderboard of Oct 24, 2026. Vals.ai Code, Hard reasoning, Tool use, Long context, Multimodal and Factuality. Artificial Analysis tested Opus 5.5 as Anthropic serves it by default, with its safeguard fallback model on. Artificial Analysis Computer use. Anthropic's own run at max effort in its computer-use harness, averaged over five runs. It fully completed 48.7% of the tasks. Anthropic Answers in full. SpeechMap tested it through OpenRouter at medium thinking and counted 12.7% of requests denied and 2.2% evaded. Another 2.3% failed at the provider and count against the score. SpeechMap.AI
Claude Fable 5.1	58.1%	63.1%	90.2%	93.4%Run by Vals.ai	59.1%	59.4%	80.7%Reported by Anthropic	85.3%	86.3%Run by Moonshot AI	68.4%	72.4%	Not yet published
Code, Hard reasoning, Tool use, Long context and Factuality. Artificial Analysis tested Fable 5.1 as Anthropic serves it by default, with its safeguard fallback model on. Artificial Analysis Science. Vals.ai's run at max effort, ranked 9th on its GPQA Diamond board, last updated September 1, 2026, before Vals archived the board. Vals.ai Computer use. Anthropic's own run on the v2.1 tasks, averaged over five runs. It replaces the 77.9% in the Fable 5.1 system card, which used the August 2026 task set. Anthropic Multimodal. Moonshot AI's run at max effort with Anthropic's fallback on, from the Kimi K3.1 Pro launch table, by the official MMMU-Pro protocol. Artificial Analysis has not run it. Moonshot AI Answers in full. With reasoning on, SpeechMap counted 15.1% of requests denied. Another 11.1% failed at the provider and count against the score, far more than any other Claude release. SpeechMap.AI
Claude Opus 5	53.5%	56.4%	85.6%	93.9%	54.9%	56.6%	77.7%	79.3%	84.7%	60.9%	73.7%	Not yet published
Computer use. The OSWorld team's own run at max effort, with its batch tool and a 500-step budget. It fully completed 44.33% of tasks. OSWorld team Answers in full. With reasoning on, SpeechMap counted 22.3% of requests denied and 3.5% evaded. With reasoning off, the model completes 59.1%. SpeechMap.AI
Claude Sonnet 5	10.6%	54.3%	65.6%	80.3%	41.3%	36.5%	57%Reported by Anthropic	82%	77.3%	40.1%	Not yet published	Not yet published
Computer use. Anthropic reran it for the Sonnet 5.5 launch, averaged over five runs. The share of tasks fully completed is 25.6%. Anthropic
Claude Fable 5	41.4%	61%	87%	85.9%	55.5%	54.1%	Not yet published	82.3%	Not yet published	65.4%	64.8%	63.5%Run by Artificial AnalysisIFBench
Code, Hard reasoning, Tool use, Long context and Factuality. Artificial Analysis tested Fable 5 with its Opus 4.8 fallback switched on, the setup its model page lists. Artificial Analysis Answers in full. With reasoning on, SpeechMap counted 27.9% of requests denied and 5.0% evaded. Another 2.4% failed at the provider and count against the score. SpeechMap.AI Instructions. Artificial Analysis ran Claude Fable 5 at max effort in its own evaluation harness. The run routes some requests to Opus 4.8 as a fallback. Artificial Analysis
Claude Opus 4.8	23.2%	54.4%	80%	91%	48.7%	45.6%	54.8%Run by OSWorld teamOSWorld 2.0	77.7%	Not yet published	48.8%	67.4%	62.2%Run by Artificial AnalysisIFBench
Computer use. The OSWorld team's own run at max effort with its batched tool and a 500-step budget, on the v2026.08.08 task release. It fully completed 20.6% of tasks; the standard tool setting scored 49.33. OSWorld team Answers in full. With thinking on, SpeechMap counted 25.2% of requests denied and 7.0% evaded. With thinking off, the model completes 43.4%. SpeechMap.AI Instructions. Artificial Analysis ran Claude Opus 4.8 at max effort in its own evaluation harness. Artificial Analysis
Claude Opus 4.7	Not yet published	54.5%	70.2%	86.4%	42.3%	Not yet published	48.9%Run by OSWorld teamOSWorld 2.0	78.7%	78.8%	48.9%	70.2%	58.6%Run by Artificial AnalysisIFBench
Computer use. The OSWorld team's own run at max effort with its batched tool and a 500-step budget, on the v2026.08.08 task release. It fully completed 18.2% of tasks; the standard tool setting scored 49.1. OSWorld team Answers in full. With reasoning on, SpeechMap counted 22.0% of requests denied and 5.1% evaded. With reasoning off, the model completes 71.5%. SpeechMap.AI Instructions. Artificial Analysis ran Claude Opus 4.7 at max effort in its own evaluation harness. Artificial Analysis
Claude Sonnet 4.6	3%Run by Artificial AnalysisTerminal-Bench 4.0	50.1%	71.1%Run by Epoch AIOTIS Mock AIME 2024-2025	78.8%	33.6%	20.1%	41.5%Run by OSWorld teamOSWorld 2.0	80%	73.3%	40.9%	35.8%	56.6%Run by Artificial AnalysisIFBench
Agentic coding. Artificial Analysis's run of the October 29 build at high effort in its own agent harness. Vals.ai has run only the October 6 preview build. Artificial Analysis Agentic coding. Unusual. Artificial Analysis's run of the October 29 build at high effort in its own agent harness. Vals.ai has run only the October 6 preview build. Oct 28, 2026 Artificial Analysis Math. Epoch AI's run at max effort, the highest effort it ran. Its 32K thinking budget run scored 85.8, and high 75.6. Epoch AI Computer use. The OSWorld team's own run at max effort with a 500-step budget, on the v2026.08.08 task release. It fully completed 8.3% of tasks; its medium-effort run scored 33.9. OSWorld team Answers in full. With thinking on, SpeechMap counted 45.4% of requests denied and 18.3% evaded. With thinking off, the model completes 29.1%. SpeechMap.AI Instructions. Artificial Analysis ran Claude Sonnet 4.6 at max effort in its own evaluation harness. Artificial Analysis
Claude Opus 4.6	Not yet published	Not yet published	66%	88.4%	39.9%	Not yet published	Not yet published	78%	75.4%	47%	59.9%	37.2%
Answers in full. With thinking on, SpeechMap counted 27.9% of requests denied and 10.4% evaded. With thinking off, the model completes 49.8%. SpeechMap.AI Instructions. On Oct 28, 2026, Scale AI ranked this run 35th. The same model with thinking off scores 56.02 on Scale's board. Scale AI SEAL
Claude Opus 4.5	Not yet published	Not yet published	34.4%	86%	30.1%	Not yet published	Not yet published	77.3%	74%	46.6%	57.5%	59%
Math. Epoch AI's run with a 32K-token thinking budget. Epoch AI Science. Epoch AI's run with a 32K-token thinking budget. With thinking off it scores 80.7%. Epoch AI Answers in full. With extended thinking on, SpeechMap counted 29.1% of requests denied and 13.3% evaded. With it off, the model completes 50.8%. SpeechMap.AI Instructions. On Oct 28, 2026, Scale AI ranked it 18th, with extended thinking on. Scale AI SEAL
Claude Haiku 4.5	Not yet published	42.2%	66.7%Run by Epoch AIOTIS Mock AIME 2024-2025	71.2%	10.4%	3.2%	Not yet published	74.3%	58.6%	18%	24.7%	50.5%
Math. Epoch AI's run with a 32K thinking budget, the highest it ran. Without extended thinking the same model scored 35.8. Epoch AI Science. Epoch AI's run with a 32K-token thinking budget. With thinking off it scores 60.5%. Epoch AI Tool use. Artificial Analysis publishes 3.2 for the reasoning version of Haiku 4.5 on this test, far below the model's other results. Artificial Analysis Tool use. Unusual. Artificial Analysis publishes 3.2 for the reasoning version of Haiku 4.5 on this test, far below the model's other results. Oct 28, 2026 Artificial Analysis Answers in full. With extended thinking on, SpeechMap counted 39.0% of requests denied and 36.4% evaded. With it off, the model completes 20.8%. SpeechMap.AI Instructions. On Oct 28, 2026, Scale AI ranked it 29th, with extended thinking on. Scale AI SEAL
Claude Sonnet 4.5	Not yet published	45.7%	23.9%	82.3%	17.8%	14.1%	Not yet published	72.3%	68.7%	32.9%	31.4%	55.3%
Math. Epoch AI's run with a 32K-token thinking budget. Epoch AI Science. Epoch AI's run with a 59K-token thinking budget. With thinking off it scores 73.7. Epoch AI Answers in full. With reasoning on, SpeechMap counted 48.3% of requests denied and 20.2% evaded. With reasoning off, the model completes 32.3%. SpeechMap.AI Instructions. On Oct 28, 2026, Scale AI ranked it 25th, with extended thinking on. Scale AI SEAL
GPT-6.1 Terra	49.5%	55.1%	90.9%	94.3%	46.2%	60.2%	74.2%Reported by OpenAIOSWorld 2.1	82.7%	84.7%	47.6%	Not yet published	74.2%
Computer use. OpenAI's own run at maximum reasoning effort, from its launch chart, at $1.19 per task. OpenAI does not say whether the score counts partial credit. OpenAI Instructions. On Oct 28, 2026, Scale AI ranked it 6th, tied with Muse Spark Open. Scale AI SEAL
GPT-6.1 Sol	55.1%	54.2%	93.7%	95.4%	52.9%	64.9%	78%Reported by OpenAIOSWorld 2.1	83%	86%	61.8%	69.5%	82%
Computer use. OpenAI's own run at maximum reasoning effort, from the GPT-6.1 Terra launch chart, at $1.15 per task against $8.17 for GPT-6 Astra. OpenAI does not say whether it counts partial credit. OpenAI Answers in full. SpeechMap tested it through OpenRouter at medium reasoning and counted 21.7% of requests denied and 8.8% evaded. SpeechMap.AI Instructions. On Oct 28, 2026, Scale AI ranked it 1st. Scale AI SEAL
GPT-6 Luna	13.6%	54.6%	78.9%	90.5%	38.5%	53.2%	48.9%Run by AnthropicOSWorld 2.1	83.3%	79.7%	39.6%	63.4%	Not yet published
Computer use. Anthropic's run of GPT-6 Luna through OpenAI's API with OpenAI's context compaction, 500 steps, averaged over five attempts, from the Claude Haiku 5.5 system card. Anthropic Answers in full. SpeechMap tested it through OpenRouter at medium reasoning and counted 27.4% of requests denied and 9.1% evaded. SpeechMap.AI
GPT-6 Astra	59.6%	56.5%	93.7%	95.8%	54.7%	68.5%	75.8%Run by Meta	80.7%	86.9%	61.6%	68.2%	Not yet published
Agentic coding. Vals.ai ranked it 4th on its leaderboard of Oct 24, 2026. Vals.ai Computer use. Meta's run of GPT-6 Astra at max effort in its own test framework on all 108 tasks: the average partial score. It fully completed 34.3% of tasks. Meta Answers in full. Reasoning cannot be turned off on this model. SpeechMap counted 21.7% of requests denied and 10.0% evaded. SpeechMap.AI
GPT-5.6 Sol	37.9%	57.1%	89.1%	93.5%	49.5%	60.1%	62.7%Run by OSWorld teamOSWorld 2.0	84%	83.4%	69.7%Run by Epoch AISimpleQA Verified	Not yet published	72.7%Run by Artificial AnalysisIFBench
Computer use. OSWorld team's official run of GPT-5.6 Sol at max reasoning, batch tool, 500 steps. OSWorld team Factuality. Epoch AI's run at max effort. SimpleQA Verified prints no hallucination rate, so none is shown beside it. Epoch AI Instructions. Artificial Analysis ran GPT-5.6 Sol at its highest published effort in its own evaluation harness. Artificial Analysis
GPT-5.6 Terra	22.7%	55%	86%	93.3%	42.9%	59.6%	Not yet published	83%	80.7%	42.3%	Not yet published	71.2%Run by Artificial AnalysisIFBench
Instructions. Artificial Analysis ran GPT-5.6 Terra at its highest published effort in its own evaluation harness. Artificial Analysis
GPT-5.6 Luna	11.6%	53.6%	82.1%	91.6%	39.5%	50.2%	Not yet published	83.7%	78.6%	41%Run by Epoch AISimpleQA Verified	Not yet published	Not yet published
Factuality. Epoch AI's run at max effort. SimpleQA Verified prints no hallucination rate, so none is shown beside it. Epoch AI
GPT-5.5	14.6%Run by Artificial AnalysisTerminal-Bench 4.0	55.8%	85.3%	93.5%Run by Artificial Analysis	45.8%	47.3%	49.5%Run by OSWorld teamOSWorld 2.0	84.3%	79.9%	63%Run by Epoch AISimpleQA Verified	Not yet published	75.9%Run by Artificial AnalysisIFBench
Agentic coding, Science and Instructions. Artificial Analysis ran GPT-5.5 at its highest published effort in its own evaluation harness. Artificial Analysis Computer use. The OSWorld team's own run of GPT-5.5 at xhigh reasoning with batched tool calls and a 500-step budget, on the first OSWorld 2.0 task release. OSWorld team Factuality. Epoch AI's run at xhigh effort, the highest it ran. SimpleQA Verified prints no hallucination rate, so none is shown beside it. Epoch AI
GPT-5.4	Not yet published	Not yet published	78.6%	93.3%	43.7%	Not yet published	Not yet published	82%	78.4%	Not yet published	Not yet published	73.9%Run by Artificial AnalysisIFBench
Instructions. Artificial Analysis ran GPT-5.4 at its highest published effort in its own evaluation harness. Artificial Analysis
gpt-oss-120b	14.2%Run by Terminal-Bench (via Epoch AI)Terminal-Bench 2.0	34%	88.9%Run by Epoch AIOTIS Mock AIME 2024-2025	75.8%	19.6%	0.2%	Not yet published	52%	Not yet published	16.8%Reported by OpenAISimpleQA	36.3%	45.3%
Agentic coding. The Terminal-Bench team's run on its version 2.0 leaderboard with the mini-swe-agent harness, the same agent the axis uses. Terminal-Bench 2.0 is the earlier, easier version of the test. Terminal-Bench (via Epoch AI) Math. Epoch AI's own run at high reasoning, the highest gpt-oss-120b offers. Epoch has not run it on FrontierMath; this competition-math set is easier. Epoch AI Tool use. Artificial Analysis publishes 0.2 for its high-effort run on this test, far below the model's other results. Artificial Analysis Factuality. OpenAI's own run in its model card, without browsing. It answered 78.2% of questions wrongly; OpenAI counts that share of all questions as its hallucination rate. OpenAI Answers in full. SpeechMap counted 63.3% of requests denied and 0.3% evaded. SpeechMap.AI Instructions. On Oct 28, 2026, Scale AI ranked it 31st. Scale AI SEAL
GPT-6 Sol	44.4%	57.6%	89.8%	94.3%	47.9%	61.6%	64.4%Reported by OpenAIOSWorld 2.0	83.7%	82.9%	54.5%	64%	Not yet published
Computer use. OpenAI's own run at maximum reasoning effort, from its launch chart, at $3.25 per task. OpenAI Answers in full. SpeechMap tested it through OpenRouter at medium reasoning and counted 22.7% of requests denied and 13.2% evaded. SpeechMap.AI
GPT-5.3-Codex	Not yet published	Not yet published	Not yet published	91.5%Run by Artificial Analysis	42.5%	Not yet published	Not yet published	83.3%	78.5%	Not yet published	Not yet published	75.4%Run by Artificial AnalysisIFBench
Science. Artificial Analysis ran GPT-5.3-Codex at its highest published effort in its own evaluation harness; Epoch AI has no qualifying run. Artificial Analysis Instructions. Artificial Analysis ran GPT-5.3 Codex at its highest published effort in its own evaluation harness. Artificial Analysis
Gemini 4 Argon	57.6%	61.8%	86%	96.4%	57.1%	77.5%	69.2%Reported by GoogleOSWorld 2.0	79.7%	Not yet published	48.3%	87%	78.7%
Computer use. Google's own run at its highest thinking setting in its Gemini computer-use harness, best of 3 runs. No outside lab has run it. Google Answers in full. SpeechMap tested it through OpenRouter at medium reasoning and counted 10.2% of requests denied and 2.4% evaded. SpeechMap.AI Instructions. On Oct 28, 2026, Scale AI ranked it 2nd. Scale AI SEAL
Gemini 3.8 Flash	19.7%	56.6%	68.4%	95.4%	47.8%	59.9%	59%Reported by GoogleOSWorld 2.0	81.3%	85.6%	50.5%	86.1%	Not yet published
Computer use. Google's own run in its Gemini computer-use harness, best of 3 runs, on the task files before the August patch. Google does not print the thinking level. Google Answers in full. SpeechMap tested it through OpenRouter with reasoning on and counted 11.7% of requests denied and 1.8% evaded. SpeechMap.AI
Gemma 4 31B	Not yet published	45.5%	Not yet published	85.7%Run by Artificial AnalysisGPQA Diamond	23.6%	4.9%	Not yet published	69.7%	73.4%	10.4%Run by Epoch AISimpleQA Verified	82.3%	75.6%Run by Artificial AnalysisIFBench
Science. Artificial Analysis's run of the reasoning version. Epoch AI, the axis's source, ran it only at minimal effort, which is not drawn. Artificial Analysis Tool use. Artificial Analysis publishes 4.9 for the reasoning version of Gemma 4 31B on this test, far below its other results. Artificial Analysis Tool use. Unusual. Artificial Analysis publishes 4.9 for the reasoning version of Gemma 4 31B on this test, far below its other results. Oct 28, 2026 Artificial Analysis Factuality. Epoch AI ran SimpleQA Verified on the instruction-tuned release. SimpleQA Verified prints no hallucination rate, so none is shown beside it. Epoch AI Answers in full. SpeechMap counted 16.8% of requests denied and 0.9% evaded. SpeechMap.AI Instructions. Artificial Analysis's run of the reasoning version on IFBench, a test of precise output constraints. Scale AI has not run it on MultiChallenge. Artificial Analysis
Gemini 3.1 Pro Preview	2.5%	58.7%	59.6%	94.4%	47%	35.4%	Not yet published	82%	82.4%	54.9%	59.9%	71.4%
Agentic coding. Vals.ai tested the February 2026 preview build. Vals.ai Agentic coding. Unusual. Vals.ai tested the February 2026 preview build. Oct 24, 2026 Vals.ai Long context. Artificial Analysis tested the February 19, 2026 preview build. Artificial Analysis Answers in full. With reasoning on, SpeechMap counted 30.9% of requests denied and 8.8% evaded. SpeechMap.AI Instructions. On Oct 28, 2026, Scale AI ranked it 8th. Scale does not say which reasoning setting it tested. Scale AI SEAL
Gemini 3.6 Flash	7.1%Run by Artificial AnalysisTerminal-Bench 4.0	53.4%	58.9%	94.1%	40.8%	53%	83%Reported by GoogleOSWorld-Verified	80%	83.2%	66.2%Run by Epoch AISimpleQA Verified	87.2%	Not yet published
Agentic coding. Artificial Analysis ran Terminal-Bench 4.0 in its own agent harness at high effort. Artificial Analysis Computer use. Google's own figure from the Gemini 3.6 Flash model card, results as of July 2026. It does not state the harness, step budget or thinking level. No independent lab has run it. Google Factuality. Epoch AI's run at high effort, the highest it ran. SimpleQA Verified prints no hallucination rate, so none is shown beside it. Epoch AI Answers in full. SpeechMap tested Gemini 3.6 Flash through its reasoning-on entry and reports complete, evasive, denial, and provider error shares. SpeechMap.AI
Kimi K3.1 Pro	63.6%	60.8%	Not yet published	94.2%Reported by Moonshot AI	52.5%	73.3%	79.6%Reported by Moonshot AIOSWorld 2.1	86.3%	83.9%	48.9%	Not yet published	Not yet published
Science. Moonshot AI's own figure, at its highest reasoning setting with no tools. No independent lab has run it yet. Epoch AI scores the earlier Kimi K3 at 93.1. Moonshot AI Computer use. Moonshot AI's own run at max effort in its Kimi Agent harness. Moonshot does not name the metric or task set; its other rows reprint Anthropic's full-set partial scores. Moonshot AI
Kimi K3	17.2%	59.5%	72.2%	93.1%	46.9%	58.3%	Not yet published	88.7%	80.5%	50.6%Run by Epoch AISimpleQA Verified	65.9%	Not yet published
Factuality. Epoch AI's run at max effort. SimpleQA Verified prints no hallucination rate, so none is shown beside it. Epoch AI Answers in full. SpeechMap tested Kimi K3 through its reasoning-on entry and reports complete, evasive, denial, and provider error shares. SpeechMap.AI
Kimi K2.7 Code	1%Run by Artificial AnalysisTerminal-Bench 4.0	47.8%	54%	87.9%	35%	24.5%	Not yet published	79.3%	Not yet published	36.5%Run by Epoch AISimpleQA Verified	Not yet published	63.1%Run by Artificial AnalysisIFBench
Agentic coding. Artificial Analysis ran Terminal-Bench 4.0 in its own agent harness at reasoning on effort. Artificial Analysis Agentic coding. Unusual. Artificial Analysis ran Terminal-Bench 4.0 in its own agent harness at reasoning on effort. Oct 31, 2026 Artificial Analysis Factuality. Epoch AI's run; Epoch prints no effort for it. SimpleQA Verified prints no hallucination rate, so none is shown beside it. Epoch AI Instructions. Artificial Analysis ran IFBench directly at reasoning on effort. Artificial Analysis
Mistral Large 4	24.7%Run by Artificial AnalysisTerminal-Bench 4.0	Not yet published	Not yet published	Not yet published	Not yet published	57.8%	Not yet published	Not yet published	Not yet published	26%	Not yet published	Not yet published
Agentic coding. Artificial Analysis's run of the October 29 build at high effort in its own agent harness. Vals.ai has run only the October 6 preview build. Artificial Analysis Tool use and Factuality. Artificial Analysis's run of the October 29 build at high effort, its highest run of that build. Artificial Analysis
Mistral Medium 3.5	0%Run by Artificial AnalysisTerminal-Bench 4.0	40.2%	Not yet published	74.8%Run by Artificial AnalysisGPQA Diamond	13.8%	6.3%	Not yet published	69.3%	64.9%	23%	70.5%	68.8%Run by Artificial AnalysisIFBench
Agentic coding. Artificial Analysis's run at high effort in its own agent harness, from its Intelligence Index evaluations. It solved none of the tasks. Vals.ai has not run it. Artificial Analysis Agentic coding. Unusual. Artificial Analysis's run at high effort in its own agent harness, from its Intelligence Index evaluations. It solved none of the tasks. Vals.ai has not run it. Oct 28, 2026 Artificial Analysis Science. Artificial Analysis's run at high effort, the highest it ran for this release, from its Intelligence Index evaluations. Epoch AI has not run it. Artificial Analysis Answers in full. SpeechMap tested it through OpenRouter with reasoning on. With reasoning off it answers 87.9% in full. SpeechMap.AI Instructions. Artificial Analysis's IFBench run at high effort: the share of out-of-domain output constraints the model keeps. Scale AI has not run MultiChallenge on it. Artificial Analysis
Grok 4.7	28.8%	57.4%	53%	92.7%	43.1%	65.6%	Not yet published	76.7%	Not yet published	42.7%	64.1%	Not yet published
Answers in full. SpeechMap tested it through OpenRouter at medium reasoning and counted 34.4% of requests denied and 1.4% evaded. SpeechMap.AI
Grok 4.6	17.2%	56.5%	66%	93.2%	42.9%	66.7%	Not yet published	80.3%	Not yet published	48.9%Run by Epoch AISimpleQA Verified	83.6%	Not yet published
Factuality. Epoch AI's run at xhigh effort, the highest it ran; its high run scored 49.3. SimpleQA Verified prints no hallucination rate. Epoch AI Answers in full. SpeechMap tested Grok 4.6 through its reasoning-on entry and reports complete, evasive, denial, and provider error shares. SpeechMap.AI
Grok 4.5	8.6%	55%	57.2%	93.4%	42.7%	57.9%	Not yet published	79.3%	80.4%	48.3%Run by Epoch AISimpleQA Verified	93.1%	Not yet published
Factuality. Epoch AI's run at high effort, the highest it ran. SimpleQA Verified prints no hallucination rate, so none is shown beside it. Epoch AI Answers in full. SpeechMap tested Grok 4.5 through its reasoning-on entry and reports complete, evasive, denial, and provider error shares. SpeechMap.AI
MiniMax M3.1 Flash	25.3%	53.4%	Not yet published	Not yet published	48%	72.5%	61.7%Reported by MiniMaxOSWorld 2.0	79.7%	79.1%	45.1%	Not yet published	Not yet published
Computer use. MiniMax's own figure on the August 2026 full task release, from its model card. MiniMax names neither the metric nor the harness nor the reasoning setting. MiniMax
Mercury 3	3%	Not yet published	Not yet published	Not yet published	Not yet published	Not yet published	Not yet published	Not yet published	Not yet published	Not yet published	Not yet published	Not yet published
GLM-5.3-Flash	37.4%	51.6%	55.8%	90.2%	39.9%	60.4%	59.1%Reported by Z.aiOSWorld 2.0	80%	86%Run by Vals.aiMMMU-Pro	28.2%	65.4%	Not yet published
Computer use. Z.ai's own figure from its launch post. It does not name the OSWorld 2.0 task release, task set, harness or metric. No independent lab has run it. Z.ai Multimodal. Vals.ai's run, rank 23 on its MMMU Pro board, last updated September 1, 2026. Vals's variant offers 4 answer options, so it reads higher than the 10-option set. Vals.ai Answers in full. SpeechMap counted 28.9% of requests denied and 3.3% evaded. 2.4% failed at the provider. SpeechMap.AI
GLM-5.3	38.9%	59%	68.8%	90.9%	42.3%	62.2%	Not yet published	79.7%	Not yet published	32.3%	71.5%	Not yet published
Answers in full. SpeechMap counted 25.1% of requests denied and 3.3% evaded. SpeechMap.AI
GLM-5.2	1%Run by Artificial AnalysisTerminal-Bench 4.0	51.2%	59.2%	Not yet published	41.1%	28.4%	Not yet published	78.3%	Not yet published	34.2%Run by Epoch AISimpleQA Verified	Not yet published	73.3%Run by Artificial AnalysisIFBench
Agentic coding. Artificial Analysis ran Terminal-Bench 4.0 in its own agent harness at max effort. Artificial Analysis Agentic coding. Unusual. Artificial Analysis ran Terminal-Bench 4.0 in its own agent harness at max effort. Oct 31, 2026 Artificial Analysis Factuality. Epoch AI's run at max effort. SimpleQA Verified prints no hallucination rate, so none is shown beside it. Epoch AI Instructions. Artificial Analysis ran IFBench directly at max effort. Artificial Analysis
Muse Spark Open	6.6%	58.4%	69.8%	88.6%	43.9%	49.7%	80.4%Reported by MetaOSWorld-Verified	78.3%	80.9%	42%	Not yet published	74.2%
Agentic coding. Vals.ai ranked it 47th of 58 on its leaderboard of Oct 29, 2026. Artificial Analysis's own-harness run agrees, at 8.1. Vals.ai Agentic coding. Unusual. Vals.ai ranked it 47th of 58 on its leaderboard of Oct 29, 2026. Artificial Analysis's own-harness run agrees, at 8.1. Oct 29, 2026 Vals.ai Computer use. Meta's own figure from the Muse Spark Open model card, at xhigh effort. Meta does not state the harness or step budget. Meta Instructions. On Oct 29, 2026, Scale AI ranked it 6th, level with GPT-6.1 Terra. Scale AI SEAL
Muse Wonder	49.5%	60.3%	82.1%	Not yet published	50.1%	68.1%	72.6%Reported by Meta	80.7%	Not yet published	38%	42.7%	Not yet published
Agentic coding. Vals.ai ranked it 12th on its leaderboard of Oct 29, 2026, level with GPT-6.1 Terra at 49.49%. Vals.ai Computer use. Meta's own run at max effort in its own test framework on all 108 tasks: the average partial score. It fully completed 34.3% of tasks. Meta Answers in full. SpeechMap tested it through OpenRouter with reasoning on. The provider's API blocked 54.8% of requests; the model denied 1.4% and evaded 1.1%. SpeechMap.AI
Muse Spark 1.3	10.6%	58.8%	74%	93.5%Run by Artificial AnalysisGPQA Diamond	48.7%	57.9%	69.6%	83%	82%	38%	25.8%	Not yet published
Science. Artificial Analysis's run at max effort, the highest it ran for this release, from its Intelligence Index evaluations; its xhigh run scored 94.1. Epoch AI has not run it. Artificial Analysis Computer use. The OSWorld team's own run at max effort, with its batch tool and a 500-step budget. It fully completed 39.25% of tasks. OSWorld team Multimodal. Artificial Analysis ran MMMU-Pro on this release at xhigh effort only; its Max record carries no MMMU-Pro figure. Artificial Analysis Answers in full. SpeechMap tested Muse Spark 1.3 through its reasoning-on entry and reports complete, evasive, denial, and provider error shares. SpeechMap.AI Answers in full. Unusual. SpeechMap tested Muse Spark 1.3 through its reasoning-on entry and reports complete, evasive, denial, and provider error shares. Oct 31, 2026 SpeechMap.AI
Llama 4 Scout	Not yet published	21.3%	7.8%Run by Epoch AIOTIS Mock AIME 2024-2025	51.8%	3.8%	0.2%	Not yet published	27.7%	52.9%	Not yet published	70.1%	39.5%Run by Artificial AnalysisIFBench
Math. Epoch AI's run of the instruction-tuned release. Llama 4 has no reasoning mode. Epoch AI Hard reasoning. Artificial Analysis publishes 3.8 for Llama 4 Scout, which has no reasoning mode, on this test. Artificial Analysis Tool use. Artificial Analysis publishes 0.2 for Llama 4 Scout on this test, far below its other results. Artificial Analysis Instructions. Artificial Analysis ran Llama 4 Scout, which has no reasoning mode, in its own evaluation harness. Artificial Analysis
Llama 4 Maverick	Not yet published	31.7%	20.6%Run by Epoch AIOTIS Mock AIME 2024-2025	67%	4.9%	0.2%	Not yet published	50%	62.1%	Not yet published	58.3%	43%Run by Artificial AnalysisIFBench
Math. Epoch AI's run of the FP8 instruction-tuned release. Llama 4 has no reasoning mode. Epoch AI Science. Epoch AI ran Meta's FP8 release of the weights. Epoch AI Hard reasoning. Artificial Analysis publishes 4.9 for Llama 4 Maverick, which has no reasoning mode, on this test. Artificial Analysis Hard reasoning. Unusual. Artificial Analysis publishes 4.9 for Llama 4 Maverick, which has no reasoning mode, on this test. Oct 28, 2026 Artificial Analysis Tool use. Artificial Analysis publishes 0.2 for Llama 4 Maverick on this test, far below its other results. Artificial Analysis Tool use. Unusual. Artificial Analysis publishes 0.2 for Llama 4 Maverick on this test, far below its other results. Oct 28, 2026 Artificial Analysis Instructions. Artificial Analysis ran Llama 4 Maverick, which has no reasoning mode, in its own evaluation harness. Artificial Analysis
Qwen3.8 27B	5.6%Run by Artificial AnalysisTerminal-Bench 4.0	46.6%	Not yet published	90.5%Run by Artificial AnalysisGPQA Diamond	33.9%	48.2%	84.3%Reported by AlibabaOSWorld-Verified	82%	76.3%	17.3%	Not yet published	79.5%Reported by AlibabaIFBench
Agentic coding. Artificial Analysis's run at xhigh effort in its own agent harness, from its Intelligence Index evaluations. Vals.ai has not run it. Artificial Analysis Agentic coding. Unusual. Artificial Analysis's run at xhigh effort in its own agent harness, from its Intelligence Index evaluations. Vals.ai has not run it. Nov 2, 2026 Artificial Analysis Science. Artificial Analysis's run at xhigh effort, the highest it ran for this release, from its Intelligence Index evaluations. Epoch AI has not run it. Artificial Analysis Computer use. Alibaba's own figure from the Qwen3.8 27B model card. It does not state the harness, step budget or reasoning effort. Alibaba Instructions. Alibaba's own figure from the Qwen3.8 27B model card. It does not state the reasoning effort. Scale AI has not run MultiChallenge on it. Alibaba
Qwen3.8 Max	38.9%	52.1%	65.6%	92.3%	43.1%	56.2%	Not yet published	80.3%	82.8%	29.5%	65.3%	Not yet published
Agentic coding. Vals.ai ranked the September 2 build 16th on its leaderboard of Oct 29, 2026. Vals.ai Answers in full. SpeechMap tested the September 2 build with reasoning on and counted 29.8% of requests denied and 2.1% evaded. SpeechMap.AI
MiMo-V2.6-Pro	31.3%	60.9%	Not yet published	Not yet published	49.4%	58.6%	82%Reported by XiaomiOSWorld-Verified	86.3%	Not yet published	34.8%	66.5%	Not yet published
Computer use. Xiaomi's own figure from the MiMo-V2.6-Pro model card. It does not state the harness, step budget or reasoning effort. No independent lab has run it. Xiaomi Answers in full. SpeechMap counted 29.7% of requests denied and 3.8% evaded. SpeechMap.AI
MiMo-V2.6-Flash	24.2%	51.3%	Not yet published	Not yet published	35.1%	64.1%	80.8%Reported by XiaomiOSWorld-Verified	74.3%	73.1%	27.2%	66.9%	Not yet published
Computer use. Xiaomi's own figure from the MiMo-V2.6-Flash model card. It does not state the harness, step budget or reasoning effort. No independent lab has run it. Xiaomi Answers in full. SpeechMap counted 30.1% of requests denied and 3.0% evaded. SpeechMap.AI
Ling 3.1 Flash	32.8%Run by Artificial AnalysisTerminal-Bench 4.0	56.8%	Not yet published	Not yet published	43.5%	67.5%	Not yet published	82.3%	Not yet published	26.5%	Not yet published	Not yet published
Agentic coding. Artificial Analysis's run with reasoning on in its own agent harness, from its Intelligence Index evaluations. Vals.ai has not run it. Artificial Analysis
Step 5 Preview	31.8%	58.9%	Not yet published	93.5%Reported by StepFunGPQA Diamond	46.5%	51%	Not yet published	88.3%	76.4%	36.4%	59.7%	Not yet published
Agentic coding. Vals.ai ranked it 22nd of 58 on its leaderboard of Oct 29, 2026. Vals.ai Science. StepFun's own figure from its launch table, at High reasoning, its highest setting. Epoch AI has not run it. StepFun Answers in full. SpeechMap ran it at medium reasoning through OpenRouter: 34.5% denied, 1.0% evaded, and 4.8% stopped by the provider's content filter. SpeechMap.AI

Axis

Agentic codingTerminal-Bench 4.0

Claude Opus 5.565.2%
Claude Sonnet 5.564.1%
Kimi K3.1 Pro63.6%
GPT-6 Astra59.6%
Claude Fable 5.158.1%
Gemini 4 Argon57.6%
GPT-6.1 Sol55.1%
Claude Opus 553.5%
GPT-6.1 Terra49.5%
Muse Wonder49.5%
GPT-6 Sol44.4%
Claude Fable 541.4%
GLM-5.338.9%
Qwen3.8 Max38.9%
GPT-5.6 Sol37.9%
GLM-5.3-Flash37.4%
Claude Haiku 5.536.4%
DeepSeek V4.1 Flash31.8%
Step 5 Preview31.8%
MiMo-V2.6-Pro31.3%
Grok 4.728.8%
MiniMax M3.1 Flash25.3%
MiMo-V2.6-Flash24.2%
Claude Opus 4.823.2%
GPT-5.6 Terra22.7%
Gemini 3.8 Flash19.7%
Kimi K317.2%
Grok 4.617.2%
DeepSeek V4 Pro14.1%
GPT-6 Luna13.6%
GPT-5.6 Luna11.6%
Claude Sonnet 510.6%
Muse Spark 1.310.6%
Grok 4.58.6%
Muse Spark Open6.6%
Mercury 33%
Gemini 3.1 Pro Preview2.5%
Ling 3.1 Flash32.8%Artificial Analysis's run, Terminal-Bench 4.0
Mistral Large 424.7%Artificial Analysis's run, Terminal-Bench 4.0
GPT-5.514.6%Artificial Analysis's run, Terminal-Bench 4.0
gpt-oss-120b14.2%Terminal-Bench's run, Terminal-Bench 2.0
Gemini 3.6 Flash7.1%Artificial Analysis's run, Terminal-Bench 4.0
Qwen3.8 27B5.6%Artificial Analysis's run, Terminal-Bench 4.0
Claude Sonnet 4.63%Artificial Analysis's run, Terminal-Bench 4.0
Kimi K2.7 Code1%Artificial Analysis's run, Terminal-Bench 4.0
GLM-5.21%Artificial Analysis's run, Terminal-Bench 4.0
Mistral Medium 3.50%Artificial Analysis's run, Terminal-Bench 4.0

The other models in the grid have no result on Terminal-Bench 4.0 yet.

CodeSciCode

The other models in the grid have no result on SciCode yet.

MathFrontierMath Tiers 1-3

GPT-6.1 Sol93.7%
GPT-6 Astra93.7%
Claude Opus 5.591.2%
GPT-6.1 Terra90.9%
Claude Fable 5.190.2%
GPT-6 Sol89.8%
GPT-5.6 Sol89.1%
Claude Sonnet 5.588.8%
Claude Fable 587%
GPT-5.6 Terra86%
Gemini 4 Argon86%
Claude Opus 585.6%
GPT-5.585.3%
GPT-5.6 Luna82.1%
Muse Wonder82.1%
Claude Opus 4.880%
GPT-6 Luna78.9%
GPT-5.478.6%
Claude Haiku 5.575.1%
Muse Spark 1.374%
Kimi K372.2%
DeepSeek V4.1 Flash71.2%
Claude Opus 4.770.2%
Muse Spark Open69.8%
GLM-5.368.8%
Gemini 3.8 Flash68.4%
Claude Opus 4.666%
Grok 4.666%
Claude Sonnet 565.6%
Qwen3.8 Max65.6%
DeepSeek V4 Pro64.6%
Gemini 3.1 Pro Preview59.6%
GLM-5.259.2%
Gemini 3.6 Flash58.9%
Grok 4.557.2%
GLM-5.3-Flash55.8%
Kimi K2.7 Code54%
Grok 4.753%
Claude Opus 4.534.4%
Claude Sonnet 4.523.9%
gpt-oss-120b88.9%Epoch AI's run, OTIS Mock AIME 2024-2025
Claude Sonnet 4.671.1%Epoch AI's run, OTIS Mock AIME 2024-2025
Claude Haiku 4.566.7%Epoch AI's run, OTIS Mock AIME 2024-2025
Llama 4 Maverick20.6%Epoch AI's run, OTIS Mock AIME 2024-2025
Llama 4 Scout7.8%Epoch AI's run, OTIS Mock AIME 2024-2025

The other models in the grid have no result on FrontierMath Tiers 1-3 yet.

ScienceGPQA Diamond

Gemini 4 Argon96.4%
GPT-6 Astra95.8%
Claude Sonnet 5.595.6%
GPT-6.1 Sol95.4%
Gemini 3.8 Flash95.4%
Gemini 3.1 Pro Preview94.4%
GPT-6.1 Terra94.3%
GPT-6 Sol94.3%
Gemini 3.6 Flash94.1%
Claude Opus 593.9%
GPT-5.6 Sol93.5%
Grok 4.593.4%
GPT-5.6 Terra93.3%
GPT-5.493.3%
DeepSeek V4.1 Flash93.2%
Grok 4.693.2%
Kimi K393.1%
Grok 4.792.7%
Qwen3.8 Max92.3%
DeepSeek V4 Pro91.7%
GPT-5.6 Luna91.6%
Claude Opus 4.891%
GLM-5.390.9%
Claude Opus 5.590.6%
GPT-6 Luna90.5%
GLM-5.3-Flash90.2%
Claude Haiku 5.589.6%
Muse Spark Open88.6%
Claude Opus 4.688.4%
Kimi K2.7 Code87.9%
Claude Opus 4.786.4%
Claude Opus 4.586%
Claude Fable 585.9%
Claude Sonnet 4.582.3%
Claude Sonnet 580.3%
Claude Sonnet 4.678.8%
gpt-oss-120b75.8%
Claude Haiku 4.571.2%
Llama 4 Maverick67%
Llama 4 Scout51.8%
Kimi K3.1 Pro94.2%Moonshot AI's own figure
GPT-5.593.5%Artificial Analysis's run
Muse Spark 1.393.5%Artificial Analysis's run, GPQA Diamond
Step 5 Preview93.5%StepFun's own figure, GPQA Diamond
Claude Fable 5.193.4%Vals.ai's run
GPT-5.3-Codex91.5%Artificial Analysis's run
Qwen3.8 27B90.5%Artificial Analysis's run, GPQA Diamond
Gemma 4 31B85.7%Artificial Analysis's run, GPQA Diamond
Mistral Medium 3.574.8%Artificial Analysis's run, GPQA Diamond

The other models in the grid have no result on GPQA Diamond yet.

Hard reasoningHumanity's Last Exam

The other models in the grid have no result on Humanity's Last Exam yet.

Tool useAutomationBench-AA

The other models in the grid have no result on AutomationBench-AA yet.

Computer useOSWorld 2.1

Claude Opus 577.7%
Muse Spark 1.369.6%
Qwen3.8 27B84.3%Alibaba's own figure, OSWorld-Verified
Gemini 3.6 Flash83%Google's own figure, OSWorld-Verified
MiMo-V2.6-Pro82%Xiaomi's own figure, OSWorld-Verified
Claude Opus 5.581.8%Anthropic's own figure
MiMo-V2.6-Flash80.8%Xiaomi's own figure, OSWorld-Verified
Claude Fable 5.180.7%Anthropic's own figure
Muse Spark Open80.4%Meta's own figure, OSWorld-Verified
Claude Sonnet 5.580.1%Anthropic's own figure
Kimi K3.1 Pro79.6%Moonshot AI's own figure, OSWorld 2.1
GPT-6.1 Sol78%OpenAI's own figure, OSWorld 2.1
GPT-6 Astra75.8%Meta's run
GPT-6.1 Terra74.2%OpenAI's own figure, OSWorld 2.1
Muse Wonder72.6%Meta's own figure
Claude Haiku 5.572.4%Anthropic's own figure, OSWorld 2.1
DeepSeek V4.1 Flash69.3%Moonshot AI's run, OSWorld 2.1
Gemini 4 Argon69.2%Google's own figure, OSWorld 2.0
GPT-6 Sol64.4%OpenAI's own figure, OSWorld 2.0
GPT-5.6 Sol62.7%OSWorld team's run, OSWorld 2.0
MiniMax M3.1 Flash61.7%MiniMax's own figure, OSWorld 2.0
GLM-5.3-Flash59.1%Z.ai's own figure, OSWorld 2.0
Gemini 3.8 Flash59%Google's own figure, OSWorld 2.0
Claude Sonnet 557%Anthropic's own figure
Claude Opus 4.854.8%OSWorld team's run, OSWorld 2.0
GPT-5.549.5%OSWorld team's run, OSWorld 2.0
Claude Opus 4.748.9%OSWorld team's run, OSWorld 2.0
GPT-6 Luna48.9%Anthropic's run, OSWorld 2.1
Claude Sonnet 4.641.5%OSWorld team's run, OSWorld 2.0

The other models in the grid have no result on OSWorld 2.1 yet.

Long contextAA-LCR v1.1

The other models in the grid have no result on AA-LCR v1.1 yet.

MultimodalMMMU-Pro

The other models in the grid have no result on MMMU-Pro yet.

FactualityAA-Omniscience Accuracy

Claude Fable 5.168.4%
Claude Opus 5.565.5%
Claude Fable 565.4%
GPT-6.1 Sol61.8%
GPT-6 Astra61.6%
Claude Opus 560.9%
Gemini 3.1 Pro Preview54.9%
GPT-6 Sol54.5%
Claude Sonnet 5.551.5%
Gemini 3.8 Flash50.5%
Claude Opus 4.748.9%
Kimi K3.1 Pro48.9%
Claude Opus 4.848.8%
Gemini 4 Argon48.3%
GPT-6.1 Terra47.6%
Claude Opus 4.647%
Claude Opus 4.546.6%
DeepSeek V4 Pro45.2%
MiniMax M3.1 Flash45.1%
Grok 4.742.7%
GPT-5.6 Terra42.3%
Muse Spark Open42%
Claude Sonnet 4.640.9%
Claude Sonnet 540.1%
DeepSeek V4.1 Flash39.6%
GPT-6 Luna39.6%
Muse Wonder38%
Muse Spark 1.338%
Step 5 Preview36.4%
MiMo-V2.6-Pro34.8%
Claude Haiku 5.534.7%
Claude Sonnet 4.532.9%
GLM-5.332.3%
Qwen3.8 Max29.5%
GLM-5.3-Flash28.2%
MiMo-V2.6-Flash27.2%
Ling 3.1 Flash26.5%
Mistral Large 426%
Mistral Medium 3.523%
Claude Haiku 4.518%
Qwen3.8 27B17.3%
GPT-5.6 Sol69.7%Epoch AI's run, SimpleQA Verified
Gemini 3.6 Flash66.2%Epoch AI's run, SimpleQA Verified
GPT-5.563%Epoch AI's run, SimpleQA Verified
Kimi K350.6%Epoch AI's run, SimpleQA Verified
Grok 4.648.9%Epoch AI's run, SimpleQA Verified
Grok 4.548.3%Epoch AI's run, SimpleQA Verified
GPT-5.6 Luna41%Epoch AI's run, SimpleQA Verified
Kimi K2.7 Code36.5%Epoch AI's run, SimpleQA Verified
GLM-5.234.2%Epoch AI's run, SimpleQA Verified
gpt-oss-120b16.8%OpenAI's own figure, SimpleQA
Gemma 4 31B10.4%Epoch AI's run, SimpleQA Verified

The other models in the grid have no result on AA-Omniscience Accuracy yet.

Instruction followingMultiChallenge

GPT-6.1 Sol82%
Gemini 4 Argon78.7%
DeepSeek V4.1 Flash74.9%
GPT-6.1 Terra74.2%
Muse Spark Open74.2%
Gemini 3.1 Pro Preview71.4%
Claude Haiku 5.567.7%
Claude Opus 4.559%
Claude Sonnet 4.555.3%
Claude Haiku 4.550.5%
gpt-oss-120b45.3%
Claude Opus 4.637.2%
Qwen3.8 27B79.5%Alibaba's own figure, IFBench
GPT-5.575.9%Artificial Analysis's run, IFBench
Gemma 4 31B75.6%Artificial Analysis's run, IFBench
GPT-5.3-Codex75.4%Artificial Analysis's run, IFBench
GPT-5.473.9%Artificial Analysis's run, IFBench
GLM-5.273.3%Artificial Analysis's run, IFBench
GPT-5.6 Sol72.7%Artificial Analysis's run, IFBench
GPT-5.6 Terra71.2%Artificial Analysis's run, IFBench
Mistral Medium 3.568.8%Artificial Analysis's run, IFBench
Claude Fable 563.5%Artificial Analysis's run, IFBench
Kimi K2.7 Code63.1%Artificial Analysis's run, IFBench
Claude Opus 4.862.2%Artificial Analysis's run, IFBench
Claude Opus 4.758.6%Artificial Analysis's run, IFBench
Claude Sonnet 4.656.6%Artificial Analysis's run, IFBench
Llama 4 Maverick43%Artificial Analysis's run, IFBench
Llama 4 Scout39.5%Artificial Analysis's run, IFBench

The other models in the grid have no result on MultiChallenge yet.

Every measure on record

Cost, speed, reliability, knowledge-work and domain results for DeepSeek V4.1 Flash beyond the twelve chart axes, each with the run it comes from and its publisher. These stay separate measures; none is folded into the chart.

Every measure on record for DeepSeek V4.1 Flash: the value, the run it comes from, the publisher and the day it was read
Measure	DeepSeek V4.1 Flash	Run	Source	Read
Specs and access
Context windowThe most tokens one request can hold, as the maker states it. Size alone says nothing about how well the model uses a long input; the Long context axis measures that.	1M		DeepSeek	Oct 28, 2026
Max outputThe most tokens the model writes in one response.	384K		DeepSeek	Oct 28, 2026
ReadsThe kinds of input the model accepts.	Text, Images		Artificial Analysis	Oct 27, 2026
LicenceThe terms the weights or the service are offered under.	MIT		Artificial Analysis	Oct 27, 2026
Open weightsWhether the maker publishes the weights for anyone to run.	Yes		Artificial Analysis	Oct 27, 2026
Routes offering itOf the 19 clouds, gateways and coding products GILD tracks, how many offer the release today, preview included.	9 of 19		GILD route matrix	Oct 28, 2026
Cost
Answer tokens per taskLower is better. Answer tokens per Intelligence Index task, reasoning excluded.	25,904	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Cost per task, cache readsLower is better. The cache-read share of AA's cost per Intelligence Index task.	$0.057	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Cost per task, cache writesLower is better. The cache-write share of AA's cost per Intelligence Index task.	$0.15	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Cost per task, inputLower is better. The input-token share of AA's cost per Intelligence Index task.	$0.21	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Cost per task, outputLower is better. The answer-token share of AA's cost per Intelligence Index task.	$0.11	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Cost per task, reasoningLower is better. The reasoning-token share of AA's cost per Intelligence Index task.	$0.075	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Cost per taskLower is better. What one Intelligence Index task cost AA at list price, input, cache, output and reasoning tokens together.	$0.32	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Cost to run the indexLower is better. What the whole Intelligence Index run cost AA at list price. The runs cost from tens of dollars to about $13,000, so the axis is log from $10 to $100,000.	$524.72	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Output tokens per taskLower is better. Tokens the model wrote per Intelligence Index task, reasoning included: how verbose it is.	88,574	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Reasoning tokens per taskLower is better. Reasoning tokens per Intelligence Index task.	62,670	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Speed
End-to-end time, 100K promptLower is better. Median seconds for a whole response with a 100,000-token prompt.	17 s	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
End-to-end response timeLower is better. Seconds AA measures for a whole response on its standard prompt.	15 s	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Time to first answer token, 100K promptLower is better. Median seconds to the first answer token with a 100,000-token prompt.	14 s	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Time to first answer tokenLower is better. Seconds until the first answer token, after any reasoning, on AA's API runs.	12 s	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Output speed, 100K promptMedian output tokens per second when the prompt is about 100,000 tokens long.	186 tokens/s	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Output speedMedian output tokens per second on AA's API runs, with the 5th and 95th percentile when AA prints them.	179 tokens/s	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Time per taskLower is better. Wall-clock seconds per Intelligence Index task. Runs take from about 15 s to about 33 min, so the axis is log from 1 s to 10,000 s.	370 s	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Time to first token, 100K promptLower is better. Median seconds to the first chunk when the prompt is about 100,000 tokens long: long-input latency, separate from window size.	3.6 s	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Time to first tokenLower is better. Median seconds to the first streamed chunk on AA's API runs, with the 5th and 95th percentile when AA prints them. Reasoning models spend this time thinking.	1.0 s	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Reliability
Hallucination rateLower is better. AA-Omniscience hallucination rate shows how often the model answers wrongly when it does not know and should decline.	64.1%	Max effort	Artificial Analysis	Oct 28, 2026
Omniscience accuracyShare of AA-Omniscience knowledge questions answered correctly.	39.6%	Max effort	Artificial Analysis	Oct 28, 2026
Omniscience indexAA-Omniscience index: correct answers minus wrong answers, declines neutral, -100 to 100.	-5.3	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
SimpleQA Verified (Epoch)Share of SimpleQA Verified factual questions answered correctly, best scorer, Epoch AI's run. A second factuality source beside AA-Omniscience.	42.5%	Max effort	Epoch AI	Oct 19, 2026
Knowledge work
AA-BriefcaseAA-Briefcase overall Elo for long business deliverables, with AA's 95% interval.	1,419	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
AnalystAgent (AA run)AA's AnalystAgent score for analyst workflows.	35%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
APEX-Agents (maintainers)APEX-Agents pass@1 from the maintainers, as Epoch AI republishes it.	39.5%		Mercor (via Epoch AI)	Oct 26, 2026
GDP-PDF (AA run)Share of GDP-PDF document tasks where every check passes.	12.8%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
GDPval-AAGDPval-AA Elo: AA's run of economically valuable knowledge-work tasks, graded pairwise, with AA's interval.	1,600	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
MLCR (AA run)AA's MLCR overall score for long, multi-document reading.	23.9%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Capability beyond the chart
AA-LCRAA long-context reasoning accuracy.	84%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
ALE-BenchALE-Bench performance rating on long optimization contests, as Epoch AI republishes it.	1,092	Max effort	Sakana AI (via Epoch AI)	Oct 26, 2026
ARC-AGI-2ARC-AGI-2 score from the ARC Prize maintainers, as Epoch AI republishes it.	72.9%	Max effort	ARC Prize Foundation (via Epoch AI)	Oct 26, 2026
Chess puzzles (Epoch)Share of chess puzzles solved, Epoch AI's run.	46%	Max effort	Epoch AI	Oct 19, 2026
CritPt (AA run)Share of CritPt research-level physics problems solved in AA's run.	14.3%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
DTBenchDTBench accuracy, as Epoch AI republishes it.	89.9%		Redwood Research (via Epoch AI)	Oct 26, 2026
Epoch Capabilities IndexEpoch Capabilities Index: Epoch AI's composite across its benchmark hub, with its confidence interval.	156	DeepSeek V4.1 Flash	Epoch AI	Oct 26, 2026
FrontierCode (AA run)AA's FrontierCode score: hard, unsaturated coding tasks.	44.0%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
FrontierMath Tier 4 (Epoch)Share of FrontierMath Tier 4 research math problems solved, Epoch AI's run.	46.3%	Max effort	Epoch AI	Oct 19, 2026
FrontierMath Tiers 1 to 3 (Epoch)Share of FrontierMath Tiers 1 to 3 problems solved, Epoch AI's run.	71.2%	Max effort	Epoch AI	Oct 19, 2026
GPQA Diamond (Epoch)GPQA Diamond accuracy, best scorer, Epoch AI's run.	93.2%	Max effort	Epoch AI	Oct 19, 2026
Humanity's Last Exam (AA run)Share of Humanity's Last Exam questions answered correctly in AA's run.	39.2%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Intelligence IndexArtificial Analysis Intelligence Index: AA's composite of its own evaluation runs, 0 to 100 points.	39.5	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
IT-Bench SRE (AA run)AA's IT-Bench SRE score for incident and reliability agents.	46.9%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
LMCALMCA score, 0 to 100, as Epoch AI republishes it. Its interval is printed only as a half-width, so it is not drawn.	47		Redwood Research (via Epoch AI)	Oct 26, 2026
MMMU-Pro (AA run)Multimodal understanding accuracy on MMMU-Pro in AA's run.	77.0%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Mystery game puzzles (Epoch)Share of mystery game puzzles solved, Epoch AI's run.	49%	Max effort	Epoch AI	Oct 19, 2026
OTIS Mock AIME (Epoch)Share of OTIS Mock AIME 2024 to 2025 problems solved, Epoch AI's run.	99.4%	Max effort	Epoch AI	Oct 19, 2026
ProofBenchShare of ProofBench proofs graded correct, from Vals AI as Epoch AI republishes it.	54%		Vals AI (via Epoch AI)	Oct 26, 2026
SciCode (AA run)SciCode subproblem accuracy in AA's run.	51.9%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Terminal-Bench 4.0 (AA run)Share of Terminal-Bench 4.0 tasks solved in AA's own harness.	26.8%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Terminal-Bench Science (AA run)Share of Terminal-Bench Science tasks solved in AA's harness.	9.0%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
WebDev ArenaWebDev Arena score from human pairwise votes on web builds, with its 95% interval, as Epoch AI republishes it.	1,614	Max effort	LMArena (via Epoch AI)	Oct 26, 2026
WeirdML v3, best runWeirdML v3 maximum performance across runs, as Epoch AI republishes it.	11.6%	High effort	Håvard Tveit Ihle (via Epoch AI)	Oct 26, 2026
Domains
EconomicsArtificial Analysis capability score for economics questions, 0 to 100 points.	44.4	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
EngineeringArtificial Analysis capability score for engineering questions, 0 to 100 points.	39.3	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Finance and accountingArtificial Analysis capability score for finance and accounting questions, 0 to 100 points.	45	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Healthcare and medicalArtificial Analysis capability score for healthcare and medical questions, 0 to 100 points.	40.8	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
LegalArtificial Analysis capability score for legal questions, 0 to 100 points.	41.3	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Strategy and operationsArtificial Analysis capability score for strategy and operations questions, 0 to 100 points.	51.7	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026
Harvey Legal Lab (AA run)AA's run of Harvey's legal task lab.	1.7%	DeepSeek V4.1 Flash (Max)	Artificial Analysis	Oct 27, 2026

Routes, prices and uptime

Where DeepSeek V4.1 Flash is offered, what each route charges, and how each endpoint is holding up.

Where it is offered

9 of 19 routes GILD tracks offer DeepSeek V4.1 Flash today, preview included. Read Oct 28, 2026 from each route's own pages.

Route	Status	Model ID	Limits and lifecycle	Sources
Claude Code	Available	deepseek-flash	DeepSeek documents this route and Anthropic doesn't: point `ANTHROPIC_BASE_URL` at `https://api.deepseek.com/anthropic`; deepseek-flash serves V4.1-Flash-1017	Anthropic DeepSeek, Integrate with Claude Code DeepSeek, Models & Pricing
Codex	Available	deepseek-flash	DeepSeek documents this as a Codex custom model provider, and OpenAI doesn't	DeepSeek, Integrate with Codex DeepSeek, Models & Pricing
Cursor	Available		DeepSeek V4.1 Flash 1017, hidden by default, US$0.30/US$1.20	Anysphere (Cursor)
Databricks	Available	databricks-deepseek-v4-1-flash-1017databricks-deepseek-v4-1-flash	V4.1 Flash (1017) and V4.1 Flash; V4.1 Flash retires Nov 19, 2026	Databricks, Databricks-hosted inference Databricks, Detailed list of models supported by Databricks Foundation Model APIs
The maker's own API	Available	deepseek-flash (serves DeepSeek-V4.1-Flash-1017)	1M context, 384K max output; off-peak price half of peak; legacy deepseek-v4-flash routes here	DeepSeek, Models & Pricing DeepSeek, DeepSeek-V4.1-Flash: Smarter, Faster, More Efficient
GitHub Models	Retired	not applicableservice retired	service retired July 30, 2026	GitHub, GitHub Models
Google Cloud Vertex / Agent Platform (Google managed)	Preview	deepseek-v4.1-flash-maas	Preview MaaS since Oct 14, 2026; global only; no batch, no Provisioned Throughput; available until at least Nov 28, 2026	Google
OpenCode	Available	deepseek-v4.1-flashdeepseek-v4.1-flash-free	Zen/Console, free tier and Go; on Go an owner must select Global under Regions	Anomaly (OpenCode)DeepSeek Hacker News
OpenRouter	Available	deepseek/deepseek-v4.1-flashdeepseek/deepseek-v4.1-flash-1017	1,048,576 context; 30+ third-party providers, mostly FP8/FP4 quantized	OpenRouter
Windsurf / Devin	Available	deepseek-v4-1-flash-highdeepseek-v4-1-flash-max	Pro tier only (High, Max)	Cognition (Devin, formerly Windsurf), AI Models

Not offered on 4 routes: Amazon Bedrock, Antigravity, GitHub Copilot and Microsoft Foundry.

5 routes do not say: Claude on Google Cloud (Anthropic managed), Claude Platform on AWS, Gemini CLI, Grok Build and Snowflake Cortex.

What each of these 9 routes' pages say, with sources

Route	Status	Limits and lifecycle	Sources
Amazon Bedrock	Not offered	Bedrock lists DeepSeek V3.2, V3.1, R1	Amazon Web Services, Models at a glance Amazon Web Services, Supported foundation models in Amazon Bedrock
Antigravity	Not offered	Antigravity lists Gemini, Claude and GPT-OSS models only	Google
GitHub Copilot	Not offered	Absent from the Copilot supported-models table, which lists each supported model	GitHub, Supported AI models in GitHub Copilot
Microsoft Foundry	Not offered	Foundry lists DeepSeek-V4-Pro, DeepSeek-V4-Flash-0731 and DeepSeek-V4-Flash	Microsoft Learn, Foundry Models sold by Azure
Claude on Google Cloud (Anthropic managed)	Not stated	Region, tier and limits not stated on the route's pages.
Claude Platform on AWS	Not stated	Region, tier and limits not stated on the route's pages.
Gemini CLI	Not stated	Region, tier and limits not stated on the route's pages.
Grok Build	Not stated	Grok Build pages name only grok-4.7	xAI (SpaceXSI)
Snowflake Cortex	Not stated	Region, tier and limits not stated on the route's pages.

Prices by route

The lowest standard rate is Decart (fp4) through OpenRouter, at $0.09 / $0.18 per million input and output tokens. US dollars per million tokens, as each route lists them, one row per route and rate plan, cheapest standard rate first after the maker's own; a blended rate states its mix. OpenRouter's endpoints follow under one heading, each with its monitor's uptime over 1 day and 30 minutes and the context and output it serves.

Route	Standard, in / out	Cache read	Other rates	Source, or uptime (1 day / 30 min) and limits
DeepSeek APIMakerpeak hours	$0.3 / $1.20	$0.006	Cache read $0.003, off-peak hoursOff-peak $0.15 / $0.6, off-peak hours	DeepSeek, Oct 28, 2026
OpenRouter	$0.14 / $0.42	$0.0042		OpenRouter, Oct 28, 2026
Artificial Analysis price record	$0.3 / $1.20	$0.006	Blended $0.52, 3 input : 1 output, no cacheBlended $0.18, 7 cache-hit : 2 input : 1 output	Artificial Analysis, Oct 27, 2026
Through OpenRouter, 41 provider endpoints. OpenRouter, Oct 28, 2026
Decart (fp4)Lowest	$0.09 / $0.18	$0.018		Up 100% / 100%1.05M context, 262K output
Sail Research (fp4, us)	$0.09 / $0.25	$0.006		Up 100% / 100%1.05M context, 384K output
Sail Research (fp4)	$0.09 / $0.25	$0.006		Up 100% / 100%1.05M context, 384K output
OpenInference (fp4)	$0.0014 / $0.54	$0.00075		Up 100% / 100%1.05M context, 944K output
Relace	$0.07 / $0.5	$0.008		Up 100% / 100%1.05M context, 944K output
Morph (fp8)	$0.063 / $0.55	$0.004		Up 100% / 100%1.05M context, 944K output
DeepInfra (fp8)30% discount included	$0.14 / $0.42	$0.0042		Up 100% / 100%1.05M context, 131K output
InferenceNet (fp8)	$0.1 / $0.6	$0.008		Up 100% / 100%1.04M context, 393K output
InferenceNet (us)	$0.1 / $0.6	$0.008		Up 100% / 100%1.04M context, 393K output
AtlasCloud (fp8)	$0.14 / $0.54	$0.013		Up 99.9% / 100%1.05M context, 393K output
StreamLake (fp8)51% discount included	$0.15 / $0.59	$0.0029		Up 99.9% / 100%1.02M context, 384K output
CoreWeave (fp8)	$0.13 / $0.65	$0.002		Up 99.1% / 100%1.05M context, 944K output
Reka30% discount included	$0.1 / $0.84	$0.007		Up 99.3% / 96.7%1.05M context, 393K output
ModelRun (fp4)	$0.17 / $0.67	$0.006		Up 100% / 100%1.05M context, 131K output
Wafer (mxfp4)	$0.19 / $0.65	$0.012		Up 100% / 100%1.05M context, 944K output
GMICloud (fp8)40% discount included	$0.18 / $0.72	$0.0036		Up 99.5% / 100%1.05M context, 944K output
Novita (fp8)35% discount included	$0.2 / $0.78	$0.0039		Up 100% / 100%1.05M context, 393K output
Fireworks (spot)	$0.21 / $0.84	$0.0042		Up 100% / 100%1.05M context, 944K output
Phala30% discount included	$0.21 / $0.84	$0.0042		Up 100% / 100%1.05M context, 944K output
DigitalOcean	$0.23 / $0.9	$0.006		Up 100% / 100%1.05M context, 944K output
Mancer 2 (fp8)	$0.2 / $1			Up 97.4% / 100%1.05M context, 944K output
Makora (fp8)	$0.26 / $1.17	$0.0058		Up 99.9% / 100%1.05M context, 944K output
Crusoe (fp8)	$0.29 / $1.20	$0.007		Up 100% / 100%1.05M context, 944K output
Baidu (fp8)	$0.3 / $1.20	$0.006		Up 100% / 100%1.05M context, 393K output
BaseTen	$0.3 / $1.20	$0.007		Up 100% / 100%1.05M context, 262K output
BaseTen (us)	$0.3 / $1.20	$0.007		Up 100% / 100%1.05M context, 262K output
Cohere	$0.3 / $1.20	$0.003		Up 99.9% / 100%1.05M context, 384K output
Fireworks	$0.3 / $1.20	$0.006		Up 99.7% / 100%1.05M context, 944K output
Google (global)	$0.3 / $1.20	$0.03		Up 99.6% / 100%1.05M context, 944K output
Modal	$0.3 / $1.20	$0.03		Up 99.9% / 100%1.05M context, 944K output
Nebius (fp8)	$0.3 / $1.20	$0.01		Up 99.0% / 100%1.05M context, 943K output
Parasail (fp8)	$0.3 / $1.20	$0.006		Up 100% / 100%1.05M context, 944K output
PrimeIntellect	$0.3 / $1.20	$0.006		Up 100% / 100%1.05M context, 131K output
SiliconFlow (fp8)	$0.3 / $1.20	$0.006		Up 99.9% / 100%1.05M context, 393K output
Together	$0.3 / $1.20	$0.006		Up 100% / 100%1.05M context, 944K output
Venice (fp8)	$0.3 / $1.20	$0.0075		Up 100% / 100%1M context, 131K output
Alibaba00:00 to 14:00 UTC	$0.33 / $1.32	$0.033	Cache read $0.017, 14:00 to 00:00 UTCOff-peak $0.17 / $0.66, 14:00 to 00:00 UTC	Up 100% / 100%1M context, 393K output
Fireworks (us)	$0.45 / $1.80	$0.009		Up 100% / 100%1.05M context, 944K output
BaseTen (fast, priority)			Cache read $0.014, priority tierPriority $0.6 / $2.40, priority tier	Up 55.7% / 9.1%1.05M context, 131K output
DeepInfra (fp8, flex)			Cache read $0.0034, flex tier, 30% discount includedFlex $0.11 / $0.34, flex tier, 30% discount included	Up 98.4% / 100%1.05M context, 131K output
DeepInfra (fp8, priority)			Cache read $0.0063, priority tier, 30% discount includedPriority $0.21 / $0.63, priority tier, 30% discount included	Up 69.9% / 96.7%1.05M context, 131K output

Uptime and speed by endpoint

Each provider endpoint as its monitor reports it: uptime over the last day and the last 30 minutes, output speed and time to first token, and the limits it serves. A blank is a figure the monitor does not publish for that endpoint.

Endpoint	Uptime, 1 day	Output speed	First token	Source
Artificial Analysis: Baseten (FAST)		868 tokens/s	0.8 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Baseten		377 tokens/s	0.5 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Bitdeer AI		165 tokens/s	1.9 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: CoreWeave		322 tokens/s	1.0 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Crusoe		145 tokens/s	0.8 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Databricks		123 tokens/s	1.2 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: DeepInfra		124 tokens/s	0.7 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: DigitalOcean		65 tokens/s	0.8 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: GMI		147 tokens/s	2.1 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Makora		189 tokens/s	0.7 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Modal		128 tokens/s	0.7 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Nebius		167 tokens/s	1.3 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Novita		162 tokens/s	1.4 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Parasail		207 tokens/s	0.6 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Scaleway		257 tokens/s	1.4 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: SiliconFlow		124 tokens/s	1.8 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Together AI		416 tokens/s	0.3 s	Artificial Analysis, Oct 28, 2026
Artificial Analysis: Wafer		363 tokens/s	0.5 s	Artificial Analysis, Oct 28, 2026
Status page: DeepSeek V4.1 Flash API服务(API Service)	99.5%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation.			DeepSeek, Oct 28, 2026

Lineup

DeepSeek's 2 releases in 2 families, oldest to newest along each tier, with list price and speed. DeepSeek V4.1 Flash is lit.

DeepSeek V4.1
DeepSeek's V4.1 family for frontier reasoning and software work.
1 release
1. This pageFlashSep 10, 2026Current. In 3 products we coverPrice$0.30 / $1.20Speed176.7 tok/s
DeepSeek V4
DeepSeek's V4 family of open-weight mixture-of-experts models.
1 release
1. ProAug 13, 2026Current. In 2 products we coverPrice$1.32 / $3.96, peak hoursSpeed74.4 tok/s

Where it fits

The layers of Agentic Software Development that DeepSeek V4.1 Flash can power. A product supplies the rest.

Product & Engineering, Software Development

Agentic Software Development

Primary role: Foundation Model / Reasoning Engine

DeepSeek V4.1 Flash can power coding, reasoning, and validation work inside an agentic software-development product. The Model does not itself provide the surrounding interface, execution environment, or system of record.

Work planning & task decomposition
The Model can assist with work planning & task decomposition when a Product supplies the necessary context and tools.
Supporting
Codebase context & intelligence
The provider documents long-context or codebase-oriented reasoning suitable for this layer. DeepSeek's page
Strong fit
Coding & implementation
The provider documents this Model for software-engineering and coding work. DeepSeek's page
Strong fit
Debugging & investigation
The Model can assist with debugging & investigation when a Product supplies the necessary context and tools.
Supporting
Testing & verification
The Model can assist with testing & verification when a Product supplies the necessary context and tools.
Supporting
Code review & quality
The Model can assist with code review & quality when a Product supplies the necessary context and tools.
Supporting

Products

We cover 3 products that offer DeepSeek V4.1 Flash today, all subscriptions.

Through a subscription

Limits each vendor states

OpenCodeGo, Go Plus: Estimated requests per 5 hours: Go 34,670, Go Plus 69,330; monthly usage: Go $80, Go Plus $160
ClineClinePass: 2 to 5 times the usage the standard API rate buys on popular open coding models, measured against a 5-hour rolling window, a weekly limit and a monthly limit; Cline publishes no figures for those limits.

Plan access as each vendor's page states it: Anomaly (Oct 28, 2026), Cline (Oct 28, 2026).

Comparisons

GILD comparisons that name DeepSeek V4.1 Flash, then those of the clouds and APIs that serve it.

What practitioners report

Strongest on Speed, weakest on Developer experience.

Each figure is the share of accounts naming DeepSeek V4.1 Flash that speak well of it on that axis. An axis is scored once praise and concerns are both on record from at least five accounts by three authors. An account from the maker, a company that sells or serves the model, or the maker's expert program is listed and counts toward no figure.

Share of practitioner accounts favorable to DeepSeek V4.1 Flash on each of seven axes, with the accounts behind each figure
Axis	Favorable	Accounts
Quality and reasoningWhether its answers and code are right on the author's own task: reasoning, accuracy, instruction following, and consistency.	61%	25 favorable, 16 not, 40 authors
Tool use and structured outputWhether it calls tools correctly, returns valid structured output, and keeps agent loops on track.	59%	10 favorable, 7 not, 17 authors
Long contextHow it holds up over long documents, large codebases, long sessions, and many images or PDFs.	58%	7 favorable, 5 not, 12 authors
Developer experienceHow it is to build with: prompting, steering, effort settings, and support across SDKs and providers.	35%	8 favorable, 15 not, 23 authors
CostWhat the work costs: price per task, token use, and what caching and batch save.	66%	25 favorable, 13 not, 37 authors
SpeedResponse time, time to first token, and throughput.	68%	21 favorable, 10 not, 30 authors
ReliabilityWhether requests keep succeeding: overloaded errors, outages, rate limits, and changes that broke what worked.	42%	5 favorable, 7 not, 12 authors

Every model on these seven axes

Share of accounts favorable on each axis for DeepSeek V4.1 Flash and every other model GILD has accounts for
Axis	DeepSeek V4.1 Flash	DeepSeek V4 Pro	Claude Haiku 5.5	Claude Sonnet 5.5	Claude Opus 5.5	Claude Fable 5.1	Claude Opus 5	Claude Sonnet 5	Claude Fable 5	Claude Opus 4.8	Claude Opus 4.7	Claude Sonnet 4.6	Claude Opus 4.6	Claude Opus 4.5	Claude Haiku 4.5	Claude Sonnet 4.5	GPT-6.1 Terra	GPT-6.1 Sol	GPT-6 Luna	GPT-6 Astra	GPT-5.6 Sol	GPT-5.6 Terra	GPT-5.6 Luna	GPT-5.5	GPT-5.4	gpt-oss-120b	GPT-6 Sol	GPT-5.3-Codex	Gemini 4 Argon	Gemini 3.8 Flash	Gemma 4 31B	Gemini 3.1 Pro Preview	Gemini 3.6 Flash	Kimi K3.1 Pro	Kimi K3	Kimi K2.7 Code	Mistral Large 4	Mistral Medium 3.5	Grok 4.7	Grok 4.6	Grok 4.5	MiniMax M3.1 Flash	Mercury 3	GLM-5.3-Flash	GLM-5.3	GLM-5.2	Muse Spark Open	Muse Wonder	Muse Spark 1.3	Llama 4 Scout	Llama 4 Maverick	Qwen3.8 27B	Qwen3.8 Max	MiMo-V2.6-Pro	MiMo-V2.6-Flash	Ling 3.1 Flash	Step 5 Preview
Quality and reasoning	61%25 favorable, 16 not, 40 authors	Only concerns on record0 favorable, 4 not, 4 authors	73%19 favorable, 7 not, 26 authors	64%14 favorable, 8 not, 22 authors	68%17 favorable, 8 not, 25 authors	54%15 favorable, 13 not, 28 authors	50%6 favorable, 6 not, 12 authors	56%9 favorable, 7 not, 16 authors	68%13 favorable, 6 not, 18 authors	68%17 favorable, 8 not, 25 authors	57%12 favorable, 9 not, 21 authors	67%12 favorable, 6 not, 18 authors	70%19 favorable, 8 not, 27 authors	62%13 favorable, 8 not, 20 authors	58%14 favorable, 10 not, 24 authors	69%11 favorable, 5 not, 16 authors	33%3 favorable, 6 not, 8 authors	55%12 favorable, 10 not, 22 authors	55%16 favorable, 13 not, 29 authors	44%12 favorable, 15 not, 26 authors	33%3 favorable, 6 not, 9 authors	38%3 favorable, 5 not, 8 authors	Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors	33%4 favorable, 8 not, 12 authors	33%2 favorable, 4 not, 6 authors	58%22 favorable, 16 not, 38 authors	31%10 favorable, 22 not, 32 authors	Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors	63%22 favorable, 13 not, 35 authors	56%27 favorable, 21 not, 45 authors	80%24 favorable, 6 not, 28 authors	56%20 favorable, 16 not, 36 authors	Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors	59%20 favorable, 14 not, 32 authors	Only praise on record3 favorable, 0 not, 3 authors	50%3 favorable, 3 not, 6 authors	58%11 favorable, 8 not, 18 authors	36%5 favorable, 9 not, 14 authors	43%16 favorable, 21 not, 36 authors	86%6 favorable, 1 not, 7 authors	60%3 favorable, 2 not, 5 authors	62%13 favorable, 8 not, 21 authors	86%18 favorable, 3 not, 21 authors	67%14 favorable, 7 not, 21 authors	76%47 favorable, 15 not, 61 authors	71%5 favorable, 2 not, 7 authors	Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors	20%1 favorable, 4 not, 5 authors	Only praise on record2 favorable, 0 not, 2 authors	53%8 favorable, 7 not, 15 authors	44%8 favorable, 10 not, 17 authors	71%24 favorable, 10 not, 34 authors	57%4 favorable, 3 not, 7 authors	22%2 favorable, 7 not, 9 authors	40%4 favorable, 6 not, 10 authors	Only concerns on record0 favorable, 1 not, 1 author	Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors
Tool use and structured output	59%10 favorable, 7 not, 17 authors	Only concerns on record0 favorable, 1 not, 1 author	67%6 favorable, 3 not, 9 authors	40%4 favorable, 6 not, 10 authors	46%6 favorable, 7 not, 13 authors	30%3 favorable, 7 not, 10 authors	Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors	70%7 favorable, 3 not, 10 authors	57%4 favorable, 3 not, 7 authors	50%4 favorable, 4 not, 8 authors	50%4 favorable, 4 not, 8 authors	29%2 favorable, 5 not, 7 authors	33%4 favorable, 8 not, 12 authors	70%7 favorable, 3 not, 10 authors	43%3 favorable, 4 not, 6 authors	50%3 favorable, 3 not, 6 authors	40%2 favorable, 3 not, 5 authors	40%4 favorable, 6 not, 10 authors	33%3 favorable, 6 not, 9 authors	62%8 favorable, 5 not, 13 authors	No accounts yet	Only concerns on record0 favorable, 2 not, 2 authors	Only praise on record1 favorable, 0 not, 1 author	Only praise on record1 favorable, 0 not, 1 author	No accounts yet	27%4 favorable, 11 not, 15 authors	56%5 favorable, 4 not, 9 authors	No accounts yet	25%2 favorable, 6 not, 8 authors	47%7 favorable, 8 not, 15 authors	67%4 favorable, 2 not, 6 authors	54%7 favorable, 6 not, 12 authors	Only concerns on record0 favorable, 1 not, 1 author	Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors	Only concerns on record0 favorable, 1 not, 1 author	No accounts yet	38%5 favorable, 8 not, 13 authors	Only concerns on record0 favorable, 5 not, 5 authors	38%6 favorable, 10 not, 16 authors	Only praise on record2 favorable, 0 not, 2 authors	Only concerns on record0 favorable, 1 not, 1 author	67%4 favorable, 2 not, 6 authors	80%8 favorable, 2 not, 10 authors	33%2 favorable, 4 not, 6 authors	57%4 favorable, 3 not, 7 authors	No accounts yet	No accounts yet	Only concerns on record0 favorable, 1 not, 1 author	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	Needs 5 accounts from 3 authors1 favorable, 2 not, 3 authors	Only concerns on record0 favorable, 4 not, 4 authors	46%6 favorable, 7 not, 13 authors	Only praise on record1 favorable, 0 not, 1 author	Only concerns on record0 favorable, 5 not, 5 authors	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 1 not, 1 author
Long context	58%7 favorable, 5 not, 12 authors	No accounts yet	29%2 favorable, 5 not, 7 authors	60%3 favorable, 2 not, 5 authors	80%8 favorable, 2 not, 10 authors	63%5 favorable, 3 not, 8 authors	67%4 favorable, 2 not, 6 authors	50%3 favorable, 3 not, 6 authors	33%2 favorable, 4 not, 6 authors	60%6 favorable, 4 not, 10 authors	50%4 favorable, 4 not, 7 authors	71%5 favorable, 2 not, 7 authors	64%7 favorable, 4 not, 11 authors	40%4 favorable, 6 not, 10 authors	40%2 favorable, 3 not, 5 authors	50%4 favorable, 4 not, 8 authors	Only concerns on record0 favorable, 1 not, 1 author	38%3 favorable, 5 not, 8 authors	20%1 favorable, 4 not, 5 authors	27%3 favorable, 8 not, 11 authors	No accounts yet	No accounts yet	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 1 not, 1 author	Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors	25%2 favorable, 6 not, 8 authors	Only concerns on record0 favorable, 3 not, 3 authors	No accounts yet	25%2 favorable, 6 not, 8 authors	38%5 favorable, 8 not, 13 authors	20%1 favorable, 4 not, 5 authors	44%7 favorable, 9 not, 16 authors	Only praise on record1 favorable, 0 not, 1 author	Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors	Only praise on record1 favorable, 0 not, 1 author	Only concerns on record0 favorable, 1 not, 1 author	Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors	No accounts yet	42%5 favorable, 7 not, 12 authors	No accounts yet	No accounts yet	40%2 favorable, 3 not, 5 authors	60%3 favorable, 2 not, 5 authors	No accounts yet	40%2 favorable, 3 not, 5 authors	No accounts yet	No accounts yet	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 3 not, 3 authors	Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors	75%6 favorable, 2 not, 8 authors	Only praise on record1 favorable, 0 not, 1 author	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	No accounts yet	No accounts yet
Developer experience	35%8 favorable, 15 not, 23 authors	No accounts yet	25%2 favorable, 6 not, 8 authors	38%5 favorable, 8 not, 13 authors	38%6 favorable, 10 not, 16 authors	50%7 favorable, 7 not, 14 authors	25%3 favorable, 9 not, 12 authors	29%2 favorable, 5 not, 7 authors	25%3 favorable, 9 not, 12 authors	53%10 favorable, 9 not, 19 authors	29%4 favorable, 10 not, 14 authors	57%4 favorable, 3 not, 7 authors	45%5 favorable, 6 not, 11 authors	67%8 favorable, 4 not, 12 authors	Needs 5 accounts from 3 authors2 favorable, 2 not, 4 authors	50%3 favorable, 3 not, 6 authors	Only concerns on record0 favorable, 2 not, 2 authors	27%3 favorable, 8 not, 11 authors	25%2 favorable, 6 not, 8 authors	41%7 favorable, 10 not, 17 authors	No accounts yet	Only concerns on record0 favorable, 1 not, 1 author	No accounts yet	No accounts yet	No accounts yet	14%1 favorable, 6 not, 7 authors	25%3 favorable, 9 not, 12 authors	No accounts yet	33%3 favorable, 6 not, 9 authors	39%7 favorable, 11 not, 17 authors	Only concerns on record0 favorable, 7 not, 7 authors	40%8 favorable, 12 not, 20 authors	No accounts yet	9%1 favorable, 10 not, 11 authors	No accounts yet	Only concerns on record0 favorable, 1 not, 1 author	13%2 favorable, 14 not, 16 authors	Only concerns on record0 favorable, 9 not, 9 authors	25%5 favorable, 15 not, 20 authors	No accounts yet	No accounts yet	Only concerns on record0 favorable, 7 not, 7 authors	11%1 favorable, 8 not, 9 authors	Only concerns on record0 favorable, 3 not, 3 authors	33%5 favorable, 10 not, 15 authors	No accounts yet	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 1 not, 1 author	No accounts yet	Only concerns on record0 favorable, 6 not, 6 authors	Only concerns on record0 favorable, 1 not, 1 author	17%1 favorable, 5 not, 6 authors	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 1 not, 1 author	No accounts yet	No accounts yet
Cost	66%25 favorable, 13 not, 37 authors	Only praise on record2 favorable, 0 not, 2 authors	69%20 favorable, 9 not, 29 authors	45%10 favorable, 12 not, 22 authors	56%15 favorable, 12 not, 27 authors	20%4 favorable, 16 not, 20 authors	43%3 favorable, 4 not, 7 authors	39%7 favorable, 11 not, 17 authors	8%1 favorable, 11 not, 12 authors	40%6 favorable, 9 not, 14 authors	50%7 favorable, 7 not, 13 authors	50%6 favorable, 6 not, 12 authors	33%7 favorable, 14 not, 20 authors	53%8 favorable, 7 not, 15 authors	29%4 favorable, 10 not, 12 authors	38%3 favorable, 5 not, 8 authors	80%8 favorable, 2 not, 9 authors	74%14 favorable, 5 not, 19 authors	79%22 favorable, 6 not, 26 authors	29%7 favorable, 17 not, 23 authors	57%4 favorable, 3 not, 7 authors	Only concerns on record0 favorable, 1 not, 1 author	50%3 favorable, 3 not, 6 authors	Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors	63%5 favorable, 3 not, 8 authors	60%3 favorable, 2 not, 5 authors	55%12 favorable, 10 not, 21 authors	Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors	35%7 favorable, 13 not, 20 authors	58%14 favorable, 10 not, 23 authors	Only praise on record7 favorable, 0 not, 7 authors	50%8 favorable, 8 not, 16 authors	Only concerns on record0 favorable, 1 not, 1 author	40%10 favorable, 15 not, 25 authors	71%5 favorable, 2 not, 7 authors	Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors	27%3 favorable, 8 not, 11 authors	Only concerns on record0 favorable, 8 not, 8 authors	19%5 favorable, 21 not, 26 authors	20%1 favorable, 4 not, 5 authors	Only praise on record4 favorable, 0 not, 4 authors	80%12 favorable, 3 not, 15 authors	50%6 favorable, 6 not, 12 authors	76%16 favorable, 5 not, 21 authors	35%8 favorable, 15 not, 23 authors	71%5 favorable, 2 not, 7 authors	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 9 not, 9 authors	Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors	Only praise on record1 favorable, 0 not, 1 author	Only praise on record1 favorable, 0 not, 1 author	Needs 5 accounts from 3 authors2 favorable, 2 not, 4 authors	Only concerns on record0 favorable, 2 not, 2 authors	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	Only praise on record3 favorable, 0 not, 3 authors	Only praise on record1 favorable, 0 not, 1 author	Only praise on record1 favorable, 0 not, 1 author
Speed	68%21 favorable, 10 not, 30 authors	Only concerns on record0 favorable, 1 not, 1 author	74%14 favorable, 5 not, 19 authors	88%14 favorable, 2 not, 16 authors	46%6 favorable, 7 not, 13 authors	44%4 favorable, 5 not, 9 authors	60%3 favorable, 2 not, 5 authors	44%4 favorable, 5 not, 9 authors	57%4 favorable, 3 not, 7 authors	31%4 favorable, 9 not, 13 authors	63%5 favorable, 3 not, 8 authors	17%1 favorable, 5 not, 6 authors	36%4 favorable, 7 not, 11 authors	73%8 favorable, 3 not, 11 authors	78%7 favorable, 2 not, 9 authors	75%6 favorable, 2 not, 8 authors	83%5 favorable, 1 not, 5 authors	37%7 favorable, 12 not, 19 authors	45%5 favorable, 6 not, 11 authors	41%7 favorable, 10 not, 17 authors	Only praise on record1 favorable, 0 not, 1 author	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	80%4 favorable, 1 not, 5 authors	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	No accounts yet	85%23 favorable, 4 not, 27 authors	57%4 favorable, 3 not, 7 authors	Only praise on record1 favorable, 0 not, 1 author	45%5 favorable, 6 not, 11 authors	61%19 favorable, 12 not, 31 authors	53%10 favorable, 9 not, 17 authors	35%6 favorable, 11 not, 16 authors	Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors	21%4 favorable, 15 not, 19 authors	Only concerns on record0 favorable, 2 not, 2 authors	No accounts yet	21%3 favorable, 11 not, 14 authors	Only concerns on record0 favorable, 2 not, 2 authors	29%5 favorable, 12 not, 17 authors	Needs 5 accounts from 3 authors1 favorable, 2 not, 3 authors	Only praise on record2 favorable, 0 not, 2 authors	44%4 favorable, 5 not, 9 authors	65%13 favorable, 7 not, 20 authors	33%3 favorable, 6 not, 9 authors	20%3 favorable, 12 not, 15 authors	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 1 not, 1 author	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors	No accounts yet	33%7 favorable, 14 not, 21 authors	Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors	Only concerns on record0 favorable, 4 not, 4 authors	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 2 not, 2 authors	Only concerns on record0 favorable, 1 not, 1 author
Reliability	42%5 favorable, 7 not, 12 authors	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	20%1 favorable, 4 not, 5 authors	18%2 favorable, 9 not, 11 authors	13%1 favorable, 7 not, 8 authors	9%1 favorable, 10 not, 11 authors	13%1 favorable, 7 not, 8 authors	63%5 favorable, 3 not, 7 authors	22%2 favorable, 7 not, 9 authors	17%2 favorable, 10 not, 12 authors	18%2 favorable, 9 not, 11 authors	17%1 favorable, 5 not, 6 authors	20%3 favorable, 12 not, 15 authors	38%6 favorable, 10 not, 16 authors	29%2 favorable, 5 not, 7 authors	40%4 favorable, 6 not, 10 authors	Only concerns on record0 favorable, 1 not, 1 author	13%1 favorable, 7 not, 8 authors	Only concerns on record0 favorable, 5 not, 5 authors	14%2 favorable, 12 not, 14 authors	Only concerns on record0 favorable, 4 not, 4 authors	Only concerns on record0 favorable, 1 not, 1 author	No accounts yet	Only concerns on record0 favorable, 2 not, 2 authors	Only concerns on record0 favorable, 4 not, 4 authors	Only concerns on record0 favorable, 4 not, 4 authors	17%1 favorable, 5 not, 6 authors	Only concerns on record0 favorable, 3 not, 3 authors	5%1 favorable, 18 not, 19 authors	15%3 favorable, 17 not, 20 authors	Only concerns on record0 favorable, 5 not, 5 authors	14%3 favorable, 19 not, 22 authors	Only concerns on record0 favorable, 1 not, 1 author	8%1 favorable, 12 not, 13 authors	Only concerns on record0 favorable, 1 not, 1 author	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	38%3 favorable, 5 not, 8 authors	Only concerns on record0 favorable, 4 not, 4 authors	22%2 favorable, 7 not, 9 authors	Only concerns on record0 favorable, 1 not, 1 author	No accounts yet	60%3 favorable, 2 not, 5 authors	33%2 favorable, 4 not, 5 authors	Only concerns on record0 favorable, 7 not, 7 authors	Only concerns on record0 favorable, 4 not, 4 authors	Only concerns on record0 favorable, 1 not, 1 author	No accounts yet	Only concerns on record0 favorable, 2 not, 2 authors	Only concerns on record0 favorable, 1 not, 1 author	Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 9 not, 9 authors	Only concerns on record0 favorable, 1 not, 1 author	Only concerns on record0 favorable, 2 not, 2 authors	No accounts yet	No accounts yet	Only concerns on record0 favorable, 1 not, 1 author

The 115 accounts behind DeepSeek V4.1 Flash's figures

GILD searched Hacker News, GitHub, Reddit, X, Artificial Analysis, OpenRouter, LMArena, Vals AI and the web for accounts that name DeepSeek V4.1 Flash.

Quality and reasoning 61%

Favorable
amongst the leading models in intelligence
Artificial Analysis - DeepSeek V4.1 Flash. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. DeepSeek V4.1 Flash (reasoning).
Favorable
DeepSeek Flash V4.1 often performs at the level of GPT 5.6 Sol for non-orchestration tasks (programming and maths)
Comment on: I am often wrong. fractorial, Hacker News commenter, self-described, Sep 20, 2026. DeepSeek V4.1 Flash in a self-built Go coding harness.
Favorable
IME GLM is inferior compared to Deepseek v4.1 Flash
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. runtime_terror, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash on real work.
Favorable
the speedy work it does is pretty much always correct
Comment on: MiniMax M3.1 Flash. TheArcane, Hacker News commenter, self-described, Oct 20, 2026. DeepSeek V4.1 Flash through platform.deepseek.com.
Favorable
Took DeepSeek v4.1 flash around an hour to get a full OpenWRT build-and-flash pipeline up and running
Comment on: Using AI to reverse engineer hardware. cookiengineer, Hacker News commenter, self-described, Oct 16, 2026. DeepSeek V4.1 Flash as a reverse-engineering agent.
Unfavorable
they're more likely to go down rabbit holes, confidently assert falsehoods, or fix bugs with changes to my test harness rather than my code
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. aftbit, Hacker News commenter, self-described, Oct 9, 2026. DeepSeek V4.1 Flash (DS V4.1 Flash) on coding tasks.
Unfavorable
DeepSeek v4.1 Flash hallucinates like crazy.
Comment on: DeepSeek-v4.1-Flash-1017. pmoriarty, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash.
Unfavorable
it took ~4x longer in a review, found less and planned worse than GPT 6.1 Sol
Comment on: DeepSeek-v4.1-Flash-1017. AmazingTurtle, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash via the DeepSeek API reviewing a C++ game engine codebase.
Favorable
seems about as capable at mid-level tasks
Comment on: DeepSeek V4.1 Flash 1017 (Max). lhl, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash and DeepSeek V4.1 Flash 1017 on fast local infrastructure.
Unfavorable
Absolutely falls apart at things Opus/Sol currently excel at with low reasoning
Comment on: SlopCodeBench 10/2026 – Opus 5.5, Astra wins overall, xhigh considered harmful. ac29, Hacker News commenter, self-described, Oct 21, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, over a weekend.
Unfavorable
none of them were capable of making progress on the main project I am working on
Comment on: Anthropic S-1. ac29, Hacker News commenter, self-described, Oct 26, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, on a main project.
Unfavorable
DeepSeek 4.1 flash fails at lower thinking levels
Comment on: DeepSeek-v4.1-Flash-1017. a11r, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash (original 4.1 flash) on moderately difficult coding tasks.
Favorable
With, in my experience, practically the same results.
Comment on: DeepInfer: High-performance inference framework for DeepSeek models. inciampati, Hacker News commenter, self-described, Oct 20, 2026. DeepSeek V4.1 Flash.
Favorable
it reaches about 98% of the quality score
Abhinav Amrute on DeepSeek V4.1 Flash. Abhinav Amrute (@AbhinavAmrute), builder of Blitsum, X user, self-described, Oct 25, 2026. DeepSeek V4.1 Flash.
Unfavorable
It needs someone to hold its hand and verify every change it makes and how it affects other parts of the software.
BURKOV on DeepSeek 4.1 Flash. BURKOV (@burkov), PhD in AI and author of The Hundred-Page LMs Book per his X bio, self-described, Sep 17, 2026. DeepSeek V4.1 Flash (DeepSeek 4.1 Flash) on a software task.
Unfavorable
The first V4.1 Flash was worse even than V4 Pro in real coding tasks and MILES behind Opus 5.5.
Luke (DevMindscape) on the first V4.1 Flash. Luke (@lk_devmindscape), builder of the Gritcode coding agent, X user, self-described, Oct 25, 2026. DeepSeek V4.1 Flash, the original release, on real coding tasks.
Favorable
it fixed first serious bugs which the previous version failed gloriously
Constantin Diez on DeepSeek V4.1 Flash 1017 on DGX Spark. Constantin Diez (@ConstantinDiez), X user, self-described, Oct 25, 2026. DeepSeek V4.1 Flash and the 1017 update on 2x DGX Spark.
Favorable
it now feels clearly ahead of GLM 5.3 Flash for me on normal coding work
Yum on DeepSeek V4.1 Flash 1017 in OpenCode. Yum (@yuhasbeentaken), growth engineer who benchmarks AI agents, X user, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, in OpenCode Go.
Favorable
4.1 Flash is smart enough
jarnMod on DeepSeek 4.1 Flash. jarnMod (@jarnMod), developer and fund operator per X bio, self-described, Oct 22, 2026. DeepSeek V4.1 Flash for agentic work.
Unfavorable
Coding tasks are lousy.
Hacker News comment by harlequinetcie on DeepSeek Flash v4.1. harlequinetcie, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash (DeepSeek Flash v4.1) in a custom agent architecture.
Unfavorable
its a big mistake to have a flash model implement without a strong model reviewing
Comment on: Prompting Claude Opus 5.5. gregwebs, Hacker News commenter, self-described, Sep 28, 2026. DeepSeek V4.1 Flash (Deepseek Flash v4.1) implementing Opus plans.
Favorable
Flash 4.1 1017 is also a very good model IMO
Comment on: Kimi K3.1 Pro and K3.1 Pro Highspeed. ZiLxxi, Hacker News commenter, self-described, Oct 24, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, through DeepSeek API credits.
Unfavorable
there was an unnacceptable quality loss
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. p1necone, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash (Deepseek-V4.1-Flash) as orchestrator and implementer on a compiler project.
Favorable
this is a really strong model
Comment on: DeepSeek v4.1 Flash. LaurensBER, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash at launch.
Favorable
got a high scoring result
Comment on: DeepSeek v4.1 Flash. raesene9, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash on a personal Kubernetes security benchmark.
Unfavorable
Deepseek v4.1 flash is able to solve it some of the time
Comment on: DeepSeek v4.1 Flash. pampas, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash on the Redactle puzzle LLM leaderboard.
Unfavorable
This is definitely not on par with GPT-6 astra. Not with GPT-5.6 sol either.
Comment on: DeepSeek v4.1 Flash. WiSaGaN, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash at launch.
Favorable
It's also really solid and comparable in real world use to a sonnet for my work.
Comment on: DeepSeek v4.1 Flash. gigatexal, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash at work.
Favorable
DeepSeek V4.1 Flash 1017 (Max) is amongst the leading models in intelligence
Artificial Analysis - DeepSeek V4.1 Flash 1017. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (Max).
Favorable
if I don't look at the model name, I honestly could not tell you if I'm using DeepSeek or Opus
Why Isn't The Industry Freaking Out About DeepSeek 4.1 Flash?. Jono, author of the Jono's Corner blog, self-described, Oct 7, 2026. DeepSeek V4.1 Flash (DeepSeek 4.1 Flash) on an OpenCode Go plan across a dozen projects.
Favorable
Below is the Pareto of performance vs price on DeepSWE, a favorite agentic software engineering benchmark today, which DeepSeek dominates.
Latest open artifacts (#25): DeepSeek v4.1 Flash 1017, American-made models. Florian Brand, researcher at Prime Intellect, writing for Interconnects, self-described, Oct 20, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (DeepSeek-V4.1-Flash-1017).
Favorable
This model is very, very good. It is a really good executor.
Yume_X on DeepSeek V4.1 Flash on two DGX Sparks. Yume_X (@yume_arasaki), X user running local AI on DGX Sparks, self-described, Sep 14, 2026. DeepSeek V4.1 Flash, EXL3 build on two DGX Sparks, thinking off.
Favorable
DeepSeek V4.1 Flash is the new #1 open-weight model on the Vals Index.
Vals AI on DeepSeek V4.1 Flash. Vals AI, public LLM evaluation publisher, self-described, Sep 10, 2026. DeepSeek V4.1 Flash on the Vals Index, high reasoning effort.
Favorable
For a model very very small compared to Kimi-K3.1-pro, the difference is huge.
AJ on Kimi-K3.1-Pro vs DeepSeek-v4.1-Flash-1017. AJ (@ItsmeAjayKV), independent AI researcher, X user, self-described, Oct 24, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, on a 3D generation task without a harness.
Favorable
it earned full marks on my scoring keys and cost less with the settings I used
DeepSeek V4.1-Flash-1017 vs. Claude Haiku 5.5: More accurate at a fraction of the price. Jessica Wachtel, writer at The New Stack, self-described, Oct 22, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, via OpenRouter pinned to DeepSeek's servers, default settings, off-peak.
Favorable
DeepSeek V4.1 Flash 1017 codes about as well as Claude Sonnet 5.5 and Haiku 5.5 at their default settings, for a fraction of the price.
DeepSeek vs Claude for coding: V4.1 Flash 1017 against Opus 5.5, Sonnet 5.5 and Haiku 5.5. Bito, AI coding tools company, on its own coding benchmark, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, inside Claude Code.
Unfavorable
with almost 2x more parameters than V4 Flash, I've expected a bit more
Tested on coding and agentic work - not that impressive. curiousily, Hugging Face user, self-described, Sep 11, 2026. DeepSeek V4.1 Flash via OpenRouter on coding and agentic tests.
Favorable
This can actually build a working app from a spec - something I was not expecting to see from a model in this cost range.
Praise and critique for this model. mindplay, Hugging Face user, self-described, Sep 20, 2026. DeepSeek V4.1 Flash (DeepSeek 4.1 Flash) building an Android app from a spec.
Unfavorable
You can really see a drastic drop in intelligence
Dumb model... total degradation! Even a 9B model is more coherent than this.. RomanHammer, Hugging Face user, self-described, Oct 13, 2026. DeepSeek V4.1 Flash.
Favorable
it has proven much better than glm 5.3 so far
wow, this thing is a beast - and i've NEVER said that about a deepseek model. aaron-newsome, Hugging Face user running the model locally, self-described, Oct 20, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, on 4x RTX PRO 6000 across dozens of tickets.
Unfavorable
Opus is enough smarter than DSF that it's worth the extra steps
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. Jonathan Ellis (jbellis), SlopCop founder and CEO, DataStax co-founder and former CTO, Oct 8, 2026. DeepSeek V4.1 Flash (DeepSeek Flash, DSF) as subagents managed by Opus in mjolnir.
Favorable
I had DS working on the same project as Sol and DS was correcting Sol's work routinely.
Comment on: DeepSeek-v4.1-Flash-1017. cromka, Hacker News commenter, self-described, Oct 20, 2026. DeepSeek V4.1 Flash (Deepseek 4.1) on a shared project with GPT Sol.
Favorable
The end result is much better with improved choreography, visual effects and overall feel.
Daniel Zambrini on Deepseek V4.1 Flash 1017 vs the earlier checkpoint. Daniel Zambrini (@DanielZambrini), X user, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, against the earlier V4.1 Flash checkpoint on an animation task.
Favorable
Its best result is #1 of 35 on SkillsBench
DeepSeek V4.1 Flash Benchmarks, Cost and Capabilities | Vals AI. Vals AI, public LLM evaluation publisher, self-described, Sep 10, 2026. DeepSeek V4.1 Flash at max reasoning effort via the DeepSeek provider.
Favorable
even at IQ1_S the model nailed most of my regular benchmarks
Ran DeepSeek V4.1 Flash 1017 on a single RTX 5090. jesdga95, r/LocalLLaMA poster developing the Basalt inference engine, self-described, Oct 21, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (Unsloth UD-IQ1_S), on an RTX 5090 plus RTX 5060 Ti.

Tool use and structured output 59%

Favorable
I've ran Chief/Builder/Reviewer swarms autonomously for multiple days
Comment on: DeepSeek-v4.1-Flash-1017. ilitirit, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash in multi-day agent swarm experiments.
Unfavorable
even has basic problems with failing tool calls
Comment on: SlopCodeBench 10/2026 – Opus 5.5, Astra wins overall, xhigh considered harmful. ac29, Hacker News commenter, self-described, Oct 21, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, over a weekend.
Favorable
the list price didn't really change, but the model loops less
Yum on more DeepSeek V4.1 Flash 1017 findings. Yum (@yuhasbeentaken), growth engineer who benchmarks AI agents, X user, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, in OpenCode free and Go.
Favorable
Good enough for agentic work
jarnMod on DeepSeek 4.1 Flash. jarnMod (@jarnMod), developer and fund operator per X bio, self-described, Oct 22, 2026. DeepSeek V4.1 Flash for agentic work.
Unfavorable
It is failing frequently and writing it's own tool calling format instead.
Comment on: DeepSeek v4.1 Flash. irthomasthomas, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash through the DeepSeek API at launch.
Favorable
It can now debug pages by clicking browser itself
Comment on: DeepSeek v4.1 Flash. BrucecarlL, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash debugging pages through the browser.
Unfavorable
i got error about tools calling on opencode desktop when it use frank/DeepSeek-V4.1-Flash
deepseek v4.1 flash bug tools calling. pichead, GitHub user filing an OpenCode issue, self-described, Sep 24, 2026. DeepSeek V4.1 Flash on the OpenCode Go plan in OpenCode Desktop.
Unfavorable
Deepseek 4.1 flash is stopping constantly mid tasks with different DSML errors
deepseek 4.1 flash. darereda, GitHub user filing an OpenCode issue, self-described, Oct 1, 2026. DeepSeek V4.1 Flash (Deepseek 4.1 flash) in OpenCode.
Unfavorable
raw internal tool-call delimiters repeatedly leak into the visible conversation instead of being correctly parsed
[P1] Fireworks DeepSeek V4.1 Flash 1017 leaks raw tool-call tokens and prematurely terminates agent turns. carterlasalle, GitHub user filing an oh-my-pi issue, self-described, Oct 18, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, via Fireworks AI in oh-my-pi v18.12.2.
Favorable
We run all our Personal Assistants now on flash (thinking high) by default and it works incredibly well.
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. lmf4lol, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash (v4.1-flash, thinking high) running personal assistants.
Favorable
Luna is very slow and bad at agentic tasks. DS runs circles around it
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. pimeys, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash (DS) on agentic tasks against Luna.
Favorable
Tools: clean at every depth, real tool calls, zero XML leakage
Yume_X on DeepSeek V4.1 Flash on two DGX Sparks. Yume_X (@yume_arasaki), X user running local AI on DGX Sparks, self-described, Sep 14, 2026. DeepSeek V4.1 Flash, EXL3 build on two DGX Sparks, thinking off.
Favorable
DeepSeek started strong, beating Haiku's accuracy on the first test. DeepSeek scored 6 of 6.
DeepSeek V4.1-Flash-1017 vs. Claude Haiku 5.5: More accurate at a fraction of the price. Jessica Wachtel, writer at The New Stack, self-described, Oct 22, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, via OpenRouter pinned to DeepSeek's servers, default settings, off-peak.
Favorable
It ran the same long multi-turn sessions, tool calls and file edits as the Claude models
DeepSeek vs Claude for coding: V4.1 Flash 1017 against Opus 5.5, Sonnet 5.5 and Haiku 5.5. Bito, AI coding tools company, on its own coding benchmark, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, inside Claude Code.
Unfavorable
It repeatedly announces that it will emit an answer or tool call, but produces neither.
V4.1-Flash native reasoning loops: prompt/history contract and model-team triage request. Hui-cd, GitHub user running the open weights on four DGX Sparks, self-described, Oct 8, 2026. DeepSeek V4.1 Flash open weights on vLLM across four DGX Spark nodes.
Unfavorable
as raw internal markup in `message.content`, instead of either the structured `tool_calls` field or a plain refusal.
[BUG] deepseek-flash emits internal DSML tool-call markup in content when tools array is omitted but history carries tool_calls. monperrus, GitHub user filing a DeepSeek issue, self-described, Sep 26, 2026. DeepSeek V4.1 Flash through the DeepSeek API model name deepseek-flash.
Favorable
Also works: tool calls, images, up to 1M context
Ran DeepSeek V4.1 Flash 1017 on a single RTX 5090. jesdga95, r/LocalLLaMA poster developing the Basalt inference engine, self-described, Oct 21, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (Unsloth UD-IQ1_S), on an RTX 5090 plus RTX 5060 Ti.

Long context 58%

Unfavorable
DeepSeek v4.1 flash seems to do this thing where it starts saying that we should wrap up the current work
Comment on: The State of Context Engineering. kadoban, Hacker News commenter, self-described, Oct 25, 2026. DeepSeek V4.1 Flash in long coding sessions.
Favorable
solves a lot of problems that other models still have
Comment on: LittleJS is 2D and 3D now. cookiengineer, Hacker News commenter, self-described, Oct 15, 2026. DeepSeek V4.1 Flash (FP4).
Favorable
Needle passed at 100% depth
Mia on DeepSeek V4.1 Flash 1017 across 4x DGX Spark. Mia (@MiaAI_lab), runs open models locally, X user, self-described, Oct 25, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint with official weights, on four DGX Sparks via TensorFold.
Unfavorable
i'm seeing compression kick in around 300K context instead of being able to stretch it close to 1M
Yum on DeepSeek V4.1 Flash 1017 in OpenCode. Yum (@yuhasbeentaken), growth engineer who benchmarks AI agents, X user, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, in OpenCode Go.
Unfavorable
not taking all the relevant context into account when making decisions
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. p1necone, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash (Deepseek-V4.1-Flash) as orchestrator and implementer on a compiler project.
Favorable
that is already a weeklong project where I share the same context
Comment on: DeepSeek-v4.1-Flash-1017. pimeys, Hacker News commenter, self-described, Oct 18, 2026. DeepSeek V4.1 Flash through Coralbricks in the omp harness.
Unfavorable
The wait gets worse as more images accumulate in context.
deepseek-v4.1-flash: 10-60 s to first token once images are in context. slovx2, GitHub user filing a Command Code issue, self-described, Oct 10, 2026. DeepSeek V4.1 Flash (deepseek/deepseek-v4.1-flash) in the Command Code CLI.
Favorable
Needle retrieval: 12/12 across the ladder, out to 548,000 prompt tokens
Yume_X on DeepSeek V4.1 Flash on two DGX Sparks. Yume_X (@yume_arasaki), X user running local AI on DGX Sparks, self-described, Sep 14, 2026. DeepSeek V4.1 Flash, EXL3 build on two DGX Sparks, thinking off.
Favorable
DeepSeek got all 1,600 fields right.
DeepSeek V4.1-Flash-1017 vs. Claude Haiku 5.5: More accurate at a fraction of the price. Jessica Wachtel, writer at The New Stack, self-described, Oct 22, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, via OpenRouter pinned to DeepSeek's servers, default settings, off-peak.
Unfavorable
it runs out of context, detects that but **does not compact** automatically
claude code auto compress fails with "deepseek-v4.1-flash:1017-cloud". fce2, GitHub user filing an Ollama issue, self-described, Oct 23, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (deepseek-v4.1-flash:1017-cloud), via Ollama in Claude Code.
Favorable
Both runs scored 36/36 on the exam.
DeepSeek V4.1 Flash manages agent memory 10× less efficiently than V4 Flash (Hermes, MarathonMemBench). ovsale, Hugging Face user and author of MarathonMemBench, self-described, Sep 14, 2026. DeepSeek V4.1 Flash in Hermes on the MarathonMemBench long-conversation memory benchmark.
Favorable
up to 1M context (a 1M prompt takes ~26 min, ouch)
Ran DeepSeek V4.1 Flash 1017 on a single RTX 5090. jesdga95, r/LocalLLaMA poster developing the Basalt inference engine, self-described, Oct 21, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (Unsloth UD-IQ1_S), on an RTX 5090 plus RTX 5060 Ti.

Developer experience 35%

Unfavorable
It's also notably fast, however very verbose.
Artificial Analysis - DeepSeek V4.1 Flash. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. DeepSeek V4.1 Flash (reasoning).
Unfavorable
they have been overthinkers
Comment on: DeepSeek-v4.1-Flash-1017. a11r, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash (original 4.1 flash) on moderately difficult coding tasks.
Favorable
Works well with Copilot
Comment on: DeepSeek-v4.1-Flash-1017. summarity, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, through OpenRouter in GitHub Copilot CLI.
Unfavorable
others like to give q4 or even q2 instead of full or q8 quantisation.
lordstorm on DeepSeek v4.1 flash 1017 providers. lordstorm (@lordsturm), X user, self-described, Oct 25, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, across API providers.
Favorable
switched to deepseek-v4.1-flash from within opencode and a minute later had my confirmation, along with a really nice explanation
Comment on: Wanted to download an ISO, ended up writing a kernel exploit. wereHamster, Hacker News commenter, self-described, Oct 15, 2026. DeepSeek V4.1 Flash (deepseek-v4.1-flash) inside opencode.
Unfavorable
it can just be /really/ annoying in some roles and to chat with some of the time
Comment on: MiniMax M3.1 Flash. bitexploder, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash (DeepSeek Flash 4.1).
Unfavorable
Vanilla pi harness fails.
Hacker News comment by harlequinetcie on DeepSeek Flash v4.1. harlequinetcie, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash (DeepSeek Flash v4.1) in a custom agent architecture.
Favorable
it plays incredibly well with deepseek flash 4.1
Comment on: GPT 6.1 Sol: Near-Astra intelligence for a fifth of the price. taylorfinley, Hacker News commenter, self-described, Sep 29, 2026. DeepSeek V4.1 Flash (deepseek flash 4.1) in the Oh My Pi harness.
Unfavorable
being way too wordy and rambly in documentation despite prompting to avoid it
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. p1necone, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash (Deepseek-V4.1-Flash) as orchestrator and implementer on a compiler project.
Unfavorable
you will not believe this is the same model. Because it isn't.
Comment on: One Month Without AI. elwebmaster, Hacker News commenter, self-described, Sep 26, 2026. DeepSeek V4.1 Flash (deepseek-4.1 flash) on OpenRouter, Qwen Cloud and the DeepSeek API.
Favorable
It also seems to be more willing to just do whatever you ask of it.
Comment on: DeepSeek v4.1 Flash. LaurensBER, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash at launch.
Unfavorable
the reasoning token counts suggest to me that it doesn't actually support seven different levels
Comment on: DeepSeek v4.1 Flash. simonw, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash on OpenRouter at seven reasoning levels.
Favorable
Works correctly in opencode, but seems like they inject a system prompt
Comment on: DeepSeek v4.1 Flash. Mashimo, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash in opencode (opencode-go/deepseek-flash).
Unfavorable
the model responds with very long texts and enters an infinite loop
DeepSeek V4.1 Flash Free model responds with long text and enters infinite loop. AFOQ-M, GitHub user filing an OpenCode issue, self-described, Oct 23, 2026. DeepSeek V4.1 Flash Free in the OpenCode application.
Unfavorable
it generated 210M tokens, which is very verbose in comparison to the median of 140M
Artificial Analysis - DeepSeek V4.1 Flash 1017. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (Max).
Unfavorable
DeepSeek is horrible at grilling sessions
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. gregwebs, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash (DeepSeek 4.1 flash) as coding orchestrator and verifier.
Favorable
with no setup beyond pointing Claude Code at the model
DeepSeek vs Claude for coding: V4.1 Flash 1017 against Opus 5.5, Sonnet 5.5 and Haiku 5.5. Bito, AI coding tools company, on its own coding benchmark, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, inside Claude Code.
Unfavorable
but the model's chat template does not, so the request returns HTTP 400
[Bug]: vLLM + DeepSeek V4.1 chat template rejects reasoning_effort "medium"/"minimal". lbaauw, GitHub user filing a Hermes Agent issue, self-described, Oct 22, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, on a local vLLM server via Hermes Agent.
Unfavorable
V4.1-Flash: 33–50 agent turns for a one-field config edit
V4.1-Flash: 33–50 agent turns for a one-field config edit (verification with no stopping criterion). jiokasd3323, Hugging Face user, self-described, Sep 11, 2026. DeepSeek V4.1 Flash in the open-source deepseek-harness agent.
Unfavorable
Twelve times more memory for the same conversation, and the extra is not facts.
DeepSeek V4.1 Flash manages agent memory 10× less efficiently than V4 Flash (Hermes, MarathonMemBench). ovsale, Hugging Face user and author of MarathonMemBench, self-described, Sep 14, 2026. DeepSeek V4.1 Flash in Hermes on the MarathonMemBench long-conversation memory benchmark.
Unfavorable
find it often gets caught up in long thinking runs
Comment on: MiniMax M3.1 Flash. seabombs, Hacker News commenter, self-described, Oct 20, 2026. DeepSeek V4.1 Flash (DS-4.1-Flash) and the 1017 update.
Favorable
set it to high and life improved immediately
Comment on: Catch a Bird – I made it for my own sanity. appstorelottery, Hacker News commenter, self-described, Oct 15, 2026. DeepSeek V4.1 Flash (DeepSeek Flash 4.1) reasoning setting.
Favorable
once I have a plan, I usually delegate to deepseek flash. its quick and doesn't stray
Comment on: SlopCodeBench 10/2026 – Opus 5.5, Astra wins overall, xhigh considered harmful. cultofmetatron, Hacker News commenter, self-described, Oct 21, 2026. DeepSeek V4.1 Flash (deepseek flash) implementing Astra plans.
Unfavorable
DeepSeek V4.1 Flash may be chatty
Comment on: Sonnet 5.5. jchw, Hacker News commenter, self-described, Sep 28, 2026. DeepSeek V4.1 Flash.
Unfavorable
Mainline and ik_llama.cpp can't load this model yet.
DeepSeek-V4.1-Flash (UD-Q4_K_XL) on 4x 3090 + EPYC: 12 → 25 tok/s prose, 39 tok/s code.. C0smo777, r/LocalLLaMA poster, self-described, Oct 20, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (Unsloth UD-Q4_K_XL), in llama.cpp on 4x RTX 3090 and EPYC.

Cost 66%

Favorable
reasonably priced when comparing to other open weight models of similar size
Artificial Analysis - DeepSeek V4.1 Flash. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. DeepSeek V4.1 Flash (reasoning).
Favorable
DS 4.1's speed and price more than make up for the "intelligence" gap
Comment on: DeepSeek-v4.1-Flash-1017. ilitirit, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash in multi-day agent swarm experiments.
Favorable
all that cost me 0.86$
Comment on: Using AI to reverse engineer hardware. cookiengineer, Hacker News commenter, self-described, Oct 16, 2026. DeepSeek V4.1 Flash as a reverse-engineering agent.
Unfavorable
Deepseek v4.1 Flash is far from the pareto line. You can get the same performance for half the cost from Luna or Haiku 5.5 now
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. criley2, Hacker News commenter, self-described, Oct 9, 2026. DeepSeek V4.1 Flash against Luna, Haiku 5.5 and Sol 6.1.
Unfavorable
spends a huge amount of tokens (an order of magnitude more) at the highest thinking levels
Comment on: DeepSeek-v4.1-Flash-1017. a11r, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash (original 4.1 flash) on moderately difficult coding tasks.
Unfavorable
Somehow GPT 6.1 Sol (medium) is cheaper per task ($0.21 vs $0.26)
Comment on: DeepSeek V4.1 Flash 1017 (Max). Tiberium, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint at max effort, on Artificial Analysis.
Favorable
Deepseek v4.1 flash goes a LONG way for $20.
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. computerex, Hacker News commenter, self-described, Oct 9, 2026. DeepSeek V4.1 Flash.
Favorable
I rip about 200 million tokens at least a day and spend maybe $3 with deepseek flash v4.1
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. Primer81, Hacker News commenter, self-described, Oct 9, 2026. DeepSeek V4.1 Flash as a daily coding driver.
Favorable
it's still cheap and mostly good enough
Comment on: GPT-6 Sol and Luna. flippingheck, Hacker News commenter, self-described, Sep 23, 2026. DeepSeek V4.1 Flash (DeepSeek 4.1 Flash).
Favorable
98% of the quality at 1/50 of the price
Abhinav Amrute on DeepSeek V4.1 Flash. Abhinav Amrute (@AbhinavAmrute), builder of Blitsum, X user, self-described, Oct 25, 2026. DeepSeek V4.1 Flash.
Favorable
it easily hit 99%+ cache hit rate
Yifeng Chen on deepseek v4.1 flash cache hits. Yifeng Chen (@__fantasyman), content creator, X user, self-described, Oct 25, 2026. DeepSeek V4.1 Flash via Command Code in the Pi harness.
Favorable
the value on OpenCode Go is kind of absurd
Yum on DeepSeek V4.1 Flash 1017 in OpenCode. Yum (@yuhasbeentaken), growth engineer who benchmarks AI agents, X user, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, in OpenCode Go.
Favorable
DeepSeek Flash v4.1 is now 22x cheaper
Hacker News comment by harlequinetcie on DeepSeek Flash v4.1. harlequinetcie, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash (DeepSeek Flash v4.1) in a custom agent architecture.
Unfavorable
Deepseek v4.1 is actually the more expensive model to use
Comment on: Prompting Claude Opus 5.5. criley2, Hacker News commenter, self-described, Sep 28, 2026. DeepSeek V4.1 Flash (Deepseek Flash v4.1) implementing with Opus 5.5 reviewing.
Favorable
The cost is 20-40x less for Deepseek Flash v4.1
Comment on: Prompting Claude Opus 5.5. gregwebs, Hacker News commenter, self-described, Sep 28, 2026. DeepSeek V4.1 Flash (Deepseek Flash v4.1) implementing Opus plans.
Unfavorable
Luna still outperformed DeepSeek Flash 4.1 in cost per task on Artificial Analysis
Comment on: MiMo v2.6. user43928, Hacker News commenter, self-described, Sep 21, 2026. DeepSeek V4.1 Flash (DeepSeek Flash 4.1) on Artificial Analysis cost per task.
Unfavorable
So for output-heavy workloads and long reasoning, DS costs more than GLM.
Comment on: MiniMax M3.1 Flash. 0xbadcafebee, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash (DS 4.1 Flash) on OpenRouter pricing.
Favorable
am usually around $1 usage for the day
Comment on: Kimi K3.1 Pro and K3.1 Pro Highspeed. ZiLxxi, Hacker News commenter, self-described, Oct 24, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, through DeepSeek API credits.
Favorable
a $200 subscription wasn't enough to get through 20-30 tasks while $30 of DeepSeek was
Comment on: Claude Opus 5.5. knapcio, Hacker News commenter, self-described, Sep 23, 2026. DeepSeek V4.1 Flash (DS4.1) as the main work model.
Favorable
This cache it improvement makes the model x2-x2.5 more efficient on a long horizon tasks in terms of cost.
Comment on: DeepSeek v4.1 Flash. k9294, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash priced on a real 447-turn coding task.
Favorable
finding it very difficult to spend more than $75 in a month
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. lifeisloving, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash (deepseek v4.1) with heavy tool and MCP use.
Unfavorable
it burns through more reasoning tokens than any other model
Comment on: DeepSeek v4.1 Flash. pampas, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash on the Redactle puzzle LLM leaderboard.
Unfavorable
Official Deepseek v4.1 Flash API costs are more than GPT 5.6 Luna.
Comment on: DeepSeek v4.1 Flash. gkbrk, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash official API pricing.
Favorable
probably will set as a new baseline for modern API based LLM because it's so cheap
Comment on: DeepSeek v4.1 Flash. WiSaGaN, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash at launch.
Unfavorable
considerably (3x) more expensive
Comment on: DeepSeek v4.1 Flash. XCSme, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash on the aibenchy comparison.
Favorable
My tasks rarely go over a dollar
Comment on: DeepSeek-v4.1-Flash-1017. pimeys, Hacker News commenter, self-described, Oct 18, 2026. DeepSeek V4.1 Flash through Coralbricks in the omp harness.
Unfavorable
The day's bill is **4.3x** the estimate at published rates.
Nous Portal: deepseek-v4.1-flash cache reads billed at ~full input price. LohasGuy, GitHub user filing a Hermes Agent issue, self-described, Sep 30, 2026. DeepSeek V4.1 Flash (deepseek/deepseek-v4.1-flash) on a Nous Portal subscription via Hermes Agent.
Favorable
reasonably priced when comparing to other open weight models of similar size
Artificial Analysis - DeepSeek V4.1 Flash 1017. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (Max).
Favorable
it brought our bill down by A LOT
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. lmf4lol, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash (v4.1-flash, thinking high) running personal assistants.
Favorable
$0.003-0.006 per 1M input tokens is shockingly cheap
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. girvo, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash direct from DeepSeek in the Pi harness.
Favorable
I have rarely exceeded $1 in expected costs in a session.
Why Isn't The Industry Freaking Out About DeepSeek 4.1 Flash?. Jono, author of the Jono's Corner blog, self-described, Oct 7, 2026. DeepSeek V4.1 Flash (DeepSeek 4.1 Flash) on an OpenCode Go plan across a dozen projects.
Favorable
The model has gained a lot of (well-deserved) attention for its performance at a small price.
Latest open artifacts (#25): DeepSeek v4.1 Flash 1017, American-made models. Florian Brand, researcher at Prime Intellect, writing for Interconnects, self-described, Oct 20, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (DeepSeek-V4.1-Flash-1017).
Favorable
the cheapest model in the open-weight top 10
Vals AI on DeepSeek V4.1 Flash. Vals AI, public LLM evaluation publisher, self-described, Sep 10, 2026. DeepSeek V4.1 Flash on the Vals Index, high reasoning effort.
Favorable
DeepSeek came in at $0.07 to Haiku's $0.30
DeepSeek V4.1-Flash-1017 vs. Claude Haiku 5.5: More accurate at a fraction of the price. Jessica Wachtel, writer at The New Stack, self-described, Oct 22, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, via OpenRouter pinned to DeepSeek's servers, default settings, off-peak.
Favorable
DeepSeek costs $0.14 a session and Sonnet $1.28, so about a ninth of the money.
DeepSeek vs Claude for coding: V4.1 Flash 1017 against Opus 5.5, Sonnet 5.5 and Haiku 5.5. Bito, AI coding tools company, on its own coding benchmark, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, inside Claude Code.
Unfavorable
continuing to generate with that model costs roughly twice as much
[Plugin]: OpenCode off-peak gate for DeepSeek V4.1 Flash. capyBearista, GitHub user proposing an OpenCode plugin, self-described, Oct 26, 2026. DeepSeek V4.1 Flash (opencode-go/deepseek-v4.1-flash) as Fixer subagents in OpenCode.
Unfavorable
One run has token accounting: 2,946,619 input tokens
V4.1-Flash: 33–50 agent turns for a one-field config edit (verification with no stopping criterion). jiokasd3323, Hugging Face user, self-described, Sep 11, 2026. DeepSeek V4.1 Flash in the open-source deepseek-harness agent.
Favorable
this model represents a clear shift in capabilities at this humble price
Praise and critique for this model. mindplay, Hugging Face user, self-described, Sep 20, 2026. DeepSeek V4.1 Flash (DeepSeek 4.1 Flash) building an Android app from a spec.
Unfavorable
I've already spent 10 dollars in about 30 minutes
excessive consumption. xdjc, Hugging Face user, self-described, Oct 22, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint.
Favorable
I find it challenging to hit the $20 mark
Comment on: MiniMax M3.1 Flash. switchbak, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash (DS) through OpenRouter and Fireworks at API rates.
Favorable
the DeepSeek 4.1 models are currently the ultimate cost-effective model the rest have to match with
Comment on: MiniMax M3.1 Flash. sinuhe69, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash (Flash 4.1) on OpenRouter.
Favorable
It is the cheapest model in the top 15.
DeepSeek V4.1 Flash Benchmarks, Cost and Capabilities | Vals AI. Vals AI, public LLM evaluation publisher, self-described, Sep 10, 2026. DeepSeek V4.1 Flash at max reasoning effort via the DeepSeek provider.

Speed 68%

Favorable
At 175 tokens per second, DeepSeek V4.1 Flash (Max) is notably fast
Artificial Analysis - DeepSeek V4.1 Flash. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. DeepSeek V4.1 Flash (reasoning).
Favorable
There's something to be said for the speed with which it gets word done
Comment on: MiniMax M3.1 Flash. TheArcane, Hacker News commenter, self-described, Oct 20, 2026. DeepSeek V4.1 Flash through platform.deepseek.com.
Unfavorable
deepseek v4.1 flash via deepseek API: took 20m to find one p1 issue
Comment on: DeepSeek-v4.1-Flash-1017. AmazingTurtle, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash via the DeepSeek API reviewing a C++ game engine codebase.
Favorable
I run it on very fast local infra (200+ tok/s)
Comment on: DeepSeek V4.1 Flash 1017 (Max). lhl, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash and DeepSeek V4.1 Flash 1017 on fast local infrastructure.
Unfavorable
faster per task (2.9 vs 4.9 minutes) vs v4.1 Flash 1017 (max)
Comment on: DeepSeek V4.1 Flash 1017 (Max). Tiberium, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint at max effort, on Artificial Analysis.
Favorable
I regularly get ~300 tokens/sec with DeepSeek Flash
Comment on: DeepInfer: High-performance inference framework for DeepSeek models. RussianCow, Hacker News commenter, self-described, Oct 20, 2026. DeepSeek V4.1 Flash (DS V4.1 Flash) on agentic tasks.
Favorable
DeepSeek V4.1 Flash is an order of magnitude faster
Comment on: MiniMax M3.1 Flash. RussianCow, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash compared with MiniMax M3.1 Flash.
Favorable
DeepSeek V4.1 Flash is astoundingly fun to use because it's so quick
Comment on: DeepInfer: High-performance inference framework for DeepSeek models. inciampati, Hacker News commenter, self-described, Oct 20, 2026. DeepSeek V4.1 Flash.
Favorable
It is terribly fast! And smart. And cheap.
Comment on: Token-maxxing is dead, OpenRouter's token usage plateaus. brazukadev, Hacker News commenter, self-described, Oct 22, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, over two days.
Favorable
V4.1 Flash is fast and perfect for everything
Comment on: DeepInfer: High-performance inference framework for DeepSeek models. VonLuderitz, Hacker News commenter, self-described, Oct 20, 2026. DeepSeek V4.1 Flash through the DeepSeek API.
Favorable
It's faster, outputting roughly 176 to 221 tokens per second compared to GPT-6 Astra's 57 tokens per second
Abhinav Amrute on DeepSeek V4.1 Flash. Abhinav Amrute (@AbhinavAmrute), builder of Blitsum, X user, self-described, Oct 25, 2026. DeepSeek V4.1 Flash.
Favorable
Speed is quite good actually
Constantin Diez on DeepSeek V4.1 Flash 1017 on DGX Spark. Constantin Diez (@ConstantinDiez), X user, self-described, Oct 25, 2026. DeepSeek V4.1 Flash and the 1017 update on 2x DGX Spark.
Favorable
the combination of speed + Go limits is probably the biggest reason i keep coming back to it
Yum on more DeepSeek V4.1 Flash 1017 findings. Yum (@yuhasbeentaken), growth engineer who benchmarks AI agents, X user, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, in OpenCode free and Go.
Unfavorable
my project finishes in 5 minutes instead of 20
Comment on: Prompting Claude Opus 5.5. criley2, Hacker News commenter, self-described, Sep 28, 2026. DeepSeek V4.1 Flash (Deepseek Flash v4.1) implementing with Opus 5.5 reviewing.
Unfavorable
Afaik you cannot serve a single DeepSeek Flash 4.1 stream at 750 t/s
Comment on: Everyone should slow down AI development except for me. user43928, Hacker News commenter, self-described, Sep 13, 2026. DeepSeek V4.1 Flash (DeepSeek Flash 4.1).
Unfavorable
DeepSeek V4.1 can run on my setup, but it's slow.
Comment on: IBM CEO says open weight models are now good and cheap on-prem. _ache_, Hacker News commenter, self-described, Oct 26, 2026. DeepSeek V4.1 Flash on a local dual R9700 GPU setup.
Unfavorable
you will see trash coming out of openrouter at 1-5 tokens/second
Comment on: One Month Without AI. elwebmaster, Hacker News commenter, self-described, Sep 26, 2026. DeepSeek V4.1 Flash (deepseek-4.1 flash) on OpenRouter, Qwen Cloud and the DeepSeek API.
Favorable
it's still 100% faster for a very good price, so I won't complain
Comment on: DeepSeek v4.1 Flash. k__, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash on OpenRouter at launch.
Unfavorable
Thinking, even set to low, adds way too much latency to be useful for this task.
Comment on: DeepSeek v4.1 Flash. jamesponddotco, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash as a voice assistant.
Favorable
a Flash model, that is 2x a 2.5x faster then the older Flash version
Comment on: DeepSeek v4.1 Flash. benjiro29, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash (DS v4.1 Flash) at launch.
Favorable
It was fast, cheap, and got a high scoring result
Comment on: DeepSeek v4.1 Flash. raesene9, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash on a personal Kubernetes security benchmark.
Favorable
but also faster and slightly more token efficient
Comment on: DeepSeek v4.1 Flash. XCSme, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash on the aibenchy comparison.
Favorable
it is really fast. What's more?
Comment on: DeepSeek v4.1 Flash. BrucecarlL, Hacker News commenter, self-described, Sep 10, 2026. DeepSeek V4.1 Flash debugging pages through the browser.
Unfavorable
every later model request in the session waits 10–60 s before the first token
deepseek-v4.1-flash: 10-60 s to first token once images are in context. slovx2, GitHub user filing a Command Code issue, self-described, Oct 10, 2026. DeepSeek V4.1 Flash (deepseek/deepseek-v4.1-flash) in the Command Code CLI.
Favorable
At 169 tokens per second, DeepSeek V4.1 Flash 1017 (Max) is notably fast
Artificial Analysis - DeepSeek V4.1 Flash 1017. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint (Max).
Favorable
If I run it all day long it costs $1-2. Its fast.
Comment on: Why isn't the industry freaking out about DeepSeek 4.1 Flash?. gregwebs, Hacker News commenter, self-described, Oct 8, 2026. DeepSeek V4.1 Flash (DeepSeek 4.1 flash) as coding orchestrator and verifier.
Favorable
DeepSeek was faster on triage and slightly faster across the three runs combined
DeepSeek V4.1-Flash-1017 vs. Claude Haiku 5.5: More accurate at a fraction of the price. Jessica Wachtel, writer at The New Stack, self-described, Oct 22, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, via OpenRouter pinned to DeepSeek's servers, default settings, off-peak.
Unfavorable
Speed goes to Sonnet by a mile. A long session took Sonnet 6.8 minutes and DeepSeek 15.4.
DeepSeek vs Claude for coding: V4.1 Flash 1017 against Opus 5.5, Sonnet 5.5 and Haiku 5.5. Bito, AI coding tools company, on its own coding benchmark, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, inside Claude Code.
Favorable
the inference speed is impressive >200t/s
Tested on coding and agentic work - not that impressive. curiousily, Hugging Face user, self-described, Sep 11, 2026. DeepSeek V4.1 Flash via OpenRouter on coding and agentic tests.
Favorable
this model is stupid fast
wow, this thing is a beast - and i've NEVER said that about a deepseek model. aaron-newsome, Hugging Face user running the model locally, self-described, Oct 20, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, on 4x RTX PRO 6000 across dozens of tickets.
Favorable
benefit from its far superior latencies
Comment on: MiniMax M3.1 Flash. switchbak, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash (DS) through OpenRouter and Fireworks at API rates.
Favorable
On Flash 4.1, I constantly get ~180 tok/s and the real work got done fast, too.
Comment on: MiniMax M3.1 Flash. sinuhe69, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash (Flash 4.1) on OpenRouter.
Unfavorable
Even though the tok/s is good, it stills feels slow because of this.
Comment on: MiniMax M3.1 Flash. seabombs, Hacker News commenter, self-described, Oct 20, 2026. DeepSeek V4.1 Flash (DS-4.1-Flash) and the 1017 update.
Favorable
finishing tasks in well under half the time
DeepSeek V4.1 Flash Benchmarks, Cost and Capabilities | Vals AI. Vals AI, public LLM evaluation publisher, self-described, Sep 10, 2026. DeepSeek V4.1 Flash at max reasoning effort via the DeepSeek provider.

Reliability 42%

Favorable
I'm guaranteed it's not being changed on me
Comment on: DeepSeek V4.1 Flash 1017 (Max). lhl, Hacker News commenter, self-described, Oct 19, 2026. DeepSeek V4.1 Flash and DeepSeek V4.1 Flash 1017 on fast local infrastructure.
Unfavorable
Getting a lot of errors from OpenRouter's endpoint
Comment on: DeepSeek-v4.1-Flash-1017. summarity, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, through OpenRouter in GitHub Copilot CLI.
Favorable
I never experience quota issues
Comment on: GPT-6 Sol and Luna. flippingheck, Hacker News commenter, self-described, Sep 23, 2026. DeepSeek V4.1 Flash (DeepSeek 4.1 Flash).
Unfavorable
Why Deepseek 4.1 flash always 429 these days
XYZ on Deepseek 4.1 flash 429 errors. XYZ (@zhangasync), full stack WebGIS developer, X user, self-described, Oct 25, 2026. DeepSeek V4.1 Flash (Deepseek 4.1 flash) on an OpenCode Go subscription.
Favorable
i've been able to run it for basically a full working day during off-peak windows
Yum on more DeepSeek V4.1 Flash 1017 findings. Yum (@yuhasbeentaken), growth engineer who benchmarks AI agents, X user, self-described, Oct 19, 2026. DeepSeek V4.1 Flash, the 1017 checkpoint, in OpenCode free and Go.
Unfavorable
Deepseek servers went unavailable for 5 hours.
Comment on: DeepSeek-v4.1-Flash-1017. npn, Hacker News commenter, self-described, Oct 17, 2026. DeepSeek V4.1 Flash on the DeepSeek API around the 1017 rollout.
Unfavorable
deepseek v4.1 flash rate limits me on prod but never seems to happen on local
Comment on: So you want to use OpenRouter?. mesmertech, Hacker News commenter, self-described, Sep 11, 2026. DeepSeek V4.1 Flash through OpenRouter in production.
Unfavorable
Whenever I select DeepSeek v4.1 Flash it always returns only
DeepSeek v4.1 Flash always returns 400. ioma8, GitHub user filing an OpenCode issue, self-described, Oct 9, 2026. DeepSeek V4.1 Flash through OpenCode Go in opencode cli v2.0.26.
Favorable
With my OpenCode Go sub of $10/month, DeepSeek is basically unlimited.
Why Isn't The Industry Freaking Out About DeepSeek 4.1 Flash?. Jono, author of the Jono's Corner blog, self-described, Oct 7, 2026. DeepSeek V4.1 Flash (DeepSeek 4.1 Flash) on an OpenCode Go plan across a dozen projects.
Unfavorable
I keep getting this error every few hours
OpenCode Go: Error: Provider blocked the response-DeepSeek V4.1 Flash. yabdali, GitHub user filing an OpenCode issue, self-described, Oct 26, 2026. DeepSeek V4.1 Flash on the OpenCode Go plan in OpenCode v2.1.4.
Unfavorable
The reasoning budget is exhausted, content is empty, and the user sees no output at all.
Reasoning loop amplification with DSpark enabled + intrinsic repetition loop in DeepSeek-V4.1-Flash. wwwcxc520, Hugging Face user running the model in production, self-described, Oct 12, 2026. DeepSeek V4.1 Flash in production on 4x RTX PRO 6000 and on the official DeepSeek API.
Favorable
it's cheap, fast, and reliable
Comment on: Sonnet 5.5. jchw, Hacker News commenter, self-described, Sep 28, 2026. DeepSeek V4.1 Flash.