Model family by Mistral AI

Mistral Large

Mistral AI's flagship general-purpose model family.

Where it sits in Mistral AI's lineup

Mistral LargeMistral AI's flagship general-purpose model family.This model
Mistral MediumMistral AI's mid-size model family for agentic and coding work.Mistral Medium 3.5

Made by: Mistral AI
Releases: 1 tracked
Current release: Mistral Large 4

Strengths and weaknesses

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Mistral Large 4

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Mistral Large 4Mistral AIThis page, always on the chart

Among the 40 current releases on this chart, Mistral Large 4's best rank is 24th of 38 on Tool use. Agentic coding is Artificial Analysis's run on Terminal-Bench 4.0, marked on the chart. It ranks lowest on Factuality, 28th of 30. Computer use and Instruction following: not yet published by Mistral AI or any independent evaluator, checked Nov 2, 2026. Code, Math, Science, Hard reasoning, Long context, Multimodal and 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

Mistral Large 4This pageResults on 3 of 12 axes$0.70 / $2.10161 tokens/sFirst token in 1.4 s

Prices and speeds as of Oct 30, 2026.

Mistral Large 4 on each axis3 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. Where it has no run of a model, the maker's own figure or another lab's run stands in, marked with its benchmark.
- Mistral Large 424.7%Artificial Analysis's runBenchmark: Terminal-Bench 4.0, accuracy, Artificial Analysis run in its own agent harness, where the axis reads pass@1 averaged over 3 runs, mini-swe-agent harness. From another source, so it is drawn and marked but not ranked with the others.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.
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
- Mistral Large 4Published only for another harness, setting or build, listed in the tableNot drawn: Artificial Analysis 54.2% on an earlier build. The table below lists it.
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
- Mistral Large 4Published only for another harness, setting or build, listed in the tableNot drawn: Epoch AI 58.6% on an earlier build. The table below lists it.
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
- Mistral Large 4Published only for another harness, setting or build, listed in the tableNot drawn: Epoch AI 88.1% on an earlier build. The table below lists it.
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
- Mistral Large 4Published only for another harness, setting or build, listed in the tableNot drawn: Artificial Analysis 35% on an earlier build. The table below lists it.
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
- Mistral Large 457.8%Artificial Analysis's run of the October 29 build at high effort, its highest run of that build.
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
- Mistral Large 4Not yet published by Mistral AI or any independent evaluator, checked Nov 2, 2026
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
- Mistral Large 4Published only for another harness, setting or build, listed in the tableNot drawn: Artificial Analysis 81.3% on an earlier build. The table below lists it.
The share of MMMU-Pro's college-level questions over charts, diagrams and photos the model answers correctly.
MMMU-Pro, overall accuracy, no tools
- Mistral Large 4Published only for another harness, setting or build, listed in the tableNot drawn: Artificial Analysis 76.4% on an earlier build. The table below lists it.
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
- Mistral Large 426%Artificial Analysis's run of the October 29 build at high effort, its highest run of that build.Hallucination rate 28.2% 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
- Mistral Large 4Published only for another harness, setting or build, listed in the tableNot drawn: SpeechMap.AI 48.5% 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
- Mistral Large 4Not yet published by Mistral AI or any independent evaluator, checked Nov 2, 2026

Beside the benchmarks

Cost, speed, capacity, quality and accuracy for Mistral Large 4, with where each figure sits among the 40 models GILD tracks. These measures stand beside the chart and never feed it.

Cost

$1.61per task

Cost per task. One Intelligence Index task at list price, every token counted. Run at mistral Large 4 Preview.

Cached input: $0.07 per 1M tokens

Speed

116tokens a second

Output speed. How fast it writes once the answer starts. Run at mistral Large 4 Preview.

First token: 1.6 s
Whole answer: 23 s

Capacity

1Mtokens

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

Max output: 262K
Reads: Text, Images

Quality

38.4of 100

Intelligence Index. One composite score across independent evaluations. Run at mistral Large 4 Preview.

GDPval knowledge work: 1,434 Elo
Terminal-Bench 4.0: 26.8%
Epoch Capabilities Index: 150

Hallucination

28.2%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 high effort.

Knowledge accuracy: 26.0%

Where you can get it

3of 19 routes

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

Open weights: Yes
Licence: Mistral Ecosystem Open Weight License

Every route and price

Figures from Artificial Analysis, Mistral AI, OpenRouter, Epoch AI and GILD's route matrix, read Oct 26 to 30, 2026. Each measure's definition and source open under .

Every value with its benchmark, run and source

Mistral Large 4's result on each axis, with the benchmark, the setting and run, who reported it, the source and the date
Axis	Benchmark and setting	Mistral Large 4	Run	Source	Date
Agentic coding	Terminal-Bench 4.0Accuracy, Artificial Analysis run in its own agent harness. 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.Stands in for Terminal-Bench 4.0, pass@1 averaged over 3 runs, mini-swe-agent harness, which has no run of Mistral Large 4 yet.	24.7%	Mistral Large 4 (High)Effort: high. Harness: Artificial Analysis agent harness. Run on the Oct 29, 2026 build.Not drawn: Vals.ai 27.8% on an earlier build, Oct 24, 2026. Source	Artificial AnalysisRun by Artificial Analysis	Oct 30, 2026
Code	SciCodeSubproblem accuracy, pass@1, highest effort run	Published only for another harness, setting or build, listed in the table	Not drawn: Artificial Analysis 54.2% on an earlier build, Oct 27, 2026. Source
Math	FrontierMath Tiers 1-3Accuracy, pass@1, highest effort run	Published only for another harness, setting or build, listed in the table	Not drawn: Epoch AI 58.6% on an earlier build, Oct 12, 2026. Source
Science	GPQA DiamondAccuracy, pass@1, highest effort run	Published only for another harness, setting or build, listed in the table	Not drawn: Epoch AI 88.1% on an earlier build, Oct 12, 2026. Source
Hard reasoning	Humanity's Last ExamAccuracy, text-only questions, no tools	Published only for another harness, setting or build, listed in the table	Not drawn: Artificial Analysis 35% on an earlier build, Oct 27, 2026. Source
Tool use	AutomationBench-AAScore, Artificial Analysis run. Artificial Analysis's run of the October 29 build at high effort, its highest run of that build.	57.8%	Mistral Large 4 (High)Effort: high. Harness: Artificial Analysis AutomationBench harness. Run on the Oct 29, 2026 build.Not drawn: Artificial Analysis 59.9% on an earlier build, Oct 27, 2026. Source	Artificial Analysis	Oct 30, 2026
Computer use	OSWorld 2.1Partial score, full 108-task set, v2.1 release	Not yet published by Mistral AI or any independent evaluator, checked Nov 2, 2026
Long context	AA-LCR v1.1Accuracy, Artificial Analysis run	Published only for another harness, setting or build, listed in the table	Not drawn: Artificial Analysis 81.3% on an earlier build, Oct 27, 2026. Source
Multimodal	MMMU-ProOverall accuracy, no tools	Published only for another harness, setting or build, listed in the table	Not drawn: Artificial Analysis 76.4% on an earlier build, Oct 27, 2026. Source
Factuality	AA-Omniscience AccuracyAccuracy, Artificial Analysis run. Artificial Analysis's run of the October 29 build at high effort, its highest run of that build.	26%Hallucination rate 28.2% in the same run (lower is better)	Mistral Large 4 (High)Effort: high. Harness: none. Run on the Oct 29, 2026 build.Not drawn: Artificial Analysis 23.9% on an earlier build, Oct 28, 2026. Source	Artificial Analysis	Oct 30, 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 48.5% on an earlier build, Oct 27, 2026. Source
Instruction following	MultiChallengeAccuracy, Scale AI run	Not yet published by Mistral AI or any independent evaluator, checked Nov 2, 2026

Price and speed

Mistral Large 4 lists at $0.70 per million input tokens and $2.10 per million output tokens. Mistral AI pricing, Oct 30, 2026.
Mistral Large 4, at high effort, writes 161.3 output tokens per second on Mistral's API, and its first token arrives 1.4 s after the request. Artificial Analysis, Oct 30, 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
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
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
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
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
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%
GPT-6 Luna39.6%
DeepSeek V4.1 Flash39.6%
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.

Releases

Every Mistral Large release we track, newest first.

Mistral Large 4Mistral AI's flagship multimodal mixture-of-experts model, in Public Preview (v26.10) on the Mistral API and Mistral Studio since October 6, 2026, and also on OpenRouter and OpenCode Zen. Mistral has published no GA date. Its open weights followed on October 29, 2026, under the Mistral Ecosystem Open-Weight license, a modified MIT license.CurrentOct 6, 2026

What practitioners report

Strongest on Quality and reasoning, weakest on Developer experience.

Each figure is the share of accounts naming Mistral Large 4 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 Mistral Large 4 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.	58%	11 favorable, 8 not, 18 authors
Tool use and structured outputWhether it calls tools correctly, returns valid structured output, and keeps agent loops on track.	38%	5 favorable, 8 not, 13 authors
Long contextHow it holds up over long documents, large codebases, long sessions, and many images or PDFs.	Needs 5 accounts from 3 authors	1 favorable, 3 not, 4 authors
Developer experienceHow it is to build with: prompting, steering, effort settings, and support across SDKs and providers.	13%	2 favorable, 14 not, 16 authors
CostWhat the work costs: price per task, token use, and what caching and batch save.	27%	3 favorable, 8 not, 11 authors
SpeedResponse time, time to first token, and throughput.	21%	3 favorable, 11 not, 14 authors
ReliabilityWhether requests keep succeeding: overloaded errors, outages, rate limits, and changes that broke what worked.	38%	3 favorable, 5 not, 8 authors

Every model on these seven axes

Share of accounts favorable on each axis for Mistral Large 4 and every other model GILD has accounts for
Axis	Mistral Large 4	Mistral Medium 3.5	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	DeepSeek V4.1 Flash	DeepSeek V4 Pro	Kimi K3.1 Pro	Kimi K3	Kimi K2.7 Code	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	58%11 favorable, 8 not, 18 authors	36%5 favorable, 9 not, 14 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	61%25 favorable, 16 not, 40 authors	Only concerns on record0 favorable, 4 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	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	38%5 favorable, 8 not, 13 authors	Only concerns on record0 favorable, 5 not, 5 authors	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	59%10 favorable, 7 not, 17 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%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	Needs 5 accounts from 3 authors1 favorable, 3 not, 4 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	58%7 favorable, 5 not, 12 authors	No accounts yet	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	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	13%2 favorable, 14 not, 16 authors	Only concerns on record0 favorable, 9 not, 9 authors	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	35%8 favorable, 15 not, 23 authors	No accounts yet	9%1 favorable, 10 not, 11 authors	No accounts yet	Only concerns on record0 favorable, 1 not, 1 author	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	27%3 favorable, 8 not, 11 authors	Only concerns on record0 favorable, 8 not, 8 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	66%25 favorable, 13 not, 37 authors	Only praise on record2 favorable, 0 not, 2 authors	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	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	21%3 favorable, 11 not, 14 authors	Only concerns on record0 favorable, 2 not, 2 authors	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	68%21 favorable, 10 not, 30 authors	Only concerns on record0 favorable, 1 not, 1 author	21%4 favorable, 15 not, 19 authors	Only concerns on record0 favorable, 2 not, 2 authors	No accounts yet	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	38%3 favorable, 5 not, 8 authors	Only concerns on record0 favorable, 4 not, 4 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	42%5 favorable, 7 not, 12 authors	Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors	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	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 67 accounts behind Mistral Large 4's figures

GILD searched Hacker News, GitHub, Reddit, X, Artificial Analysis, OpenRouter, LMArena, Vals AI and the web for accounts that name Mistral Large 4.

Quality and reasoning 58%

Favorable
above average in intelligence
Artificial Analysis - Mistral Large 4. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. Mistral Large 4 (reasoning).
Favorable
it gave me quite a lot of good info during a half an hour chat on C and embedded programming
Mistral Large 4. delis-thumbs-7e, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 through OpenRouter in a web UI, a half-hour chat on C and embedded programming.
Unfavorable
it keeps trying to cheat at the task (trying to remember hallucinations)
Mistral Large 4. TuxSH, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 running as subagents.
Favorable
Definitely the best I've seen from any Mistral model
Mistral Large 4. simonw (Simon Willison), Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 through the llm CLI at reasoning none and high, pelican SVG test.
Favorable
the leap from Mistral Large 3 (their previous best model) to Mistral Large 4 was similar to the Haiku 4.5 to 5.5 jump
Claude Haiku 5.5. simonw (Simon Willison), Hacker News commenter, self-described, Oct 8, 2026. Mistral Large 4 against Mistral Large 3 on the pelican SVG test.
Unfavorable
verbose and also performse worse
Mistral Large 4. XCSme, Hacker News commenter, self-described, Oct 7, 2026. Mistral Large 4 against GPT-6 Luna on the aibenchy comparison.
Favorable
I just runned it on code reviews right now and it was able to catch some thread safety issue than DeepSeek-4.1 didn't
Mistral Large 4. adev_, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 on code reviews.
Unfavorable
Mistral Large 4 is not very good. It can sometimes solve a game with ~40 guesses whereas the top models like Gemini 3.8 Flash or Grok 4.7 can one shot most puzzles
Mistral Large 4. pampas, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 on the Redactle trivia benchmark.
Favorable
goes from 58% to 74% correct. Definitely a generational shift
Mistral Large 4. chriddyp (Chris Parmer), co-founder of Plotly, Oct 6, 2026. Mistral Large 4 on Plotly's data analytics benchmark.
Unfavorable
it performed pretty poorly (on par with Kimi K3)
Mistral Large 4. dom96, Hacker News commenter, self-described, Oct 21, 2026. Mistral Large 4 on the killswitch-lang adversarial programming language benchmark.
Unfavorable
it seemed to make all kinds of obvious errors of the kind that I haven't seen from opus, sol, deepseek flash etc in over a year
Timestral: Mistral's time-series foundation models. ritzaco, Hacker News commenter, self-described, Oct 19, 2026. Mistral Large 4 (Le Chonk) for general coding work.
Unfavorable
it spits out Chinese characters just like the chinese models when chatting with it in German
Mistral Large 4. nik736, Hacker News commenter, self-described, Oct 21, 2026. Mistral Large 4 through the API, chats held with German prompts.
Favorable
small mistakes but reached the solution
Samsung TV privacy issues. nekitamo, Hacker News commenter, self-described, Oct 24, 2026. Mistral Large 4 (listed as Mistral Large 4.0) on a crackme reverse engineering benchmark through Ghidrasql.
Unfavorable
It's not the best, the cheapest, or the fastest.
Pawel Huryn on X: Mistral Large 4 bug-fix test. Paweł Huryn, AI PM and author of the Product Compass newsletter, self-described, Oct 7, 2026. Mistral Large 4 at high reasoning on two real repos with 105 replanted bugs.
Favorable
it knows more, good job
H. Gskrd on X: testing Mistral Large 4. h_gskrd, X user, self-described, Oct 14, 2026. Mistral Large 4, which the author calls GA and therefore usable with zero data retention; Mistral's model card lists it as a public preview, and Mistral's Commercial Terms (section 4.3) say zero data retention opt-outs do not apply to preview models.
Favorable
Mistral Large 4 joins my leaderboard right beside those "almost frontier" models!
Povilas Korop on X: Mistral Large 4 on the AI Coding Daily leaderboard. Povilas Korop, AI coding and Laravel educator, self-described, Oct 15, 2026. Mistral Large 4 on the AI Coding Daily coding leaderboard.
Favorable
This makes it the most intelligent model from outside the US and China
Mistral has released Mistral Large 4, making France home to the most intelligent model outside the US and China. Artificial Analysis, independent benchmark publisher, self-described, Oct 6, 2026. Mistral Large 4 Preview on the Artificial Analysis Intelligence Index, Cyber Index and GDP.pdf.
Favorable
Mistral Large 4 passes 32 of 43 (74%, a C), and it improved in every category but one
Mistral Large 4 Benchmark Results for Data Analysis. Plotly, company blog by Chris Parmer, co-founder and CPO, Oct 6, 2026. Mistral Large 4 in Plotly Studio Embedded on a 43-question agentic data analytics exam over a 20M-row warehouse.
Unfavorable
SWE-bench Multimodal 33.13 % ± 2.15 20 / 20
Mistral Large 4 Benchmarks, Cost and Capabilities | Vals AI. Vals AI, independent benchmark publisher, self-described, Oct 26, 2026. Mistral Large 4 at high reasoning effort through the Mistral API on Vals AI benchmarks.
Favorable
Mistral Large 4 just tied #1 on 669 clinical decisions
Maziyar Panahi on X: Mistral Large 4 on clinical decisions. Maziyar Panahi, on-device clinical AI builder (X bio), self-described, Oct 6, 2026. Mistral Large 4 on his 669-decision clinical benchmark on launch day.
Unfavorable
On my chess bench, where general capability increase usually leads to higher elos for new models, it performed abysmal and still near mistral-large-2-2411 (nov 2024) levels.
Europe rejoins the fight with Chonky! Mistral Large 4 Released, Open weights end of month, who's ready?. dubesor86, Reddit commenter who runs a chess benchmark, self-described, Oct 7, 2026. Mistral Large 4 on code analysis, image classification and his chess benchmark.
Favorable
None of the 14 fails is a planning defect
docs: add the mistral-large-4 test results to the mistral backlog. Rabbid76, GitHub user and roomle-hi-example contributor, self-described, Oct 21, 2026. mistral-large-4 on 52 of 75 MCP planning tests against the roomle UI.

Tool use and structured output 38%

Unfavorable
I've had seen subagents fail with "hmm, hmm - hmm, hmm (...)" multiple times
Mistral Large 4. TuxSH, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 running as subagents.
Unfavorable
Seems to have problems with tool calling? When doing heavy research it makes a 'json db' but then has trouble working with jq.
Mistral Large 4. postepowanieadm, Hacker News commenter, self-described, Oct 7, 2026. Mistral Large 4 on a heavy research task with tools.
Unfavorable
Mistral Large 4 itself returns `reasoning_content` with its tool calls, so a Mistral-only tool loop in OpenCode fails at round 2.
Mistral and InferencePort reject reasoning that agent clients send back. voodoohop, GitHub user, self-described, Oct 26, 2026. Mistral Large 4 on the Mistral API in an OpenCode tool loop.
Favorable
tool loop: 6 of 6 runs called the tool correctly, with and without thinking.
feat: switch Ultra tier to Mistral Large 4. Movm, GitHub user and Gruenerator contributor, self-described, Oct 6, 2026. Mistral Large 4 (mistral-large-4) as Gruenerator's Ultra tier, tested live against the Mistral API on launch day.
Unfavorable
Mistral's own models reject the history. A session that has used GLM fails on every request after a switch to `mistral/mistral-large-4-0`
Mistral-family tool-call ID normalization collapses GLM call IDs to "chatcmplt" (400 "Duplicate tool call id" on Mistral Large). infinitr0us, GitHub user, self-described, Oct 23, 2026. Mistral Large 4 (mistral-large-4-0) in OpenCode after a GLM session, through a gateway and directly on api.mistral.ai.
Favorable
The Mistral API accepts an assistant message that carries both `thinking` content and `tool_calls` (verified on mistral-small-2603, mistral-medium-2604 and mistral-large-4-0)
langchain-mistralai: reasoning (thinking) blocks are dropped from assistant messages that carry tool calls. fchoron-pixel, GitHub user, self-described, Oct 19, 2026. Mistral Large 4 (mistral-large-4-0) through the Mistral API with thinking and tool calls in one assistant message.
Favorable
Real OmO CLI continuation, high-thinking answer separation, and a successful tool roundtrip.
fix: support Mistral Large 4 thinking, continuations, and tool replay. audreyt, GitHub user, self-described, Oct 6, 2026. Mistral Large 4 in a real OmO CLI tool loop with high thinking.
Favorable
the model works it out on its own through a series of tool calls: exploring the schema, writing queries, checking the results and writing up the answer
Mistral Large 4 Benchmark Results for Data Analysis. Plotly, company blog by Chris Parmer, co-founder and CPO, Oct 6, 2026. Mistral Large 4 in Plotly Studio Embedded on a 43-question agentic data analytics exam over a 20M-row warehouse.
Unfavorable
Log excerpt from a real session (model: `mistral-large-4:cloud` via local Ollama daemon): ``` 11:35:10.721 WARN Malformed tool call JSON from provider
Malformed tool-call arguments replayed raw on next in-run request; strict providers (Ollama) 400 and kill the run. thylong, GitHub user, self-described, Oct 10, 2026. mistral-large-4:cloud through a local Ollama daemon in Crush.
Unfavorable
`ollama-cloud/mistral-large-4` does this: each call has its own `id`, but `function.index` is `0` for all of them.
[Bug]: ollama-native fails turns where a model returns several tool calls at the same function index. MarcTCruz, GitHub user, self-described, Oct 9, 2026. ollama-cloud/mistral-large-4 from Codex CLI through opencodex.
Favorable
Verified live Ollama Cloud mistral-large-4:cloud tool-calling (3.7s live test).
test: 5-pass self-proof loop, Ollama live testing & UX friction remediation. poferraz, GitHub user, self-described, Oct 16, 2026. mistral-large-4:cloud on Ollama Cloud as the agent model in allowagent-mcp.
Unfavorable
Mistral Large 4 called a nonexistent bash 226 times in 209 replies ($4.33, 35% of its spend).
fix(agent): a 64k output budget, unknown-tool feedback, honest failed summaries, ordered headless JSON. gold-silver-copper, GitHub user and rig contributor, self-described, Oct 24, 2026. Mistral Large 4 as the agent model in the rig CLI through OpenRouter.
Unfavorable
the model returned "label": ["cat", "dog"] and all five JSON parsers crashed
Add Mistral Large 4 under the full benchmark protocol. SkalskiP (Piotr Skalski), Roboflow vlm-exam maintainer, self-described, Oct 7, 2026. Mistral Large 4 through OpenRouter on Roboflow's vlm-exam, 6 vision tasks at low and high effort, 3 repeats.

Long context Needs 5 accounts from 3 authors

Unfavorable
starts to forget instructions after awhile it seems
Mistral Large 4. delis-thumbs-7e, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 through OpenRouter in a web UI, a half-hour chat on C and embedded programming.
Favorable
Strong document and image reasoning: Mistral Large 4 Preview scores 19% on GDP.pdf
Mistral has released Mistral Large 4, making France home to the most intelligent model outside the US and China. Artificial Analysis, independent benchmark publisher, self-described, Oct 6, 2026. Mistral Large 4 Preview on the Artificial Analysis Intelligence Index, Cyber Index and GDP.pdf.
Unfavorable
Mistral Large 4 gives OCR error when using document_url content
[BUG MODEL]: Mistral Large 4 gives OCR error when using document_url content. smathot, GitHub user, self-described, Oct 8, 2026. mistral-large-4 through the Mistral Python SDK with a PDF passed as document_url.
Favorable
found every time) Lane Prompt tokens Time to first delta Ultra 97,493 17.3 s / 22.2 s Ultra 191,011 45.7 s Ultra 241,041 (56k cached) 35.3 s Ultra 861,094 111.2 s
feat(chat): give Ultra and Panda their measured ~1M context window. Movm, GitHub user and Gruenerator contributor, self-described, Oct 22, 2026. Mistral Large 4 (mistral-large-4) as Gruenerator's Ultra lane with prompts from 97k to 861k tokens on production keys.
Unfavorable
11 end on the known Mistral limitation of images in tool results: get-plan-images overflows the context
docs: add the mistral-large-4 test results to the mistral backlog. Rabbid76, GitHub user and roomle-hi-example contributor, self-described, Oct 21, 2026. mistral-large-4 on 52 of 75 MCP planning tests against the roomle UI.

Developer experience 13%

Unfavorable
it generated 200M tokens, which is very verbose in comparison to the median of 87M
Artificial Analysis - Mistral Large 4. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. Mistral Large 4 (reasoning).
Unfavorable
Very verbose and starts to forget instructions
Mistral Large 4. delis-thumbs-7e, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 through OpenRouter in a web UI, a half-hour chat on C and embedded programming.
Unfavorable
Surprisingly it only supports reasoning "none" or reasoning "high". That setting didn't seem to make any real difference
Mistral Large 4. simonw (Simon Willison), Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 through the llm CLI at reasoning none and high, pelican SVG test.
Unfavorable
reasoning effort parameter doesn't seem to be there and output is sort of broken because it reasons directly in the output tokens
Mistral Large 4. XCSme, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 through the API on launch day.
Unfavorable
The only ones available on my end are Medium 3.5 and GLM-5.3 despite having a Pro subscription.
Timestral: Mistral's time-series foundation models. pims, Hacker News commenter, self-described, Oct 19, 2026. Mistral Large 4 ("Mistral 4") access in Mistral Vibe on a Pro subscription.
Unfavorable
multiple different sessions are convinced that they're in a simulation/fictional environment right off the bat
azziee on X: coding with Mistral Large 4. azzieewas, X user, self-described, Oct 21, 2026. Mistral Large 4 Preview used exclusively for coding.
Unfavorable
please guys just make it an insufferable polytechnician instead of this suckup vizier
H. Gskrd on X: testing Mistral Large 4. h_gskrd, X user, self-described, Oct 14, 2026. Mistral Large 4, which the author calls GA and therefore usable with zero data retention; Mistral's model card lists it as a public preview, and Mistral's Commercial Terms (section 4.3) say zero data retention opt-outs do not apply to preview models.
Unfavorable
it launched oct 6, it's oct 22 and there's no checkpoint to download
Duggal on X: no Mistral Large 4 checkpoint yet. Duggal, founder of darkfunnelhq, self-described, Oct 22, 2026. Mistral Large 4 open weights and list price on Oct 22.
Favorable
improvements in their API, which now accepts 100 images per request, up from 8 for previous Mistral models
Mistral has released Mistral Large 4, making France home to the most intelligent model outside the US and China. Artificial Analysis, independent benchmark publisher, self-described, Oct 6, 2026. Mistral Large 4 Preview on the Artificial Analysis Intelligence Index, Cyber Index and GDP.pdf.
Unfavorable
This blocks Codex clients that send developer-role instructions.
Cloud: mistral-large-4 rejects developer-role messages with HTTP 400. thericardoli, GitHub user, self-described, Oct 15, 2026. mistral-large-4 on Ollama Cloud through the Responses endpoint, called from Codex.
Unfavorable
every call crashes with a TypeError, whether streaming is on or off
[BUG] MistralModel crashes on reasoning models (Mistral Large 4): content returned as list of ThinkChunk/TextChunk. margotmercier, GitHub user, self-described, Oct 9, 2026. Mistral Large 4 through Strands MistralModel with reasoning on.
Unfavorable
Mistral provider shows a list of models but that list is not complete. For example, Mistral Large 4 and GLM 5.3 are missing.
Mistral provider doesn't work with certain Mistral models. tomsep, GitHub user, self-described, Oct 16, 2026. mistral-large-4 through the twinny VS Code extension's Mistral provider.
Favorable
the issue is fixed for me with every thinking variants
Mistral Large 4 not working: type validation failed (invalid_union). Ronan-Le-Pivaingt-Dotscreen, GitHub user, self-described, Oct 9, 2026. opencode/mistral-large-4 with every thinking variant in OpenCode v2.0.26.
Unfavorable
Large 4 accepts reasoning_effort: high and none , but rejects prompt_mode: reasoning .
fix: support Mistral Large 4 thinking, continuations, and tool replay. audreyt, GitHub user, self-described, Oct 6, 2026. Mistral Large 4 in a real OmO CLI tool loop with high thinking.
Favorable
I updated my vibe config to point to Mistral 4 and it charges my subscription capacity, not my API allowance.
Timestral: Mistral's time-series foundation models. data-ottawa, Hacker News commenter, self-described, Oct 19, 2026. Mistral Large 4 ("Mistral 4") in Mistral Vibe through a config change on a subscription.
Unfavorable
Mistral Large 4 (Le Chonk) higher reasoning effort scored worse and cost 8.3x more
SkalskiP on X: Mistral Large 4 reasoning effort. SkalskiP, open-source lead at Roboflow (X bio), self-described, Oct 12, 2026. Mistral Large 4 (Le Chonk) at low and high reasoning effort on his vision leaderboards.
Unfavorable
Large 4 writes long structured answers: about 2–4K tokens even on tiny inputs
fix(providers): stream Mistral responses with an idle timeout instead of a 5-minute total deadline. akoita, GitHub user and draft-loop maintainer, self-described, Oct 8, 2026. Mistral Large 4 profile generation through the Mistral API without streaming.
Unfavorable
Large 4 reasons by default, puts the reasoning inside content as parts, and reports 0 reasoning tokens in usage.
Roster v0.29: mistral-large-4-0 on the minimum-thinking channel (reasoning_effort none). srinivasgumdelli, GitHub user and seismograph contributor, self-described, Oct 10, 2026. Mistral Large 4 (mistral-large-4-0) on the Mistral API with default reasoning in the seismograph 87-call battery.

Cost 27%

Favorable
well priced when comparing to other models of similar price
Artificial Analysis - Mistral Large 4. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. Mistral Large 4 (reasoning).
Favorable
The price is really good with ML4.
Mistral Large 4. pimeys, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 (ML4) pricing for summarization and content generation.
Unfavorable
Xiaomi prices are still much better than the Mistral introductory offer for Le Chonk
Mistral Large 4. phillc73, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 (Le Chonk) introductory API pricing against Xiaomi MiMo-2.6.
Favorable
It's 10x cheaper than Mistral Medium 3.5 from April
Mistral Large 4. chriddyp (Chris Parmer), co-founder of Plotly, Oct 6, 2026. Mistral Large 4 on Plotly's data analytics benchmark.
Unfavorable
more expensive than both of those
Mistral Large 4. dom96, Hacker News commenter, self-described, Oct 7, 2026. Mistral Large 4 on the killswitch-lang programming language benchmark.
Unfavorable
The cost is about 10x of Deepseek though comparing on OpenRouter
Mistral Large 4. Flux159, Hacker News commenter, self-described, Oct 21, 2026. Mistral Large 4 pricing on OpenRouter against DeepSeek 4.1.
Unfavorable
Mistral Large 4 seems a much more expensive but overall weaker model across the board
Mistral Large 4. Systemerror7A69, Hacker News commenter, self-described, Oct 21, 2026. Mistral Large 4 against DeepSeek V4.1 Flash 1017 on price and benchmarks.
Unfavorable
A proprietary model with performance comparable to GPT-6 Luna and Deepseek 4.1 Flash, but at a higher price than either
Mistral Large 4. wyrdcurt, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 against GPT-6 Luna and DeepSeek 4.1 Flash on price and performance.
Unfavorable
This chonky model being expensive, mistral's subscription quota will run out ridiculously fast
Mistral Large 4. sbinnee, Hacker News commenter, self-described, Oct 7, 2026. Mistral Large 4 on a Mistral subscription.
Unfavorable
Been using the mistral-large-4 now since it came out for a few things, and while it's not price competitive
Voxtral 3. barrell, Hacker News commenter, self-described, Oct 20, 2026. mistral-large-4 since launch, replacing browser chat use.
Unfavorable
if you costed it during the launch weeks, your number is half the real one
OTHMANE M on X: mistral-large-4 price doubled. OTHMANEM9, X user who runs a price sync, self-described, Oct 25, 2026. mistral-large-4 API list price after the launch discount.
Unfavorable
Over 4x the Cost per Task of similar-intelligence open weights models
Mistral has released Mistral Large 4, making France home to the most intelligent model outside the US and China. Artificial Analysis, independent benchmark publisher, self-described, Oct 6, 2026. Mistral Large 4 Preview on the Artificial Analysis Intelligence Index, Cyber Index and GDP.pdf.
Favorable
This is very cost efficient and similar to models like OpenAI GPT-6.1 Sol ($2.61) and Qwen 3.8 27B ($1.71)
Mistral Large 4 Benchmark Results for Data Analysis. Plotly, company blog by Chris Parmer, co-founder and CPO, Oct 6, 2026. Mistral Large 4 in Plotly Studio Embedded on a 43-question agentic data analytics exam over a 20M-row warehouse.
Unfavorable
However, it uses much more context on long agentic tasks, bringing its Vals Index cost to $13.78 per test.
Vals AI on X: Mistral Large 4 cost on the Vals Index. Vals AI, independent benchmark publisher, self-described, Oct 6, 2026. Mistral Large 4 on the Vals Index.

Speed 21%

Favorable
At 113 tokens per second, Mistral Large 4 Preview is faster than average
Artificial Analysis - Mistral Large 4. Artificial Analysis, independent benchmark publisher, self-described, Oct 26, 2026. Mistral Large 4 (reasoning).
Favorable
It is very fast via openrouter (significantly better than Kimi K3) on webui
Mistral Large 4. delis-thumbs-7e, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 through OpenRouter in a web UI, a half-hour chat on C and embedded programming.
Unfavorable
GLM 5.3 (which Mistral also serves at 3x the speed!) wipes the floor with it
Mistral Large 4. TuxSH, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 running as subagents.
Unfavorable
compared to GPT-6 luna it is much slower
Mistral Large 4. XCSme, Hacker News commenter, self-described, Oct 7, 2026. Mistral Large 4 against GPT-6 Luna on the aibenchy comparison.
Unfavorable
it is much slower than Sonnet 5.5
Mistral Large 4. dom96, Hacker News commenter, self-described, Oct 7, 2026. Mistral Large 4 on the killswitch-lang programming language benchmark.
Unfavorable
some days it's so slow I interrupt it because I thought it was hanging
Timestral: Mistral's time-series foundation models. sspiff, Hacker News commenter, self-described, Oct 19, 2026. Mistral Large 4 through Mistral's service over two weeks.
Unfavorable
Just tested Mistral 4 too in Vibe and it's slow. It works well, but is way too slow.
Timestral: Mistral's time-series foundation models. maelito, Hacker News commenter, self-described, Oct 19, 2026. Mistral Large 4 ("Mistral 4") in Mistral Vibe.
Unfavorable
It's slow as hell, and so is their GLM offering.
Timestral: Mistral's time-series foundation models. nozzlegear, Hacker News commenter, self-described, Oct 19, 2026. Mistral Large 4 ("Mistral 4") from OMP with a Vibe subscription.
Favorable
Like basically instant responses that seem faster and better in quality than ChatGPT on instant mode
Mistral Large 4. bluerooibos, Hacker News commenter, self-described, Oct 6, 2026. Mistral Large 4 tried on launch day.
Unfavorable
it is not a good fit for DwarfStar because of the very large number of active parameters, not just because of memory: would be too slow anyway
antirez on X: Mistral Large 4 and DwarfStar. antirez, programmer (X bio), self-described, Oct 21, 2026. Mistral Large 4 considered for local inference in DwarfStar.
Favorable
It's also fast: it answers a typical question in about 22 seconds.
Mistral Large 4 Benchmark Results for Data Analysis. Plotly, company blog by Chris Parmer, co-founder and CPO, Oct 6, 2026. Mistral Large 4 in Plotly Studio Embedded on a 43-question agentic data analytics exam over a 20M-row warehouse.
Unfavorable
One profile part therefore took more than 5 minutes, timed out, was retried, and failed again.
fix(providers): stream Mistral responses with an idle timeout instead of a 5-minute total deadline. akoita, GitHub user and draft-loop maintainer, self-described, Oct 8, 2026. Mistral Large 4 profile generation through the Mistral API without streaming.
Unfavorable
our first-token idle deadline (20 s) fires while Mistral silently reads the prompt
feat(chat): give Ultra and Panda their measured ~1M context window. Movm, GitHub user and Gruenerator contributor, self-described, Oct 22, 2026. Mistral Large 4 (mistral-large-4) as Gruenerator's Ultra lane with prompts from 97k to 861k tokens on production keys.
Unfavorable
because the model is too slow: about 3 minutes per run, the chat a median of 80 s and a mean of 153 s
docs: add the mistral-large-4 test results to the mistral backlog. Rabbid76, GitHub user and roomle-hi-example contributor, self-described, Oct 21, 2026. mistral-large-4 on 52 of 75 MCP planning tests against the roomle UI.
Unfavorable
High-effort detection averages 161 s and 12k output tokens per image.
Add Mistral Large 4 under the full benchmark protocol. SkalskiP (Piotr Skalski), Roboflow vlm-exam maintainer, self-described, Oct 7, 2026. Mistral Large 4 through OpenRouter on Roboflow's vlm-exam, 6 vision tasks at low and high effort, 3 repeats.

Reliability 38%

Unfavorable
I think they're shifting hosting capacity around, or otherwise messing with the model
Timestral: Mistral's time-series foundation models. sspiff, Hacker News commenter, self-described, Oct 19, 2026. Mistral Large 4 through Mistral's service over two weeks.
Unfavorable
On launch day 3 of 4 image requests returned 503, so images keep going to VISION_MODEL .
feat: switch Ultra tier to Mistral Large 4. Movm, GitHub user and Gruenerator contributor, self-described, Oct 6, 2026. Mistral Large 4 (mistral-large-4) as Gruenerator's Ultra tier, tested live against the Mistral API on launch day.
Unfavorable
a Mistral Large 4 profile run took 22 minutes, then saved a draft with **0 facts** and one issue: "The provider returned an invalid extraction response.
fix(profile): name the provider failure reason, and retry a Mistral part whose reply arrives broken. akoita, GitHub user and draft-loop maintainer, self-described, Oct 8, 2026. Mistral Large 4 profile extraction runs in draft-loop through the Mistral API.
Unfavorable
During a Mistral API rate-limiting period (HTTP 429 responses), the CLI calls the identity endpoint every 2-4 s
bug: identity endpoint retried in a tight loop during Mistral 429s + silent CLI crash (CancelledError). Simon-LM, GitHub user, self-described, Oct 11, 2026. Mistral Large 4 (large-4) and GLM-5.3 in long-lived Mistral Vibe 2.26 sessions.
Unfavorable
other/2026-10-06-mistral-mistral-large-4-rep-3-retry-1 TRANSPORT_ERROR (TRANSPORT)
Runs: mistral-large-4. Microck, GitHub user and keygen-bench maintainer, self-described, Oct 6, 2026. mistral-large-4 through the Mistral route on keygen-bench, three ranked attempts on launch day.
Favorable
mistral-large-4-0@mistral answered 87/87 with plain-string content on reasoning_effort: none
Roster: mistral-large-4-0 and inkling confirmed live, drop availability=pending. srinivasgumdelli, GitHub user and seismograph contributor, self-described, Oct 10, 2026. Mistral Large 4 (mistral-large-4-0) on the Mistral API at reasoning_effort none in the seismograph 87-call battery.
Favorable
All 13 run-quality checks and sensebench verify passed, with no transport errors or truncated responses.
Submit OpenRouter sweep: 13 runs across 9 models (p001). emilkayumov, GitHub user and sensebench contributor, self-described, Oct 13, 2026. Mistral Large 4 at reasoning none through OpenRouter in a 13-run sensebench sweep.
Favorable
6 tasks x 2 efforts x 3 repeats, 36 result files, zero failed samples.
Add Mistral Large 4 under the full benchmark protocol. SkalskiP (Piotr Skalski), Roboflow vlm-exam maintainer, self-described, Oct 7, 2026. Mistral Large 4 through OpenRouter on Roboflow's vlm-exam, 6 vision tasks at low and high effort, 3 repeats.