Cost
$0.068per task
Cost per task. One Intelligence Index task at list price, every token counted. Run at max effort.
GPT Luna model by OpenAI
GPT-6 Luna
OpenAI's most efficient GPT-6 model, for focused, high-volume tasks. API model gpt-6-luna, with a 1,050,000-token context on the OpenAI API; Microsoft Foundry caps input at 922,000 tokens.
Where it sits in OpenAI's lineup
- GPT TerraOpenAI's fast, efficient GPT tier for visual reasoning, computer use, and writing.GPT-6.1 Terra
- GPT SolOpenAI's GPT model tier for demanding everyday and agentic work.GPT-6.1 Sol
- GPT LunaOpenAI's most efficient GPT model tier for focused, high-volume tasks.This model
- GPT AstraOpenAI's highest-capability GPT model tier.GPT-6 Astra
- GPT-5OpenAI's GPT-5 frontier models, released before the Sol, Terra and Luna tiers.GPT-5.5
- gpt-ossOpenAI's open-weight GPT models under the Apache 2.0 license.gpt-oss-120b
- GPT CodexOpenAI's GPT models tuned for agentic coding in Codex.GPT-5.3-Codex
- Stack role
- Foundation Model / Reasoning Engine
- Offered in
- 6 products
- Made by
- OpenAI
- Family
- GPT Luna, version 6
- Released
- Sep 22, 2026 Current
- In its family
- The newest of 2 GPT Luna releases.
Strengths and weaknesses
Compare with other models Up to 3 beside GPT-6 Luna
- GPT-6 Luna
Suggested comparisons
The models a buyer weighs against GPT-6 Luna, picked on list price and on Artificial Analysis's Intelligence Index of overall quality. Add one to draw it beside GPT-6 Luna.
GPT-6 Luna
Among the 40 current releases on this chart, GPT-6 Luna ranks 9th of 38 on Long context. On Computer use, Anthropic's runs on OSWorld 2.1 put GPT-6 Luna 38.3 points under Claude Opus 5.5, 35 points under Claude Sonnet 5.5 and 23.5 points under Claude Haiku 5.5. It ranks lowest on Hard reasoning, 33rd of 38 and Science, 20th of 24. Instruction following: not yet published by OpenAI or any independent evaluator, checked Nov 2, 2026.
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
- GPT-6 LunaThis pageResults on 11 of 12 axes$0.10 / $0.50For prompts under 272K tokens; $0.20 / $0.75 at 272K or more, as OpenRouter lists it.124 tokens/sFirst token in 77 s
Prices and speeds read between Oct 26, 2026 and Oct 28, 2026.
GPT-6 Luna on each axis11 of 12 with a result
The share of Terminal-Bench 4.0's long terminal tasks the model finishes on its own: services, kernels, proofs and data pipelines, graded on the final artifact with no partial credit.
Terminal-Bench 4.0, pass@1 averaged over 3 runs, mini-swe-agent harness
- GPT-6 Luna13.6%
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
- GPT-6 Luna54.6%
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
- GPT-6 Luna78.9%
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
- GPT-6 Luna90.5%
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
- GPT-6 Luna38.5%
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
- GPT-6 Luna53.2%
How much of each OSWorld 2.1 workflow the model completes by operating a real Ubuntu desktop through screenshots, mouse and keyboard: all 108 long tasks across apps, files, mail and browsers, on the September 2026 v2.1 release of the task files. A point is the mean share of each task's weighted checks the model passes (the partial score, as a percentage). The OSWorld team's own runs come first. Where the team has not run a release, the point is another lab's run or the maker's own published figure, marked with who ran it and the benchmark it ran: OSWorld 2.1 in the maker's harness, OSWorld 2.0's offline set or the earlier OSWorld-Verified. Those points are drawn so the release has a figure, and they are not ranked against the team's runs.
OSWorld 2.1, partial score, full 108-task set, v2.1 release. Where it has no run of a model, the maker's own figure or another lab's run stands in, marked with its benchmark.
- GPT-6 Luna48.9%Anthropic's runBenchmark: OSWorld 2.1, partial score, offline 82-task subset, v2.1 release, where the axis reads full 108-task set. From another source, so it is drawn and marked but not ranked with the others.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.
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
- GPT-6 Luna83.3%
The share of MMMU-Pro's college-level questions over charts, diagrams and photos the model answers correctly.
MMMU-Pro, overall accuracy, no tools
- GPT-6 Luna79.7%
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
- GPT-6 Luna39.6%Hallucination rate 79.3% 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
- GPT-6 Luna63.4%SpeechMap tested it through OpenRouter at medium reasoning and counted 27.4% of requests denied and 9.1% evaded.0.1% of requests failed at the provider and count against 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
- GPT-6 LunaNot yet published by OpenAI or any independent evaluator, checked Nov 2, 2026
Cost, speed, capacity, quality and accuracy for GPT-6 Luna, with where each figure sits among the 40 models GILD tracks. These measures stand beside the chart and never feed it.
Speed
127tokens a second
Output speed. How fast it writes once the answer starts. Run at max effort.
- First token
- 94 s
- Whole answer
- 98 s
Capacity
1.05Mtokens
Context window. The most one request can hold, as the maker states it.
- Max output
- 128K
- Reads
- Text, Images
Quality
38.1of 100
Intelligence Index. One composite score across independent evaluations. Run at max effort.
- GDPval knowledge work
- 1,445 Elo
- Terminal-Bench 4.0
- 12.6%
- Epoch Capabilities Index
- 157
Hallucination
79.3%wrong when it should say “I don’t know”
Hallucination rate. Of the questions it can't get right, how often it answers anyway. Lower is better. Run at max effort.
- Knowledge accuracy
- 39.6%
Where you can get it
10of 19 routes
Routes offering it. Of the clouds, gateways and coding products GILD tracks, those offering it today.
- Open weights
- No
- Licence
- OpenAI Services Agreement
Figures from Artificial Analysis, OpenAI, Epoch AI and GILD's route matrix, read Oct 26 to Nov 1, 2026. Each measure's definition and source open under . Not yet published for GPT-6 Luna: Cached input.
Every value with its benchmark, run and source
| Axis | Benchmark and setting | GPT-6 Luna | Run | Source | Date |
|---|---|---|---|---|---|
| Agentic coding | Terminal-Bench 4.0Pass@1 averaged over 3 runs, mini-swe-agent harness | 13.6% | GPT-6 LunaEffort: not published. Harness: mini-swe-agent. | Vals.ai | Oct 24, 2026 |
| Code | SciCodeSubproblem accuracy, pass@1, highest effort run | 54.6% | GPT-6 Luna (Max)Effort: max. Harness: none. | Artificial Analysis | Oct 28, 2026 |
| Math | FrontierMath Tiers 1-3Accuracy, pass@1, highest effort run | 78.9% | Effort: max. Harness: none. | Epoch AI | Sep 22, 2026 |
| Science | GPQA DiamondAccuracy, pass@1, highest effort run | 90.5% | Effort: max. Harness: none. | Epoch AI | Sep 22, 2026 |
| Hard reasoning | Humanity's Last ExamAccuracy, text-only questions, no tools | 38.5% | GPT-6 Luna (Max)Effort: max. Harness: none. | Artificial Analysis | Oct 28, 2026 |
| Tool use | AutomationBench-AAScore, Artificial Analysis run | 53.2% | GPT-6 Luna (Max)Effort: max. Harness: Artificial Analysis AutomationBench harness. | Artificial Analysis | Oct 28, 2026 |
| Computer use | OSWorld 2.1Partial score, offline 82-task subset, v2.1 release. 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.Stands in for OSWorld 2.1, partial score, full 108-task set, v2.1 release, which has no run of GPT-6 Luna yet. | 48.9% | GPT-6 LunaEffort: max. Harness: Anthropic computer-use harness.Not drawn: OpenAI 52.7% in its maker's own harness, Sep 22, 2026. SourceNot drawn: OpenAI 55.5% in its maker's own harness, Oct 26, 2026. Source | AnthropicRun by Anthropic | Oct 7, 2026 |
| Long context | AA-LCR v1.1Accuracy, Artificial Analysis run | 83.3% | GPT-6 Luna (Max)Effort: max. Harness: none. | Artificial Analysis | Oct 28, 2026 |
| Multimodal | MMMU-ProOverall accuracy, no tools | 79.7% | GPT-6 Luna (Max)Effort: max. Harness: none. | Artificial Analysis | Oct 28, 2026 |
| Factuality | AA-Omniscience AccuracyAccuracy, Artificial Analysis run | 39.6%Hallucination rate 79.3% in the same run (lower is better) | GPT-6 Luna (Max)Effort: max. Harness: none. | Artificial Analysis | Oct 28, 2026 |
| Answers in full | SpeechMap% Complete over 2,120 contested requests, reasoning-on entry. SpeechMap tested it through OpenRouter at medium reasoning and counted 27.4% of requests denied and 9.1% evaded. | 63.4%0.1% of requests failed at the provider and count against it | Effort: medium. Harness: none. | SpeechMap.AI | Oct 27, 2026 |
| Instruction following | MultiChallengeAccuracy, Scale AI run | Not yet published by OpenAI or any independent evaluator, checked Nov 2, 2026 |
Price and speed
- GPT-6 Luna lists at $0.10 per million input tokens and $0.50 per million output tokens. For prompts under 272K tokens; $0.20 / $0.75 at 272K or more, as OpenRouter lists it. OpenAI pricing, Oct 26, 2026.
- GPT-6 Luna, at max effort, writes 124.3 output tokens per second on OpenAI's API, and its first token arrives 77 s after the request. Artificial Analysis, Oct 28, 2026.
- Fastest on the chart is the shortest time to a 500-token answer: time to first token plus 500 tokens at the median output speed.
Every model with a result, axis by axis
57 models. A maker's own figure and another lab's run are marked with their benchmark; a missing result reads "Not yet published", never zero.
| Model | Agentic coding | Code | Math | Science | Hard reasoning | Tool use | Computer use | Long context | Multimodal | Factuality | Answers in full | Instructions |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
| ||||||||||||
| 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% |
| ||||||||||||
| 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% |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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% |
| ||||||||||||
| 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 |
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| 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 |
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| 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% |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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% |
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| 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% |
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| 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% |
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| 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% |
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| 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% |
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| 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 |
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| 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 |
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| 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% |
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| 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 |
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| 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% |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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% |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
| ||||||||||||
| 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 |
| ||||||||||||
| 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 |
| ||||||||||||
| 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 |
| ||||||||||||
| 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 |
| ||||||||||||
| 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 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
- Claude Opus 5.566.9%
- Claude Fable 5.163.1%
- Gemini 4 Argon61.8%
- Claude Sonnet 5.561%
- Claude Fable 561%
- MiMo-V2.6-Pro60.9%
- Kimi K3.1 Pro60.8%
- Muse Wonder60.3%
- Kimi K359.5%
- GLM-5.359%
- Step 5 Preview58.9%
- Muse Spark 1.358.8%
- Gemini 3.1 Pro Preview58.7%
- Muse Spark Open58.4%
- GPT-6 Sol57.6%
- Grok 4.757.4%
- GPT-5.6 Sol57.1%
- Ling 3.1 Flash56.8%
- Gemini 3.8 Flash56.6%
- GPT-6 Astra56.5%
- Grok 4.656.5%
- Claude Opus 556.4%
- DeepSeek V4.1 Flash55.9%
- GPT-5.555.8%
- GPT-6.1 Terra55.1%
- GPT-5.6 Terra55%
- Claude Haiku 5.555%
- Grok 4.555%
- GPT-6 Luna54.6%
- Claude Opus 4.754.5%
- Claude Opus 4.854.4%
- Claude Sonnet 554.3%
- GPT-6.1 Sol54.2%
- GPT-5.6 Luna53.6%
- Gemini 3.6 Flash53.4%
- MiniMax M3.1 Flash53.4%
- Qwen3.8 Max52.1%
- GLM-5.3-Flash51.6%
- MiMo-V2.6-Flash51.3%
- GLM-5.251.2%
- DeepSeek V4 Pro51%
- Claude Sonnet 4.650.1%
- Kimi K2.7 Code47.8%
- Qwen3.8 27B46.6%
- Claude Sonnet 4.545.7%
- Gemma 4 31B45.5%
- Claude Haiku 4.542.2%
- Mistral Medium 3.540.2%
- gpt-oss-120b34%
- Llama 4 Maverick31.7%
- Llama 4 Scout21.3%
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
- Claude Opus 5.561.4%
- Claude Fable 5.159.1%
- Gemini 4 Argon57.1%
- Claude Fable 555.5%
- Claude Sonnet 5.555%
- Claude Opus 554.9%
- GPT-6 Astra54.7%
- GPT-6.1 Sol52.9%
- Kimi K3.1 Pro52.5%
- Muse Wonder50.1%
- GPT-5.6 Sol49.5%
- MiMo-V2.6-Pro49.4%
- Claude Opus 4.848.7%
- Muse Spark 1.348.7%
- MiniMax M3.1 Flash48%
- GPT-6 Sol47.9%
- Gemini 3.8 Flash47.8%
- Gemini 3.1 Pro Preview47%
- Kimi K346.9%
- Step 5 Preview46.5%
- GPT-6.1 Terra46.2%
- GPT-5.545.8%
- Claude Haiku 5.544.4%
- Muse Spark Open43.9%
- GPT-5.443.7%
- Ling 3.1 Flash43.5%
- DeepSeek V4.1 Flash43.3%
- Grok 4.743.1%
- Qwen3.8 Max43.1%
- GPT-5.6 Terra42.9%
- Grok 4.642.9%
- Grok 4.542.7%
- GPT-5.3-Codex42.5%
- Claude Opus 4.742.3%
- GLM-5.342.3%
- Claude Sonnet 541.3%
- GLM-5.241.1%
- DeepSeek V4 Pro41%
- Gemini 3.6 Flash40.8%
- Claude Opus 4.639.9%
- GLM-5.3-Flash39.9%
- GPT-5.6 Luna39.5%
- GPT-6 Luna38.5%
- MiMo-V2.6-Flash35.1%
- Kimi K2.7 Code35%
- Qwen3.8 27B33.9%
- Claude Sonnet 4.633.6%
- Claude Opus 4.530.1%
- Gemma 4 31B23.6%
- gpt-oss-120b19.6%
- Claude Sonnet 4.517.8%
- Mistral Medium 3.513.8%
- Claude Haiku 4.510.4%
- Llama 4 Maverick4.9%
- Llama 4 Scout3.8%
The other models in the grid have no result on Humanity's Last Exam yet.
Tool useAutomationBench-AA
- Gemini 4 Argon77.5%
- Kimi K3.1 Pro73.3%
- MiniMax M3.1 Flash72.5%
- Claude Sonnet 5.571.8%
- Claude Opus 5.569.5%
- GPT-6 Astra68.5%
- Muse Wonder68.1%
- Ling 3.1 Flash67.5%
- Grok 4.666.7%
- DeepSeek V4.1 Flash66.6%
- Grok 4.765.6%
- GPT-6.1 Sol64.9%
- MiMo-V2.6-Flash64.1%
- GLM-5.362.2%
- GPT-6 Sol61.6%
- GLM-5.3-Flash60.4%
- GPT-6.1 Terra60.2%
- GPT-5.6 Sol60.1%
- Gemini 3.8 Flash59.9%
- GPT-5.6 Terra59.6%
- Claude Fable 5.159.4%
- MiMo-V2.6-Pro58.6%
- Kimi K358.3%
- Grok 4.557.9%
- Muse Spark 1.357.9%
- Mistral Large 457.8%
- DeepSeek V4 Pro56.7%
- Claude Opus 556.6%
- Qwen3.8 Max56.2%
- Claude Fable 554.1%
- GPT-6 Luna53.2%
- Gemini 3.6 Flash53%
- Step 5 Preview51%
- GPT-5.6 Luna50.2%
- Muse Spark Open49.7%
- Qwen3.8 27B48.2%
- GPT-5.547.3%
- Claude Opus 4.845.6%
- Claude Sonnet 536.5%
- Claude Haiku 5.535.4%
- Gemini 3.1 Pro Preview35.4%
- GLM-5.228.4%
- Kimi K2.7 Code24.5%
- Claude Sonnet 4.620.1%
- Claude Sonnet 4.514.1%
- Mistral Medium 3.56.3%
- Gemma 4 31B4.9%
- Claude Haiku 4.53.2%
- gpt-oss-120b0.2%
- Llama 4 Scout0.2%
- Llama 4 Maverick0.2%
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
- GPT-6 Luna48.9%Anthropic's run, OSWorld 2.1
- Claude Opus 4.748.9%OSWorld team's run, OSWorld 2.0
- 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
- Kimi K388.7%
- Step 5 Preview88.3%
- Kimi K3.1 Pro86.3%
- MiMo-V2.6-Pro86.3%
- Claude Fable 5.185.3%
- Claude Opus 5.584.7%
- GPT-5.584.3%
- GPT-5.6 Sol84%
- GPT-5.6 Luna83.7%
- GPT-6 Sol83.7%
- GPT-6 Luna83.3%
- GPT-5.3-Codex83.3%
- GPT-6.1 Sol83%
- GPT-5.6 Terra83%
- Muse Spark 1.383%
- GPT-6.1 Terra82.7%
- Claude Haiku 5.582.7%
- Claude Sonnet 5.582.7%
- DeepSeek V4.1 Flash82.7%
- Claude Fable 582.3%
- Ling 3.1 Flash82.3%
- GPT-5.482%
- Claude Sonnet 582%
- Gemini 3.1 Pro Preview82%
- Qwen3.8 27B82%
- Gemini 3.8 Flash81.3%
- GPT-6 Astra80.7%
- Muse Wonder80.7%
- DeepSeek V4 Pro80.3%
- Grok 4.680.3%
- Qwen3.8 Max80.3%
- Claude Sonnet 4.680%
- Gemini 3.6 Flash80%
- GLM-5.3-Flash80%
- Gemini 4 Argon79.7%
- MiniMax M3.1 Flash79.7%
- GLM-5.379.7%
- Claude Opus 579.3%
- Kimi K2.7 Code79.3%
- Grok 4.579.3%
- Claude Opus 4.778.7%
- GLM-5.278.3%
- Muse Spark Open78.3%
- Claude Opus 4.678%
- Claude Opus 4.877.7%
- Claude Opus 4.577.3%
- Grok 4.776.7%
- Claude Haiku 4.574.3%
- MiMo-V2.6-Flash74.3%
- Claude Sonnet 4.572.3%
- Gemma 4 31B69.7%
- Mistral Medium 3.569.3%
- gpt-oss-120b52%
- Llama 4 Maverick50%
- Llama 4 Scout27.7%
The other models in the grid have no result on AA-LCR v1.1 yet.
MultimodalMMMU-Pro
- Claude Opus 5.587.7%
- GPT-6 Astra86.9%
- GPT-6.1 Sol86%
- Gemini 3.8 Flash85.6%
- GPT-6.1 Terra84.7%
- Claude Opus 584.7%
- Kimi K3.1 Pro83.9%
- GPT-5.6 Sol83.4%
- Gemini 3.6 Flash83.2%
- GPT-6 Sol82.9%
- Qwen3.8 Max82.8%
- Gemini 3.1 Pro Preview82.4%
- Muse Spark 1.382%
- Muse Spark Open80.9%
- GPT-5.6 Terra80.7%
- DeepSeek V4.1 Flash80.6%
- Kimi K380.5%
- Grok 4.580.4%
- GPT-5.579.9%
- GPT-6 Luna79.7%
- MiniMax M3.1 Flash79.1%
- Claude Opus 4.778.8%
- GPT-5.6 Luna78.6%
- GPT-5.3-Codex78.5%
- GPT-5.478.4%
- Claude Sonnet 577.3%
- Step 5 Preview76.4%
- Qwen3.8 27B76.3%
- Claude Opus 4.675.4%
- Claude Opus 4.574%
- Gemma 4 31B73.4%
- Claude Sonnet 4.673.3%
- MiMo-V2.6-Flash73.1%
- Claude Sonnet 4.568.7%
- Mistral Medium 3.564.9%
- Llama 4 Maverick62.1%
- Claude Haiku 4.558.6%
- Llama 4 Scout52.9%
- Claude Fable 5.186.3%Moonshot AI's run
- GLM-5.3-Flash86%Vals.ai's run, MMMU-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.
Answers in fullSpeechMap
- Grok 4.593.1%
- Gemini 3.6 Flash87.2%
- Gemini 4 Argon87%
- Gemini 3.8 Flash86.1%
- Grok 4.683.6%
- Claude Opus 5.582.8%
- Gemma 4 31B82.3%
- Claude Sonnet 5.581.6%
- Claude Haiku 5.578.8%
- Claude Opus 573.7%
- Claude Fable 5.172.4%
- DeepSeek V4 Pro72%
- GLM-5.371.5%
- Mistral Medium 3.570.5%
- Claude Opus 4.770.2%
- Llama 4 Scout70.1%
- GPT-6.1 Sol69.5%
- GPT-6 Astra68.2%
- Claude Opus 4.867.4%
- MiMo-V2.6-Flash66.9%
- MiMo-V2.6-Pro66.5%
- Kimi K365.9%
- GLM-5.3-Flash65.4%
- Qwen3.8 Max65.3%
- Claude Fable 564.8%
- Grok 4.764.1%
- GPT-6 Sol64%
- GPT-6 Luna63.4%
- Claude Opus 4.659.9%
- Gemini 3.1 Pro Preview59.9%
- Step 5 Preview59.7%
- Llama 4 Maverick58.3%
- Claude Opus 4.557.5%
- Muse Wonder42.7%
- gpt-oss-120b36.3%
- Claude Sonnet 4.635.8%
- Claude Sonnet 4.531.4%
- Muse Spark 1.325.8%
- Claude Haiku 4.524.7%
The other models in the grid have no result on SpeechMap yet.
Instruction followingMultiChallenge
- GPT-6.1 Sol82%
- Gemini 4 Argon78.7%
- DeepSeek V4.1 Flash74.9%
- GPT-6.1 Terra74.2%
- Muse Spark Open74.2%
- Gemini 3.1 Pro Preview71.4%
- Claude Haiku 5.567.7%
- Claude Opus 4.559%
- Claude Sonnet 4.555.3%
- Claude Haiku 4.550.5%
- gpt-oss-120b45.3%
- Claude Opus 4.637.2%
- Qwen3.8 27B79.5%Alibaba's own figure, IFBench
- GPT-5.575.9%Artificial Analysis's run, IFBench
- Gemma 4 31B75.6%Artificial Analysis's run, IFBench
- GPT-5.3-Codex75.4%Artificial Analysis's run, IFBench
- GPT-5.473.9%Artificial Analysis's run, IFBench
- GLM-5.273.3%Artificial Analysis's run, IFBench
- GPT-5.6 Sol72.7%Artificial Analysis's run, IFBench
- GPT-5.6 Terra71.2%Artificial Analysis's run, IFBench
- Mistral Medium 3.568.8%Artificial Analysis's run, IFBench
- Claude Fable 563.5%Artificial Analysis's run, IFBench
- Kimi K2.7 Code63.1%Artificial Analysis's run, IFBench
- Claude Opus 4.862.2%Artificial Analysis's run, IFBench
- Claude Opus 4.758.6%Artificial Analysis's run, IFBench
- Claude Sonnet 4.656.6%Artificial Analysis's run, IFBench
- Llama 4 Maverick43%Artificial Analysis's run, IFBench
- Llama 4 Scout39.5%Artificial Analysis's run, IFBench
The other models in the grid have no result on MultiChallenge yet.
Every measure on record
Cost, speed, reliability, knowledge-work and domain results for GPT-6 Luna beyond the twelve chart axes, each with the run it comes from and its publisher. These stay separate measures; none is folded into the chart.
| Measure | GPT-6 Luna | Run | Source | Read |
|---|---|---|---|---|
| Specs and access | ||||
| Context windowThe most tokens one request can hold, as the maker states it. Size alone says nothing about how well the model uses a long input; the Long context axis measures that. | 1.05M | OpenAI | Oct 28, 2026 | |
| Max outputThe most tokens the model writes in one response. | 128K | OpenAI | Oct 28, 2026 | |
| ReadsThe kinds of input the model accepts. | Text, Images | Artificial Analysis | Oct 27, 2026 | |
| LicenceThe terms the weights or the service are offered under. | OpenAI Services Agreement | OpenAI | Nov 1, 2026 | |
| Open weightsWhether the maker publishes the weights for anyone to run. | No | Artificial Analysis | Oct 27, 2026 | |
| Routes offering itOf the 19 clouds, gateways and coding products GILD tracks, how many offer the release today, preview included. | 10 of 19 | GILD route matrix | Oct 28, 2026 | |
| Cost | ||||
| Answer tokens per taskLower is better. Answer tokens per Intelligence Index task, reasoning excluded. | 11,122 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Cost per task, cache readsLower is better. The cache-read share of AA's cost per Intelligence Index task. | $0.029 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Cost per task, cache writesLower is better. The cache-write share of AA's cost per Intelligence Index task. | $0.012 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Cost per task, inputLower is better. The input-token share of AA's cost per Intelligence Index task. | $0.043 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Cost per task, outputLower is better. The answer-token share of AA's cost per Intelligence Index task. | $0.025 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Cost per task, reasoningLower is better. The reasoning-token share of AA's cost per Intelligence Index task. | $0.019 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Cost per taskLower is better. What one Intelligence Index task cost AA at list price, input, cache, output and reasoning tokens together. | $0.068 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Cost to run the indexLower is better. What the whole Intelligence Index run cost AA at list price. The runs cost from tens of dollars to about $13,000, so the axis is log from $10 to $100,000. | $122.05 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Output tokens per taskLower is better. Tokens the model wrote per Intelligence Index task, reasoning included: how verbose it is. | 50,012 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Reasoning tokens per taskLower is better. Reasoning tokens per Intelligence Index task. | 38,890 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Speed | ||||
| End-to-end time, 100K promptLower is better. Median seconds for a whole response with a 100,000-token prompt. | 190 s | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| End-to-end response timeLower is better. Seconds AA measures for a whole response on its standard prompt. | 98 s | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Time to first answer token, 100K promptLower is better. Median seconds to the first answer token with a 100,000-token prompt. | 185 s | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Time to first answer tokenLower is better. Seconds until the first answer token, after any reasoning, on AA's API runs. | 94 s | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Output speed, 100K promptMedian output tokens per second when the prompt is about 100,000 tokens long. | 116 tokens/s | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Output speedMedian output tokens per second on AA's API runs, with the 5th and 95th percentile when AA prints them. | 127 tokens/s | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Time per taskLower is better. Wall-clock seconds per Intelligence Index task. Runs take from about 15 s to about 33 min, so the axis is log from 1 s to 10,000 s. | 392 s | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Time to first token, 100K promptLower is better. Median seconds to the first chunk when the prompt is about 100,000 tokens long: long-input latency, separate from window size. | 185 s | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Time to first tokenLower is better. Median seconds to the first streamed chunk on AA's API runs, with the 5th and 95th percentile when AA prints them. Reasoning models spend this time thinking. | 94 s | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Reliability | ||||
| Hallucination rateLower is better. AA-Omniscience hallucination rate shows how often the model answers wrongly when it does not know and should decline. | 79.3% | Max effort | Artificial Analysis | Oct 28, 2026 |
| Omniscience accuracyShare of AA-Omniscience knowledge questions answered correctly. | 39.6% | Max effort | Artificial Analysis | Oct 28, 2026 |
| Omniscience indexAA-Omniscience index: correct answers minus wrong answers, declines neutral, -100 to 100. | 0.7 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| SimpleQA Verified (Epoch)Share of SimpleQA Verified factual questions answered correctly, best scorer, Epoch AI's run. A second factuality source beside AA-Omniscience. | 41.4% | Max effort | Epoch AI | Sep 22, 2026 |
| Knowledge work | ||||
| AA-BriefcaseAA-Briefcase overall Elo for long business deliverables, with AA's 95% interval. | 1,336 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| AnalystAgent (AA run)AA's AnalystAgent score for analyst workflows. | 43.8% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| APEX-Agents (maintainers)APEX-Agents pass@1 from the maintainers, as Epoch AI republishes it. | 44.3% | Max effort | Mercor (via Epoch AI) | Oct 26, 2026 |
| GDP-PDF (AA run)Share of GDP-PDF document tasks where every check passes. | 22.8% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| GDPval-AAGDPval-AA Elo: AA's run of economically valuable knowledge-work tasks, graded pairwise, with AA's interval. | 1,445 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| MLCR (AA run)AA's MLCR overall score for long, multi-document reading. | 26.1% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Capability beyond the chart | ||||
| AA-LCRAA long-context reasoning accuracy. | 83.3% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| ALE-BenchALE-Bench performance rating on long optimization contests, as Epoch AI republishes it. | 1,577 | Extra-high effort | Sakana AI (via Epoch AI) | Oct 26, 2026 |
| ARC-AGI-2ARC-AGI-2 score from the ARC Prize maintainers, as Epoch AI republishes it. | 59.3% | Max effort | ARC Prize Foundation (via Epoch AI) | Oct 26, 2026 |
| Chess puzzles (Epoch)Share of chess puzzles solved, Epoch AI's run. | 36% | Max effort | Epoch AI | Oct 13, 2026 |
| CritPt (AA run)Share of CritPt research-level physics problems solved in AA's run. | 19.4% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| DTBenchDTBench accuracy, as Epoch AI republishes it. | 90.1% | Max effort | Redwood Research (via Epoch AI) | Oct 26, 2026 |
| Epoch Capabilities IndexEpoch Capabilities Index: Epoch AI's composite across its benchmark hub, with its confidence interval. | 157 | GPT-6 Luna | Epoch AI | Oct 26, 2026 |
| FrontierCode (AA run)AA's FrontierCode score: hard, unsaturated coding tasks. | 41.9% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| FrontierMath Tier 4 (Epoch)Share of FrontierMath Tier 4 research math problems solved, Epoch AI's run. | 56.1% | Max effort | Epoch AI | Sep 22, 2026 |
| FrontierMath Tiers 1 to 3 (Epoch)Share of FrontierMath Tiers 1 to 3 problems solved, Epoch AI's run. | 78.9% | Max effort | Epoch AI | Sep 22, 2026 |
| GPQA Diamond (Epoch)GPQA Diamond accuracy, best scorer, Epoch AI's run. | 90.5% | Max effort | Epoch AI | Sep 22, 2026 |
| Humanity's Last Exam (AA run)Share of Humanity's Last Exam questions answered correctly in AA's run. | 38.5% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Intelligence IndexArtificial Analysis Intelligence Index: AA's composite of its own evaluation runs, 0 to 100 points. | 38.1 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| LMCALMCA score, 0 to 100, as Epoch AI republishes it. Its interval is printed only as a half-width, so it is not drawn. | 44.5 | Max effort | Redwood Research (via Epoch AI) | Oct 26, 2026 |
| MMMU-Pro (AA run)Multimodal understanding accuracy on MMMU-Pro in AA's run. | 79.7% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Mystery game puzzles (Epoch)Share of mystery game puzzles solved, Epoch AI's run. | 7% | Max effort | Epoch AI | Sep 22, 2026 |
| OTIS Mock AIME (Epoch)Share of OTIS Mock AIME 2024 to 2025 problems solved, Epoch AI's run. | 98.9% | Max effort | Epoch AI | Sep 22, 2026 |
| ProofBenchShare of ProofBench proofs graded correct, from Vals AI as Epoch AI republishes it. | 64% | Vals AI (via Epoch AI) | Oct 26, 2026 | |
| SciCode (AA run)SciCode subproblem accuracy in AA's run. | 54.6% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Terminal-Bench 4.0 (AA run)Share of Terminal-Bench 4.0 tasks solved in AA's own harness. | 12.6% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Terminal-Bench Science (AA run)Share of Terminal-Bench Science tasks solved in AA's harness. | 8.6% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| WebDev ArenaWebDev Arena score from human pairwise votes on web builds, with its 95% interval, as Epoch AI republishes it. | 1,582 | Max effort | LMArena (via Epoch AI) | Oct 26, 2026 |
| WeirdML v3, best runWeirdML v3 maximum performance across runs, as Epoch AI republishes it. | 12.0% | Extra-high effort | Håvard Tveit Ihle (via Epoch AI) | Oct 26, 2026 |
| Domains | ||||
| EconomicsArtificial Analysis capability score for economics questions, 0 to 100 points. | 45.2 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| EngineeringArtificial Analysis capability score for engineering questions, 0 to 100 points. | 37.1 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Finance and accountingArtificial Analysis capability score for finance and accounting questions, 0 to 100 points. | 40.3 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Healthcare and medicalArtificial Analysis capability score for healthcare and medical questions, 0 to 100 points. | 38.9 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| LegalArtificial Analysis capability score for legal questions, 0 to 100 points. | 40.3 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Strategy and operationsArtificial Analysis capability score for strategy and operations questions, 0 to 100 points. | 45.7 | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
| Harvey Legal Lab (AA run)AA's run of Harvey's legal task lab. | 3.3% | GPT-6 Luna (Max) | Artificial Analysis | Oct 27, 2026 |
Routes, prices and uptime
Where GPT-6 Luna is offered, what each route charges, and how each endpoint is holding up.
Where it is offered
10 of 19 routes GILD tracks offer GPT-6 Luna today, preview included. Read Oct 28, 2026 from each route's own pages.
| Route | Status | Model ID | Limits and lifecycle | Sources |
|---|---|---|---|---|
| Amazon Bedrock | Available | openai.gpt-6-luna | us./global. profiles on bedrock-runtime; bedrock-mantle; EOL not sooner than Sept 22, 2027; tiers: Standard only | Amazon Web Services, Models at a glanceAmazon Web Services, GPT-6 Luna |
| Codex | Available | gpt-6-luna | codex -m gpt-6-luna | OpenAI, Models |
| Databricks | Available | system.ai.gpt-6-luna | Databricks' supported-models page, read Oct 30, 2026, lists GPT-6 Luna as system.ai.gpt-6-luna, for use through the Responses API; it is not supported in AI Playground. The foundation-model overview page, read Oct 30, 2026, lists GPT-6 Astra and GPT-5.6 Sol, Terra and Luna among OpenAI models, and not GPT-6 Luna. | Databricks, Databricks-hosted inferenceDatabricks, Detailed list of models supported by Databricks Foundation Model APIs |
| The maker's own API | Available | gpt-6-luna | 1,050,000 context / 128,000 output; Fast 2x, Ultrafast 6x, Batch and Flex -50%, regional +10%, >272K input 2x input and 1.5x output | OpenAI, GPT-6 Luna ModelOpenAI, GPT-6.1 Sol Model |
| GitHub Copilot | Available | not statedpicker label GPT-6 Luna | available (GA) | GitHub |
| GitHub Models | Retired | not applicableservice retired | service retired July 30, 2026 | GitHub, GitHub Models |
| Microsoft Foundry | Available | gpt-6-luna | listed in GPT-6 series | Microsoft, Foundry Models sold by AzureMicrosoft, Region availability for Foundry Models sold by Azure |
| OpenCode | Available | gpt-6-luna | OpenCode Zen, OpenAI Responses endpoint | Anomaly (OpenCode), ModelsOpenCode (Anomaly) |
| OpenRouter | Available | openai/gpt-6-luna | 1,050,000 / 128,000; batch variant listed | OpenRouter |
| Snowflake Cortex | Preview | openai-gpt-6-luna | Private preview | Snowflake |
| Windsurf / Devin | Available | gpt-6-luna-highgpt-6-luna-high-prioritygpt-6-luna-lowgpt-6-luna-low-prioritygpt-6-luna-maxgpt-6-luna-max-prioritygpt-6-luna-mediumgpt-6-luna-medium-prioritygpt-6-luna-nonegpt-6-luna-none-prioritygpt-6-luna-xhighgpt-6-luna-xhigh-priority | Low to Max thinking, plus Fast (priority) variants | Cognition (Devin, formerly Windsurf), AI Models |
Not offered on 3 routes: Antigravity, Cursor and Google Cloud Vertex / Agent Platform (Google managed).
5 routes do not say: Claude Code, Claude on Google Cloud (Anthropic managed), Claude Platform on AWS, Gemini CLI and Grok Build.
What each of these 8 routes' pages say, with sources
| Route | Status | Model ID | Limits and lifecycle | Sources |
|---|---|---|---|---|
| Antigravity | Not offered | Antigravity lists GPT-OSS-120b only among OpenAI models | ||
| Cursor | Not offered | Cursor's OpenAI rows stop at GPT-5.6 Sol, Terra, Luna | Anysphere (Cursor)CursorX (@thegitai) | |
| Google Cloud Vertex / Agent Platform (Google managed) | Not offered | OpenAI section lists gpt-oss only | Google Cloud, Anthropic's Claude on Google Cloud modelsGoogle Cloud, Gemini Enterprise Agent Platform partner models for MaaS | |
| Claude Code | Not stated | Claude Code's model configuration page, read Oct 28 and Oct 29, 2026, does not name this model. It takes a full model name on the Anthropic API, an inference profile ARN on Amazon Bedrock and a deployment name on Microsoft Foundry. | Anthropic | |
| Claude on Google Cloud (Anthropic managed) | Not stated | Region, tier and limits not stated on the route's pages. | ||
| Claude Platform on AWS | Not stated | Region, tier and limits not stated on the route's pages. | ||
| Gemini CLI | Not stated | Region, tier and limits not stated on the route's pages. | ||
| Grok Build | Not stated | Grok Build pages name only grok-4.7 | xAI (SpaceXSI) |
Prices by route
The lowest standard rate is OpenAI API, at $0.1 / $0.5 per million input and output tokens. US dollars per million tokens, as each route lists them, one row per route and rate plan, cheapest standard rate first after the maker's own; a blended rate states its mix. OpenRouter's endpoints follow under one heading, each with its monitor's uptime over 1 day and 30 minutes and the context and output it serves.
| Route | Standard, in / out | Cache read | Other rates | Source, or uptime (1 day / 30 min) and limits |
|---|---|---|---|---|
| OpenAI APIMakerLowest | $0.1 / $0.5 | OpenAI, Oct 26, 2026 | ||
| Artificial Analysis price record | $0.1 / $0.5 | $0.01 | Blended $0.2, 3 input : 1 output, no cacheBlended $0.077, 7 cache-hit : 2 input : 1 outputCache write $0.13 | Artificial Analysis, Oct 27, 2026 |
| OpenRouterprompts under 272K tokens | $0.1 / $0.5 | $0.01 | Cache read $0.02, prompts of 272K tokens or moreCache write $0.25, 5-minute cache, prompts of 272K tokens or moreCache write $0.13, 5-minute cache, prompts under 272K tokensLong context $0.2 / $0.75, prompts of 272K tokens or more | OpenRouter, Oct 28, 2026 |
| Through OpenRouter, 7 provider endpoints. OpenRouter, Oct 28, 2026 | ||||
| Azureprompts under 272K tokens | $0.1 / $0.5 | $0.01 | Cache read $0.02, prompts of 272K tokens or moreCache write $0.25, 5-minute cache, prompts of 272K tokens or moreCache write $0.13, 5-minute cache, prompts under 272K tokensLong context $0.2 / $0.75, prompts of 272K tokens or more | Up 100% / 100%1.05M context, 128K output |
| OpenAIprompts under 272K tokens | $0.1 / $0.5 | $0.01 | Cache read $0.02, prompts of 272K tokens or moreCache write $0.25, 5-minute cache, prompts of 272K tokens or moreCache write $0.13, 5-minute cache, prompts under 272K tokensLong context $0.2 / $0.75, prompts of 272K tokens or more | Up 100% / 100%1.05M context, 128K output |
| Amazon Bedrock (us-east-1)prompts under 272K tokens | $0.11 / $0.55 | $0.011 | Cache read $0.022, prompts of 272K tokens or moreCache write $0.28, 5-minute cache, prompts of 272K tokens or moreCache write $0.14, 5-minute cache, prompts under 272K tokensLong context $0.22 / $0.82, prompts of 272K tokens or more | Up 100% / 100%1.05M context, 128K output |
| Azure (eu)prompts under 272K tokens | $0.11 / $0.55 | $0.011 | Cache read $0.022, prompts of 272K tokens or moreCache write $0.28, 5-minute cache, prompts of 272K tokens or moreCache write $0.14, 5-minute cache, prompts under 272K tokensLong context $0.22 / $0.82, prompts of 272K tokens or more | Up 100% / 100%1.05M context, 128K output |
| Azure (us)prompts under 272K tokens | $0.11 / $0.55 | $0.011 | Cache read $0.022, prompts of 272K tokens or moreCache write $0.28, 5-minute cache, prompts of 272K tokens or moreCache write $0.14, 5-minute cache, prompts under 272K tokensLong context $0.22 / $0.82, prompts of 272K tokens or more | Up 100% / 100%1.05M context, 128K output |
| OpenAI (fast, priority) | Cache read $0.04, priority tier, prompts of 272K tokens or moreCache read $0.02, priority tier, prompts under 272K tokensCache write $0.5, priority tier, 5-minute cache, prompts of 272K tokens or moreCache write $0.25, priority tier, 5-minute cache, prompts under 272K tokensLong context $0.4 / $1.50, priority tier, prompts of 272K tokens or morePriority $0.2 / $1, priority tier, prompts under 272K tokens | Up 100% / 100%1.05M context, 128K output | ||
| OpenAI (flex) | Cache read $0.01, flex tier, prompts of 272K tokens or moreCache read $0.005, flex tier, prompts under 272K tokensCache write $0.13, flex tier, 5-minute cache, prompts of 272K tokens or moreCache write $0.063, flex tier, 5-minute cache, prompts under 272K tokensFlex $0.05 / $0.25, flex tier, prompts under 272K tokensLong context $0.1 / $0.38, flex tier, prompts of 272K tokens or more | Up 97.6% / 100%1.05M context, 128K output | ||
Uptime and speed by endpoint
Each provider endpoint as its monitor reports it: uptime over the last day and the last 30 minutes, output speed and time to first token, and the limits it serves. A blank is a figure the monitor does not publish for that endpoint.
| Endpoint | Uptime, 1 day | Uptime, 30 min | Output speed | First token | Context / max output | Source |
|---|---|---|---|---|---|---|
| Artificial Analysis: Microsoft Azure | 185 tokens/s | 70 s | Artificial Analysis, Oct 28, 2026 | |||
| Artificial Analysis: OpenAI (Flex) | 64 tokens/s | 166 s | Artificial Analysis, Oct 28, 2026 | |||
| Artificial Analysis: OpenAI | 124 tokens/s | 77 s | Artificial Analysis, Oct 28, 2026 | |||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100.0%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100.0%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100.0%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 | ||||
| Status page: OpenAI API aggregate | 100%, Displayed window: Jul 30, 2026 to Oct 28, 2026; aggregate across tiers, models, and error types where the page states this limitation. | OpenAI, Oct 28, 2026 |
Lineup
OpenAI's 12 releases in 7 families, oldest to newest along each tier, with list price and speed. GPT-6 Luna is lit.
CurrentLegacyThis model
Price is the list price per 1M tokens, input / output. Speed is median output tokens per second. Bars share one scale across the lineup. Read between Oct 26, 2026 and Nov 2, 2026.
GPT Terra
OpenAI's fast, efficient GPT tier for visual reasoning, computer use, and writing.
2 releases
GPT Sol
OpenAI's GPT model tier for demanding everyday and agentic work.
3 releases
GPT Luna
OpenAI's most efficient GPT model tier for focused, high-volume tasks.
2 releases
- 5.6Current. In 6 products we coverPrice$0.20 / $1.20Speed118.9 tok/s
- This page6Current. In 6 products we coverPrice$0.10 / $0.50Speed124.3 tok/s
GPT Astra
OpenAI's highest-capability GPT model tier.
1 release
GPT-5
OpenAI's GPT-5 frontier models, released before the Sol, Terra and Luna tiers.
2 releases
gpt-oss
OpenAI's open-weight GPT models under the Apache 2.0 license.
1 release
GPT Codex
OpenAI's GPT models tuned for agentic coding in Codex.
1 release
Where it fits
The layers of Agentic Software Development that GPT-6 Luna can power. A product supplies the rest.
Product & Engineering, Software Development
Agentic Software Development
- Primary role
- Foundation Model / Reasoning Engine
GPT-6 Luna is positioned by its provider for focused, high-volume tasks. In an agentic software-development product it suits steps such as routing, classification, and extraction. The Model does not itself provide the surrounding interface, execution environment, or system of record.
- Supporting
Work planning & task decomposition
The Model can assist with work planning & task decomposition when a Product supplies the necessary context and tools.
- Supporting
Codebase context & intelligence
The Model can assist with codebase context & intelligence when a Product supplies the necessary context and tools.
- Supporting
Coding & implementation
The Model can assist with coding & implementation when a Product supplies the necessary context and tools.
- Supporting
Debugging & investigation
The Model can assist with debugging & investigation when a Product supplies the necessary context and tools.
- Supporting
Testing & verification
The Model can assist with testing & verification when a Product supplies the necessary context and tools.
- Supporting
Code review & quality
The Model can assist with code review & quality when a Product supplies the necessary context and tools.
Products
We cover 6 products that offer GPT-6 Luna today: 5 subscriptions and 1 API.
Through a subscription
- OpenCode+27 PositiveOpen-source, MIT-licensed coding agent that edits code and runs commands from a terminal UI, desktop app, web client, or editor extension.
- GitHub Copilot+15 MixedGitHub's AI coding agents, IDE assistance, and pull request review.
- ChatGPT+14 MixedOpenAI's general-purpose AI assistant for individuals and organizations.
- Codex+55 Strong positiveOpenAI's coding agent for cloud, terminal, IDE, and app workflows.
- Zed+25 PositiveNative code editor with its own agent, edit predictions, and room for external agents.
Through an API
Limits each vendor states
- OpenCodeGo, Go Plus
- Estimated requests per 5 hours: Go 4,230, Go Plus 16,920; monthly usage: Go $15, Go Plus $60
- GitHub CopilotCopilot Pro, Copilot Pro+, Copilot Max, Copilot Business, Copilot Enterprise
- Included on the Copilot plans listed. Copilot Free and Copilot Student get auto model selection only, so they cannot pick it.
- ChatGPTFree, Go, Plus
- Free and Go: GPT-6 Luna at Standard speed in the desktop app, subject to rollout; 350-3,000 local messages per five-hour period on Plus and Standard Business (estimate)
- CodexFree, Go, Plus
- Free and Go: GPT-6 Luna at Standard speed in the desktop app, subject to rollout; 350-3,000 local messages per five-hour period on Plus and Standard Business (estimate)
- ZedPro (14-day free trial)
- $5 of GPT-6 Luna usage and unlimited accepted edit predictions during the 14-day trial; the trial ends when the $5 balance is used or 14 days pass; GPT-6 Luna is the only hosted model available during the free trial
Plan access as each vendor's page states it: Anomaly (Oct 28, 2026), GitHub (Oct 31, 2026), OpenAI (Oct 28, 2026), Zed (Oct 28, 2026).
Comparisons
GILD comparisons that name GPT-6 Luna, then those of the clouds and APIs that serve it.
- Claude API vs OpenAI API: Top Scores or Lower Cost per Task?Names GPT-6 Luna
- Claude Code vs Codex: Which Agentic Coding Workflow Fits Your Team?Names GPT-6 Luna
- Codex vs Devin: Which Delegated Coding Agent Fits Your Team?Names GPT-6 Luna
- Codex vs GitHub Copilot: ChatGPT-Bundled Agent or GitHub-Native Platform?Names GPT-6 Luna
- Factory vs Codex: Every Model Under One Plan, or OpenAI's Agent Inside ChatGPT?Names GPT-6 Luna
- OpenAI API vs Azure OpenAI: Same Models, Different ContractsNames GPT-6 Luna
- OpenAI API vs Gemini API: Who Finishes the Task for Less?Names GPT-6 Luna
- Amazon Bedrock vs Claude API: Buying Claude Through AWS or From AnthropicCompares routes that serve GPT-6 Luna
- Amazon Bedrock vs Microsoft Foundry: Which Cloud AI Platform Fits Your Stack?Compares routes that serve GPT-6 Luna
- DeepSeek API vs OpenRouter: Buy DeepSeek From DeepSeek, or From Another Host?Compares routes that serve GPT-6 Luna
- Gemini API vs Amazon Bedrock: One Model Family or a Model Marketplace?Compares routes that serve GPT-6 Luna
- Gemini Enterprise Agent Platform vs Microsoft Foundry: Google Cloud or Azure for Models and AgentsCompares routes that serve GPT-6 Luna
- LiteLLM vs OpenRouter: Run Your Own Gateway or Rent One?Compares routes that serve GPT-6 Luna
- OpenRouter vs Portkey: Rent Model Access or Run a Governed Gateway?Compares routes that serve GPT-6 Luna
- Together AI vs OpenRouter: One Open-Model Host or a Router Across HostsCompares routes that serve GPT-6 Luna
What practitioners report
Strongest on Cost, weakest on Long context.
Each figure is the share of accounts naming GPT-6 Luna 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.
| Axis | Favorable | Accounts |
|---|---|---|
| Quality and reasoningWhether its answers and code are right on the author's own task: reasoning, accuracy, instruction following, and consistency. | 55% | 16 favorable, 13 not, 29 authors |
| Tool use and structured outputWhether it calls tools correctly, returns valid structured output, and keeps agent loops on track. | 33% | 3 favorable, 6 not, 9 authors |
| Long contextHow it holds up over long documents, large codebases, long sessions, and many images or PDFs. | 20% | 1 favorable, 4 not, 5 authors |
| Developer experienceHow it is to build with: prompting, steering, effort settings, and support across SDKs and providers. | 25% | 2 favorable, 6 not, 8 authors |
| CostWhat the work costs: price per task, token use, and what caching and batch save. | 79% | 22 favorable, 6 not, 26 authors |
| SpeedResponse time, time to first token, and throughput. | 45% | 5 favorable, 6 not, 11 authors |
| ReliabilityWhether requests keep succeeding: overloaded errors, outages, rate limits, and changes that broke what worked. | Only concerns on record | 0 favorable, 5 not, 5 authors |
Every model on these seven axes
| Axis | GPT-6 Luna | GPT-6.1 Terra | GPT-6.1 Sol | 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 | 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 | 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 | Mistral Large 4 | Mistral Medium 3.5 | Grok 4.7 | Grok 4.6 | Grok 4.5 | MiniMax M3.1 Flash | Mercury 3 | GLM-5.3-Flash | GLM-5.3 | GLM-5.2 | Muse Spark Open | Muse Wonder | Muse Spark 1.3 | Llama 4 Scout | Llama 4 Maverick | Qwen3.8 27B | Qwen3.8 Max | MiMo-V2.6-Pro | MiMo-V2.6-Flash | Ling 3.1 Flash | Step 5 Preview |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Quality and reasoning | 55%16 favorable, 13 not, 29 authors | 33%3 favorable, 6 not, 8 authors | 55%12 favorable, 10 not, 22 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 | 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 | 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 | 58%11 favorable, 8 not, 18 authors | 36%5 favorable, 9 not, 14 authors | 43%16 favorable, 21 not, 36 authors | 86%6 favorable, 1 not, 7 authors | 60%3 favorable, 2 not, 5 authors | 62%13 favorable, 8 not, 21 authors | 86%18 favorable, 3 not, 21 authors | 67%14 favorable, 7 not, 21 authors | 76%47 favorable, 15 not, 61 authors | 71%5 favorable, 2 not, 7 authors | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors | 20%1 favorable, 4 not, 5 authors | Only praise on record2 favorable, 0 not, 2 authors | 53%8 favorable, 7 not, 15 authors | 44%8 favorable, 10 not, 17 authors | 71%24 favorable, 10 not, 34 authors | 57%4 favorable, 3 not, 7 authors | 22%2 favorable, 7 not, 9 authors | 40%4 favorable, 6 not, 10 authors | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors |
| Tool use and structured output | 33%3 favorable, 6 not, 9 authors | 40%2 favorable, 3 not, 5 authors | 40%4 favorable, 6 not, 10 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 | 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 | 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%5 favorable, 8 not, 13 authors | Only concerns on record0 favorable, 5 not, 5 authors | 38%6 favorable, 10 not, 16 authors | Only praise on record2 favorable, 0 not, 2 authors | Only concerns on record0 favorable, 1 not, 1 author | 67%4 favorable, 2 not, 6 authors | 80%8 favorable, 2 not, 10 authors | 33%2 favorable, 4 not, 6 authors | 57%4 favorable, 3 not, 7 authors | No accounts yet | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | Needs 5 accounts from 3 authors1 favorable, 2 not, 3 authors | Only concerns on record0 favorable, 4 not, 4 authors | 46%6 favorable, 7 not, 13 authors | Only praise on record1 favorable, 0 not, 1 author | Only concerns on record0 favorable, 5 not, 5 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author |
| Long context | 20%1 favorable, 4 not, 5 authors | Only concerns on record0 favorable, 1 not, 1 author | 38%3 favorable, 5 not, 8 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 | 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 | 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 | Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors | No accounts yet | 42%5 favorable, 7 not, 12 authors | No accounts yet | No accounts yet | 40%2 favorable, 3 not, 5 authors | 60%3 favorable, 2 not, 5 authors | No accounts yet | 40%2 favorable, 3 not, 5 authors | No accounts yet | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 3 not, 3 authors | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors | 75%6 favorable, 2 not, 8 authors | Only praise on record1 favorable, 0 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | No accounts yet | No accounts yet |
| Developer experience | 25%2 favorable, 6 not, 8 authors | Only concerns on record0 favorable, 2 not, 2 authors | 27%3 favorable, 8 not, 11 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 | 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 | 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 | 13%2 favorable, 14 not, 16 authors | Only concerns on record0 favorable, 9 not, 9 authors | 25%5 favorable, 15 not, 20 authors | No accounts yet | No accounts yet | Only concerns on record0 favorable, 7 not, 7 authors | 11%1 favorable, 8 not, 9 authors | Only concerns on record0 favorable, 3 not, 3 authors | 33%5 favorable, 10 not, 15 authors | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | Only concerns on record0 favorable, 6 not, 6 authors | Only concerns on record0 favorable, 1 not, 1 author | 17%1 favorable, 5 not, 6 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | No accounts yet |
| Cost | 79%22 favorable, 6 not, 26 authors | 80%8 favorable, 2 not, 9 authors | 74%14 favorable, 5 not, 19 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 | 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 | 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 | 27%3 favorable, 8 not, 11 authors | Only concerns on record0 favorable, 8 not, 8 authors | 19%5 favorable, 21 not, 26 authors | 20%1 favorable, 4 not, 5 authors | Only praise on record4 favorable, 0 not, 4 authors | 80%12 favorable, 3 not, 15 authors | 50%6 favorable, 6 not, 12 authors | 76%16 favorable, 5 not, 21 authors | 35%8 favorable, 15 not, 23 authors | 71%5 favorable, 2 not, 7 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 9 not, 9 authors | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors | Only praise on record1 favorable, 0 not, 1 author | Only praise on record1 favorable, 0 not, 1 author | Needs 5 accounts from 3 authors2 favorable, 2 not, 4 authors | Only concerns on record0 favorable, 2 not, 2 authors | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | Only praise on record3 favorable, 0 not, 3 authors | Only praise on record1 favorable, 0 not, 1 author | Only praise on record1 favorable, 0 not, 1 author |
| Speed | 45%5 favorable, 6 not, 11 authors | 83%5 favorable, 1 not, 5 authors | 37%7 favorable, 12 not, 19 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 | 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 | 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 | 21%3 favorable, 11 not, 14 authors | Only concerns on record0 favorable, 2 not, 2 authors | 29%5 favorable, 12 not, 17 authors | Needs 5 accounts from 3 authors1 favorable, 2 not, 3 authors | Only praise on record2 favorable, 0 not, 2 authors | 44%4 favorable, 5 not, 9 authors | 65%13 favorable, 7 not, 20 authors | 33%3 favorable, 6 not, 9 authors | 20%3 favorable, 12 not, 15 authors | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors | No accounts yet | 33%7 favorable, 14 not, 21 authors | Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors | Only concerns on record0 favorable, 4 not, 4 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 2 not, 2 authors | Only concerns on record0 favorable, 1 not, 1 author |
| Reliability | Only concerns on record0 favorable, 5 not, 5 authors | Only concerns on record0 favorable, 1 not, 1 author | 13%1 favorable, 7 not, 8 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 | 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 | 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 | 38%3 favorable, 5 not, 8 authors | Only concerns on record0 favorable, 4 not, 4 authors | 22%2 favorable, 7 not, 9 authors | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | 60%3 favorable, 2 not, 5 authors | 33%2 favorable, 4 not, 5 authors | Only concerns on record0 favorable, 7 not, 7 authors | Only concerns on record0 favorable, 4 not, 4 authors | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | Only concerns on record0 favorable, 2 not, 2 authors | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 9 not, 9 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 2 not, 2 authors | No accounts yet | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author |
The 71 accounts behind GPT-6 Luna's figures
GILD searched Hacker News, GitHub, Reddit, X, Artificial Analysis and the web for accounts that name GPT-6 Luna.
Quality and reasoning 55%
- Favorable
competent at both SQL queries and building HTML and JavaScript for Datasette Apps
Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna, and a new price war. Simon Willison, independent developer and Datasette creator (stated on his weblog), self-described, Sep 22, 2026. GPT-6 Luna running the Datasette Agent at agent.datasette.io.
- Favorable
GPT-6 Luna: Shockingly capable
X post by @alighodsi. Ali Ghodsi, CEO and co-founder of Databricks, as listed on the Databricks Data + AI Summit Tuesday Keynote session page (read Oct 26, 2026) (role source), Sep 28, 2026. GPT-6 Luna in Databricks' internal model test across 2,400 engineers. A partner account: the author's company sells or serves this model, so it counts toward no figure.
- Favorable
GPT-6 Luna is surprisingly capable given how cheap it is
X post by @pwendell. Patrick Wendell, co-founder and VP of engineering at Databricks, as listed on the Databricks Data + AI Summit Tuesday Keynote session page (read Oct 26, 2026) (role source), Sep 28, 2026. GPT-6 Luna in Databricks' online workload analysis of 2,400 engineers plus offline evals, routed through Unity Gateway. A partner account: the author's company sells or serves this model, so it counts toward no figure.
- Favorable
it eventually does get shit done
X post by @kunchenguid. kunchenguid (Kun Chen), builder of the firstmate agent tool (stated in X bio and post), self-described, Sep 23, 2026. GPT-6 Luna at high reasoning as the firstmate agent.
- Unfavorable
Do not trust GPT-6 Luna AT ALL. Low, Med, High all fail
X post by @leeloumt. leeloumt (Liam Debono), builds Project Memory and OperAI (stated in X bio), self-described, Oct 22, 2026. GPT-6 Luna at Low, Med, High, xHigh and Max against GPT-6.1 Sol at Low on his memory project.
- Favorable
GPT-6 Luna Decisions: 79.0% → 95.2%
X post by @SaadMRP. SaadMRP (Pial), MSCS student at UIUC working on SREGym (stated in X bio), self-described, Oct 21, 2026. GPT-6 Luna through the Decisions API on 21 fault-diagnosis tasks.
- Favorable
gpt 6 Luna is a super smart model
X post by @ZackKorman. Zack Korman, cofounder of Embroidery, building AI cybersecurity tools (stated in X bio), self-described, Oct 25, 2026. GPT-6 Luna.
- Unfavorable
Give it a task and it can spend a whole afternoon and still not get it right
X post by @buildwithhassan. buildwithhassan (Hassan), software engineer who tests AI coding agents (stated in X bio), self-described, Oct 12, 2026. GPT-6 Luna as a coding agent and as a cheap helper inside Hermes, against GPT-6 Astra.
- Favorable
GPT-6 Luna did a bit better
Hacker News comment by chriddyp. chriddyp, runs Plotly's DataAnalyticsBench (stated in comment), self-described, Oct 7, 2026. GPT-6 Luna on Plotly's DataAnalyticsBench.
- Favorable
In almost all cases, GPT-6 Luna (XHigh) have been enough
Hacker News comment by Alifatisk. Alifatisk, Hacker News commenter (no role stated), self-described, Sep 29, 2026. GPT-6 Luna at XHigh in Codex.
- Unfavorable
GPT-6 Luna lost $60k on average
Hacker News comment by zodwick. zodwick, author of Cafe Bench built at Dot (stated in comment), self-described, Sep 23, 2026. GPT-6 Luna on Cafe Bench, Dot's world-simulation eval.
- Unfavorable
It also caught 32 of the 40 unanswerable questions vs Jev's 38
Hacker News comment by Bocajmai. Bocajmai, Hacker News commenter (no role stated), self-described, Oct 13, 2026. GPT-6 Luna ranking pages for a live phone agent, against Jev.
- Favorable
Even cheap models like GPT 6 Luna do decent job in programming these days
Hacker News comment by rwyinuse. rwyinuse, Hacker News commenter (no role stated), self-described, Oct 12, 2026. GPT-6 Luna for programming at their company.
- Unfavorable
Luna misses the obvious (and more importantly, the clearly explained) consistently
Hacker News comment by pessimizer. pessimizer, Hacker News commenter (no role stated), self-described, Oct 8, 2026. GPT-6 Luna reviewing abstract game rules.
- Unfavorable
Haiku 5.5 is noticeably smarter than GPT-6 Luna
Hacker News comment by dannyw. dannyw, Hacker News commenter (no role stated), self-described, Oct 7, 2026. GPT-6 Luna against Claude Haiku 5.5.
- Favorable
I have been pleasantly surprised with the writing quality of GPT 6 Luna
Hacker News comment by treetalker. treetalker, Hacker News commenter (no role stated), self-described, Oct 3, 2026. GPT-6 Luna for summarizing, rephrasing and drafting.
- Unfavorable
Luna without reasoning turned on might as well be a model from early 2025
Hacker News comment by bob1029. bob1029, Hacker News commenter (no role stated), self-described, Oct 2, 2026. GPT-6 Luna with reasoning_effort none through the Decisions API.
- Favorable
with no tradeoff in accuracy
Hacker News comment by CharlieDigital. CharlieDigital, Hacker News commenter (no role stated), self-described, Sep 29, 2026. GPT-6 Luna in batch mode, 20 records per prompt, against Jev.
- Unfavorable
I've had bad experiences with gpt 6 luna
Hacker News comment by r_lee. r_lee, Hacker News commenter (no role stated), self-described, Sep 26, 2026. GPT-6 Luna against GPT-5.6 Luna on React work.
- Favorable
which does a tremendous job of Haskell
Hacker News comment by tome. tome, Hacker News commenter (no role stated), self-described, Sep 26, 2026. GPT-6 Luna at xhigh on a Plus subscription, Haskell work.
- Favorable
Today I worked two hours with GPT 6 Luna, have been very productive tackling some hard stuff
Hacker News comment by epolanski. epolanski, Hacker News commenter (no role stated), self-described, Sep 23, 2026. GPT-6 Luna on the 20 euro GPT plan.
- Unfavorable
the results were 30/36 for gpt 6 luna, and 33/36 for gpt 5.6 luna
Hacker News comment by pjankiewicz. pjankiewicz, Hacker News commenter (no role stated), self-described, Sep 23, 2026. GPT-6 Luna on 36 agent-and-tool benchmark scenarios, against GPT-5.6 Luna.
- Favorable
GPT-6 Luna is smarter than 5.6 Terra
Hacker News comment by XCSme. XCSme, runs the aibenchy comparison site (stated in comment), self-described, Sep 23, 2026. GPT-6 Luna against GPT-5.6 Terra on aibenchy.
- Unfavorable
6-luna is no improvement over 5.6, merely a price cut
Hacker News comment by user43928. user43928, Hacker News commenter (no role stated), self-described, Sep 22, 2026. GPT-6 Luna against GPT-5.6 Luna.
- Favorable
offers similar accuracy compared to gpt-4.1-mini fine tuned for my specific task
Hacker News comment by buckwheatmilk. buckwheatmilk, Hacker News commenter (no role stated), self-described, Sep 23, 2026. GPT-6 Luna replacing a fine-tuned GPT-4.1-mini for structured outputs.
- Unfavorable
In plan mode, this produces a plan with a lot of holes
Auto model selection mode tends to always choose GPT 6 Luna. cyonix, GitHub issue author (no role stated), self-described, Oct 1, 2026. GPT-6 Luna chosen by auto model selection in plan mode.
- Favorable
GPT-6 Luna scored 23-27/30 on the same lecture
Decide the default model: Gemini 3.8 Flash vs GPT-6 Luna. stegra05, GitHub issue author (no role stated), self-described, Oct 21, 2026. GPT-6 Luna generating slide decks from a 72-slide lecture in blind-reviewed rounds.
- Unfavorable
a re-prompt/restart overstatement and unlabeled gap citations failed the factual gate
GPT 6 Luna High: corrected held-out storyline trial after #232. noelsaw1, GitHub issue author (no role stated), self-described, Sep 30, 2026. gpt-6-luna at high reasoning in Codex CLI narrating storyline cases.
- Favorable
Hand checks against the PDFs confirmed the fixes and the new rows checked
Complete + re-verify the remaining 242 Sonnet-distilled paper pages with gpt-6-luna. alejandroll10, GitHub issue author (no role stated), self-described, Oct 4, 2026. gpt-6-luna completing and verifying research paper pages.
- Favorable
GPT-6 Luna passed every deterministic task, remained close to the raw quality leaders
Model routing v2: pin GPT-6 Luna across the agent pipeline. huangyingting, GitHub issue author (no role stated), self-described, Sep 23, 2026. GPT-6 Luna (copilot) at high and xhigh effort across nine Copilot CLI agents.
- Unfavorable
and less keen to say none
Our RAG kept retrieving the wrong page, so we tested letting Jev pick pages instead. jacobsayora, developer building an AI phone receptionist, self-described, Oct 13, 2026. GPT-6 Luna picking the answering page from a list, against Jev.
Tool use and structured output 33%
- Unfavorable
It leaks tool calls and markers in the output like crazy
Hacker News comment by sunaookami. sunaookami, Hacker News commenter (no role stated), self-described, Sep 29, 2026. gpt-6-luna against gpt-5.6-terra.
- Favorable
I've been using GPT-6 Luna in some capacity for nearly all my agent workflows
Hacker News comment by swalsh. swalsh, Hacker News commenter (no role stated), self-described, Oct 7, 2026. GPT-6 Luna in agent workflows.
- Favorable
I run some agents, mostly on Luna 6 right now. It's just tool use, web browsing, etc
Hacker News comment by pkulak. pkulak, Hacker News commenter (no role stated), self-described, Oct 7, 2026. GPT-6 Luna (written as Luna 6) running tool-using agents.
- Favorable
perfectly capable of returning structured JSON
Hacker News comment by waximabbax. waximabbax, Hacker News commenter (no role stated), self-described, Sep 26, 2026. GPT-6 Luna among cheap LLMs, against Jev.
- Unfavorable
still does not work through Logos with Claude Code. Every turn fails with
Logos: gpt-6-luna rejects function tools combined with reasoning_effort on /v1/chat/completions. wasnertobias, GitHub issue author (no role stated), self-described, Oct 12, 2026. gpt-6-luna through the Logos gateway with Claude Code, function tools plus reasoning_effort on Chat Completions.
- Unfavorable
switching Rancher AI v1.1.0 from GPT-5.6 Luna to GPT-6 Luna causes chat requests to fail with HTTP 400
Support GPT-6 Luna tool calling with the OpenAI Responses API. alvarogomez93, GitHub issue author (no role stated), self-described, Sep 23, 2026. GPT-6 Luna replacing GPT-5.6 Luna in Rancher AI v1.1.0, Chat Completions with tools.
- Unfavorable
The model only accepts tools when `reasoning_effort` is sent explicitly as `"none"`
Combo fallback to gpt-6-luna fails with 400 when tools + reasoning_effort are sent. pitupi, GitHub issue author (no role stated), self-described, Oct 12, 2026. gpt-6-luna as an OmniRoute fallback step for an agent sending tools and reasoning_effort high.
- Unfavorable
fails tool-call discipline in **both directions**
gpt-6-luna tool-call discipline fails both directions. mrwogu, GitHub issue author (no role stated), self-described, Oct 7, 2026. gpt-6-luna behind hindsight-api-slim 0.10.2 with tool_choice required.
- Unfavorable
the agent often ends a turn after announcing its next step, without doing it
Agent ends a turn early: submit batched with its first tool call (Codex gpt-6-luna). JaimeCernuda, GitHub issue author (no role stated), self-described, Sep 30, 2026. gpt-6-luna on Codex Direct with CLIO's text-mode tool protocol.
- Unfavorable
GPT-6 Luna excluded: its bug run searched the web with search turned off
X post on gpt-6-luna test behavior. Tested Stack, independent AI tool-testing account, self-described, Oct 21, 2026. GPT-6 Luna on the publisher's bug-fix and control runs.
Long context 20%
- Favorable
An earlier compaction threshold substantially reduced that amplification in this test without preventing the model from completing the same style of repository audit and follow-up verification.
Adaptive compaction threshold for large-context GPT-6 Luna Low agentic workloads. inglesenburgos, GitHub issue author (no role stated), self-described, Sep 25, 2026. GPT-6 Luna at Low reasoning in Kilo Code 7.7.7, against GPT-5.6 Luna Low.
- Unfavorable
the compaction gives error for gpt-6 luna even though it is not at max context lenght
[Bug] Gpt-6 luna compaction error. suse-coder, GitHub issue author (no role stated), self-described, Sep 24, 2026. GPT-6 Luna in opencode v2.0.16.
- Unfavorable
Luna at max reasoning effort, even though its not optimal for long conversations
Comment on: GPT-6 Sol and Luna. Alifatisk, Hacker News commenter, self-described, Sep 22, 2026. GPT-6 Luna at max reasoning on long conversations, discussed on its release thread.
- Unfavorable
Luna uses was more tokens and fills the context up way to fast
Comment on: GPT-6 Sol and Luna. o_m, Hacker News commenter, self-described, Sep 22, 2026. GPT-6 Luna in back-and-forth sessions, discussed on its release thread.
- Unfavorable
Needle recall at 690K–909K is inconsistent on Sol and Luna, even though acceptance is consistent.
Live receipts: GPT-6 Sol and Luna context acceptance and recall. myagizmaktav, Hermes agent issue reporter who ran live context tests, self-described, Sep 28, 2026. GPT-6 Luna on Codex OAuth at low reasoning, tested at 689,512 input tokens.
Developer experience 25%
- Unfavorable
using it interactively feels totally unusable because you have to wait for many turns before a useful outcome comes back
X post by @kunchenguid. kunchenguid (Kun Chen), builder of the firstmate agent tool (stated in X bio and post), self-described, Sep 23, 2026. GPT-6 Luna at high reasoning as the firstmate agent.
- Unfavorable
it ignores your instructions and does its own thing. No matter how many constraints you give it.
X post by @buildwithhassan. buildwithhassan (Hassan), software engineer who tests AI coding agents (stated in X bio), self-described, Oct 12, 2026. GPT-6 Luna as a coding agent and as a cheap helper inside Hermes, against GPT-6 Astra.
- Favorable
i enjoy it a lot more if i use a super fast model with low reasoning (gpt 6 luna low effort + fast mode)
Hacker News comment by humlex. humlex, Hacker News commenter (no role stated), self-described, Sep 26, 2026. GPT-6 Luna at low effort with fast mode.
- Unfavorable
it "helpfully" included a third paragraph to point out that the author is opinionated
Hacker News comment by pelican0. pelican0, Hacker News commenter (no role stated), self-described, Oct 12, 2026. GPT-6 Luna summarizing an article.
- Favorable
I dont have to do anything except change it to gpt-6-luna
Hacker News comment by dhdsingfgg. dhdsingfgg, Hacker News commenter (no role stated), self-described, Sep 22, 2026. GPT-6 Luna as a drop-in replacement.
- Unfavorable
OpenAI models (Luna, and even Sol) seem to work noticeably worse in opencode than they do in Codex
Collapse repeated encrypted Thought lines from GPT-6 Luna/Sol; review Luna session quality. brentyates-swx, GitHub issue author (no role stated), self-described, Sep 26, 2026. GPT-6 Luna via swx-azure in the opencode TUI.
- Unfavorable
Cannot utilize GPT 6 Luna model with OpenAI API Key in Local Mode
Cannot utilize GPT 6 Luna model with OpenAI API Key in Local Mode. aaronge-2020, GitHub issue author (no role stated), self-described, Oct 23, 2026. GPT-6 Luna with an OpenAI API key in AnkiBrain Local Mode.
- Unfavorable
setting `ASSISTANT_MODEL=gpt-6-luna` does not bring the assistant back on its own
AI Formula Assistant: default gpt-4o is removed and the named replacement refuses temperature 0. Lakshya-0910 (Lakshya), building a tool that checks model migrations (stated in issue), self-described, Oct 26, 2026. gpt-6-luna as the named replacement for gpt-4o-2024-08-06, Chat Completions with temperature 0.
Cost 79%
- Favorable
Somehow GPT-6 Luna is half the price of that again
Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna, and a new price war. Simon Willison, independent developer and Datasette creator (stated on his weblog), self-described, Sep 22, 2026. GPT-6 Luna running the Datasette Agent at agent.datasette.io.
- Favorable
20x cheaper than Opus 5.5 for high-volume routing
X post by @alighodsi. Ali Ghodsi, CEO and co-founder of Databricks, as listed on the Databricks Data + AI Summit Tuesday Keynote session page (read Oct 26, 2026) (role source), Sep 28, 2026. GPT-6 Luna in Databricks' internal model test across 2,400 engineers. A partner account: the author's company sells or serves this model, so it counts toward no figure.
- Favorable
It was at least 20 times cheaper per-task than Opus 5.5 in every offline benchmark we tested and in observed online use
X post by @pwendell. Patrick Wendell, co-founder and VP of engineering at Databricks, as listed on the Databricks Data + AI Summit Tuesday Keynote session page (read Oct 26, 2026) (role source), Sep 28, 2026. GPT-6 Luna in Databricks' online workload analysis of 2,400 engineers plus offline evals, routed through Unity Gateway. A partner account: the author's company sells or serves this model, so it counts toward no figure.
- Favorable
it's insanely cheap, even with the point below considered
X post by @kunchenguid. kunchenguid (Kun Chen), builder of the firstmate agent tool (stated in X bio and post), self-described, Sep 23, 2026. GPT-6 Luna at high reasoning as the firstmate agent.
- Favorable
costing a tiny fraction of what opus 4.5 cost a year ago
X post by @ZackKorman. Zack Korman, cofounder of Embroidery, building AI cybersecurity tools (stated in X bio), self-described, Oct 25, 2026. GPT-6 Luna.
- Favorable
was about 30% the cost of Haiku 5.5
Hacker News comment by chriddyp. chriddyp, runs Plotly's DataAnalyticsBench (stated in comment), self-described, Oct 7, 2026. GPT-6 Luna on Plotly's DataAnalyticsBench.
- Favorable
its dirt cheap, yet highly capable
Hacker News comment by Alifatisk. Alifatisk, Hacker News commenter (no role stated), self-described, Sep 29, 2026. GPT-6 Luna at XHigh in Codex.
- Favorable
My company has largely switched to Luna after Microsoft raised prices for Copilot
Hacker News comment by rwyinuse. rwyinuse, Hacker News commenter (no role stated), self-described, Oct 12, 2026. GPT-6 Luna for programming at their company.
- Favorable
it's definitely very cheap
Hacker News comment by pessimizer. pessimizer, Hacker News commenter (no role stated), self-described, Oct 8, 2026. GPT-6 Luna reviewing abstract game rules.
- Unfavorable
gpt-6-luna was sneakily added to the 1 mio free tokens group instead of 10 mio
Hacker News comment by sunaookami. sunaookami, Hacker News commenter (no role stated), self-described, Sep 29, 2026. gpt-6-luna against gpt-5.6-terra.
- Favorable
a GBP 20 per month Plus subscription gets me basically all the GPT-6-Luna-xhigh I can eat
Hacker News comment by tome. tome, Hacker News commenter (no role stated), self-described, Sep 26, 2026. GPT-6 Luna at xhigh on a Plus subscription, Haskell work.
- Favorable
gpt 6 Luna can be swapped for 5.6 terra or even sol in some cases, and it is much cheaper
Hacker News comment by moomoo11. moomoo11, Hacker News commenter (no role stated), self-described, Sep 26, 2026. GPT-6 Luna on their own evals against GPT-5.6 Terra and Sol.
- Favorable
my weekly usage went from 57% to 56%
Hacker News comment by epolanski. epolanski, Hacker News commenter (no role stated), self-described, Sep 23, 2026. GPT-6 Luna on the 20 euro GPT plan.
- Unfavorable
Apparently v4.1 flash uses 1.75 times as many tokens so still cheaper
Hacker News comment by KoolKat23. KoolKat23, Hacker News commenter (no role stated), self-described, Sep 23, 2026. GPT-6 Luna through Nous, cache-heavy usage, against DeepSeek v4 and v4.1 Flash.
- Favorable
comparing Deepseek models to Luna, and to my surprise Luna won
Hacker News comment by pavo-etc. pavo-etc, Hacker News commenter (no role stated), self-described, Sep 29, 2026. GPT-6 Luna in the pi-msg agent harness against DeepSeek Flash models.
- Unfavorable
it seems to be about twice as expensive as DS v4.1 Flash
Hacker News comment by dools. dools, Hacker News commenter (no role stated), self-described, Sep 29, 2026. GPT-6 Luna against DeepSeek v4.1 Flash.
- Favorable
Luna comes out a fair amount cheaper due to more efficient tokenization and less outputs
Hacker News comment by Farmadupe. Farmadupe, Hacker News commenter (no role stated), self-described, Oct 8, 2026. GPT-6 Luna on classification, batch and synchronous, against Claude Haiku 5.5.
- Favorable
and the pricing is cheap
Hacker News comment by swalsh. swalsh, Hacker News commenter (no role stated), self-described, Oct 7, 2026. GPT-6 Luna in agent workflows.
- Favorable
not much can compete with GPT-6 Luna on cost / performance
Hacker News comment by senordevnyc. senordevnyc, Hacker News commenter (no role stated), self-described, Sep 28, 2026. GPT-6 Luna in AI pipelines in their product.
- Favorable
and costs *15x* less [0]
Hacker News comment by XCSme. XCSme, runs the aibenchy comparison site (stated in comment), self-described, Sep 23, 2026. GPT-6 Luna against GPT-5.6 Terra on aibenchy.
- Favorable
GPT-6 Luna being half the price of GPT-5.6 Luna is a really big deal
Hacker News comment by simonw. simonw (Simon Willison), independent developer (stated in profile), self-described, Sep 22, 2026. GPT-6 Luna pelican tests across reasoning efforts.
- Favorable
my monthly token cost is going to be down atleast 50%
Hacker News comment by dhdsingfgg. dhdsingfgg, Hacker News commenter (no role stated), self-described, Sep 22, 2026. GPT-6 Luna as a drop-in replacement.
- Favorable
Love the cost savings
Hacker News comment by sfblah. sfblah, Hacker News commenter (no role stated), self-described, Sep 22, 2026. GPT-6 Luna on classification jobs.
- Favorable
gpt-6-luna, has really competitive pricing
Hacker News comment by buckwheatmilk. buckwheatmilk, Hacker News commenter (no role stated), self-described, Sep 23, 2026. GPT-6 Luna replacing a fine-tuned GPT-4.1-mini for structured outputs.
- Unfavorable
The additional tokens wipe out the cost savings for us
Hacker News comment by anelson. anelson, Hacker News commenter (no role stated), self-described, Sep 26, 2026. GPT-6 Luna against GPT-5.6 Luna on internal cybersecurity evals.
- Unfavorable
That caused context to grow much faster, which then amplified repeated cache reads on every model -> tool -> model cycle
Adaptive compaction threshold for large-context GPT-6 Luna Low agentic workloads. inglesenburgos, GitHub issue author (no role stated), self-described, Sep 25, 2026. GPT-6 Luna at Low reasoning in Kilo Code 7.7.7, against GPT-5.6 Luna Low.
- Unfavorable
it will run the 5-hour limit out in less than 10 minutes using still the same GPT 6 Luna Medium
Excessive Model Usage. pietertal, GitHub issue author (no role stated), self-described, Oct 19, 2026. GPT-6 Luna Medium in Codex on a Plus plan.
- Favorable
at $0.03-0.04 per run, against $0.45-0.86 for Gemini
Decide the default model: Gemini 3.8 Flash vs GPT-6 Luna. stegra05, GitHub issue author (no role stated), self-described, Oct 21, 2026. GPT-6 Luna generating slide decks from a 72-slide lecture in blind-reviewed rounds.
- Favorable
and was dramatically cheaper
Model routing v2: pin GPT-6 Luna across the agent pipeline. huangyingting, GitHub issue author (no role stated), self-described, Sep 23, 2026. GPT-6 Luna (copilot) at high and xhigh effort across nine Copilot CLI agents.
- Favorable
Above 100,000 tokens, Luna looks like a much better deal.
Claude Haiku 5.5. Simon Willison, maintainer of the llm-anthropic plugin, self-described, Oct 7, 2026. GPT-6 Luna pricing against Claude Haiku 5.5.
Speed 45%
- Favorable
it seems to be fast and competent at both SQL queries
Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna, and a new price war. Simon Willison, independent developer and Datasette creator (stated on his weblog), self-described, Sep 22, 2026. GPT-6 Luna running the Datasette Agent at agent.datasette.io.
- Unfavorable
it's extremely slow, not in terms of time to first token or toks/sec, but how many turns it takes to get something done
X post by @kunchenguid. kunchenguid (Kun Chen), builder of the firstmate agent tool (stated in X bio and post), self-described, Sep 23, 2026. GPT-6 Luna at high reasoning as the firstmate agent.
- Unfavorable
xHigh and Max take WAY too long (30 mins) compared to 6.1 Sol that completes the same thing at Low thinking in 4 minutes
X post by @leeloumt. leeloumt (Liam Debono), builds Project Memory and OperAI (stated in X bio), self-described, Oct 22, 2026. GPT-6 Luna at Low, Med, High, xHigh and Max against GPT-6.1 Sol at Low on his memory project.
- Unfavorable
Luna took 0.66s at at the median and 1.27s at the p95. Versus 0.28s and 0.42s with Jev
Hacker News comment by Bocajmai. Bocajmai, Hacker News commenter (no role stated), self-described, Oct 13, 2026. GPT-6 Luna ranking pages for a live phone agent, against Jev.
- Favorable
GPT 6 Luna in batch mode at 20 records per batch was about 1.6x faster
Hacker News comment by CharlieDigital. CharlieDigital, Hacker News commenter (no role stated), self-described, Sep 29, 2026. GPT-6 Luna in batch mode, 20 records per prompt, against Jev.
- Favorable
dont have to wait around for 20minutes
Hacker News comment by humlex. humlex, Hacker News commenter (no role stated), self-described, Sep 26, 2026. GPT-6 Luna at low effort with fast mode.
- Favorable
compared to GPT-6 luna it is much slower
Hacker News comment by XCSme. XCSme, runs the aibenchy comparison site (stated in comment), self-described, Oct 7, 2026. GPT-6 Luna against Mistral Large 4 on aibenchy.
- Favorable
In my testing so far, I'm seeing 160-175ms end to end
Hacker News comment by agentdev001. agentdev001, Hacker News commenter (no role stated), self-described, Oct 7, 2026. GPT-6 Luna behind the Decisions API.
- Unfavorable
6-Luna uses way more tokens to produce the same result at a given thinking effort, and takes longer to do so
Hacker News comment by anelson. anelson, Hacker News commenter (no role stated), self-described, Sep 26, 2026. GPT-6 Luna against GPT-5.6 Luna on internal cybersecurity evals.
- Unfavorable
roughly twice the throughput of Luna and far lower latency
Hacker News comment by Topfi. Topfi, Hacker News commenter (no role stated), self-described, Oct 7, 2026. GPT-6 Luna on OpenRouter against Claude Haiku 5.5.
- Unfavorable
GPT-6 Luna picking from the same list tied it on ranking, but was more than twice as slow
Our RAG kept retrieving the wrong page, so we tested letting Jev pick pages instead. jacobsayora, developer building an AI phone receptionist, self-described, Oct 13, 2026. GPT-6 Luna picking the answering page from a list, against Jev.
Reliability Only concerns on record
- Unfavorable
I'm seeing some crappy error rates on the Openai models even on 6.1 sol and 6.0 luna
Comment on 'Are GPT 5.6 models down?' in r/aws. Professional-Sir7048, uses Amazon Bedrock at work (stated in comment), self-described, Oct 5, 2026. GPT-6 Luna (written as 6.0 luna) on Amazon Bedrock.
- Unfavorable
when Luna hits the 60 s fallback timeout, no backend answers and the launch is refused
Swarm tick classifier: one Luna timeout blocks launches. nestorcolt, GitHub issue author (no role stated), self-described, Oct 7, 2026. gpt-6-luna through Codex as the swarm classifier fallback.
- Unfavorable
every chat request that runs `openai.gpt-6-luna` returns an `error` event
Production: every GPT-6 Luna request fails, so NWB, HED and BIDS return errors. bendichter, GitHub issue author (no role stated), self-described, Oct 6, 2026. openai.gpt-6-luna on Amazon Bedrock as the default chat model.
- Unfavorable
gpt-6-luna has been nerfed
X post on gpt-6-luna test behavior. Roberto Mello, self-described longtime Postgres writer, Oct 24, 2026. GPT-6 Luna behavior over time.
- Unfavorable
GPT-6 Luna excluded: its bug run searched the web with search turned off, and its control run was unavailable.
X post on gpt-6-luna test behavior. Tested Stack, independent AI tool-testing account, self-described, Oct 21, 2026. GPT-6 Luna on the publisher's bug-fix and control runs.
- Unfavorable
Even GPT 6 Luna is out of capacity on Codex now!
GPT-6 Luna at capacity in Codex. The Vibe Coder (quantumaidev), X author using Codex, self-described, Oct 19, 2026. GPT-6 Luna in Codex.