Compare · ModelsLive · 3 picked · head to head
GPT-5.1 vs Kimi K2 0711 vs o3
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GPT-5.1 wins on 16/23 benchmarks
GPT-5.1 wins 16 of 23 shared benchmarks. Leads in coding · language · math.
Category leads
coding·GPT-5.1knowledge·o3language·GPT-5.1math·GPT-5.1reasoning·GPT-5.1
Hype vs Reality
Attention vs performance
GPT-5.1
#97 by perf·no signal
Kimi K2 0711
#63 by perf·no signal
o3
#69 by perf·no signal
Best value
Kimi K2 0711
3.5x better value than o3
GPT-5.1
8.8 pts/$
$5.63/M
Kimi K2 0711
39.2 pts/$
$1.43/M
o3
11.0 pts/$
$5.00/M
Vendor risk
Who is behind the model
OpenAI
$840.0B·Tier 1
moonshotai
private · undisclosed
OpenAI
$840.0B·Tier 1
Head to head
23 benchmarks · 3 models
GPT-5.1Kimi K2 0711o3
GSO-Bench
GPT-5.1 leads by +4.9
GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues.
GPT-5.1
13.7
Kimi K2 0711
4.9
o3
8.8
HELM · GPQA
o3 leads by +10.1
GPT-5.1
44.2
Kimi K2 0711
65.2
o3
75.3
HELM · IFEval
GPT-5.1 leads by +6.6
GPT-5.1
93.5
Kimi K2 0711
85.0
o3
86.9
HELM · MMLU-Pro
o3 leads by +4.0
GPT-5.1
57.9
Kimi K2 0711
81.9
o3
85.9
HELM · Omni-MATH
o3 leads by +6.0
GPT-5.1
46.4
Kimi K2 0711
65.4
o3
71.4
HELM · WildBench
GPT-5.1 leads by +0.1
GPT-5.1
86.3
Kimi K2 0711
86.2
o3
86.1
SimpleBench
GPT-5.1 leads by +0.1
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
GPT-5.1
43.8
Kimi K2 0711
11.6
o3
43.7
WeirdML
GPT-5.1 leads by +8.4
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
GPT-5.1
60.8
Kimi K2 0711
39.4
o3
52.4
Aider polyglot
o3 leads by +22.2
Aider Polyglot · measures how well AI models can edit code across multiple programming languages using the Aider coding assistant framework.
Kimi K2 0711
59.1
o3
81.3
ARC-AGI
GPT-5.1 leads by +12.0
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
GPT-5.1
72.8
o3
60.8
ARC-AGI-2
GPT-5.1 leads by +11.1
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
GPT-5.1
17.6
o3
6.5
Fiction.LiveBench
o3 leads by +27.8
Fiction.LiveBench · a continuously updated benchmark using recently published fiction to test reading comprehension and reasoning, preventing data contamination.
Kimi K2 0711
61.1
o3
88.9
FrontierMath-2025-02-28-Private
GPT-5.1 leads by +12.3
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
GPT-5.1
31.0
o3
18.7
FrontierMath-Tier-4-2025-07-01-Private
GPT-5.1 leads by +10.4
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
GPT-5.1
12.5
o3
2.1
GPQA diamond
GPT-5.1 leads by +7.7
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
GPT-5.1
83.5
o3
75.8
HLE
GPT-5.1 leads by +3.5
HLE (Humanity's Last Exam) · a reasoning benchmark designed to be the hardest public evaluation of AI. Questions span mathematics, physics, philosophy, and logic · curated to be at or beyond the frontier of human expert capability. Tested with and without tool augmentation. Claude Opus 4.7 scores 46.9% without tools and 54.7% with tools · making it one of the few benchmarks where the top score is below 60%.
GPT-5.1
19.8
o3
16.3
Lech Mazur Writing
Kimi K2 0711 leads by +3.0
Lech Mazur Writing · evaluates creative writing ability, assessing prose quality, narrative coherence, and stylistic sophistication.
Kimi K2 0711
86.9
o3
83.9
OTIS Mock AIME 2024-2025
GPT-5.1 leads by +4.7
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
GPT-5.1
88.6
o3
83.9
SimpleQA Verified
o3 leads by +4.1
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
GPT-5.1
48.9
o3
53.0
SWE-Bench verified
GPT-5.1 leads by +5.7
SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability.
GPT-5.1
68.0
o3
62.3
SWE-Bench Verified (Bash Only)
GPT-5.1 leads by +7.6
SWE-Bench Verified (Bash Only) · a curated subset of SWE-bench where models fix real Python repository bugs using only bash commands, no agent frameworks.
GPT-5.1
66.0
o3
58.4
Terminal Bench
GPT-5.1 leads by +19.8
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence.
GPT-5.1
47.6
Kimi K2 0711
27.8
VPCT
GPT-5.1 leads by +10.0
VPCT (Visual Pattern Completion Test) · tests visual reasoning and pattern recognition by having models complete visual sequences and transformations.
GPT-5.1
38.0
o3
28.0
Full benchmark table
| Benchmark | GPT-5.1 | Kimi K2 0711 | o3 |
|---|---|---|---|
GSO-Bench GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues. | 13.7 | 4.9 | 8.8 |
HELM · GPQA | 44.2 | 65.2 | 75.3 |
HELM · IFEval | 93.5 | 85.0 | 86.9 |
HELM · MMLU-Pro | 57.9 | 81.9 | 85.9 |
HELM · Omni-MATH | 46.4 | 65.4 | 71.4 |
HELM · WildBench | 86.3 | 86.2 | 86.1 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 43.8 | 11.6 | 43.7 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 60.8 | 39.4 | 52.4 |
Aider polyglot Aider Polyglot · measures how well AI models can edit code across multiple programming languages using the Aider coding assistant framework. | — | 59.1 | 81.3 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 72.8 | — | 60.8 |
ARC-AGI-2 ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data. | 17.6 | — | 6.5 |
Fiction.LiveBench Fiction.LiveBench · a continuously updated benchmark using recently published fiction to test reading comprehension and reasoning, preventing data contamination. | — | 61.1 | 88.9 |
FrontierMath-2025-02-28-Private FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning. | 31.0 | — | 18.7 |
FrontierMath-Tier-4-2025-07-01-Private FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning. | 12.5 | — | 2.1 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 83.5 | — | 75.8 |
HLE HLE (Humanity's Last Exam) · a reasoning benchmark designed to be the hardest public evaluation of AI. Questions span mathematics, physics, philosophy, and logic · curated to be at or beyond the frontier of human expert capability. Tested with and without tool augmentation. Claude Opus 4.7 scores 46.9% without tools and 54.7% with tools · making it one of the few benchmarks where the top score is below 60%. | 19.8 | — | 16.3 |
Lech Mazur Writing Lech Mazur Writing · evaluates creative writing ability, assessing prose quality, narrative coherence, and stylistic sophistication. | — | 86.9 | 83.9 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 88.6 | — | 83.9 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 48.9 | — | 53.0 |
SWE-Bench verified SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability. | 68.0 | — | 62.3 |
SWE-Bench Verified (Bash Only) SWE-Bench Verified (Bash Only) · a curated subset of SWE-bench where models fix real Python repository bugs using only bash commands, no agent frameworks. | 66.0 | — | 58.4 |
Terminal Bench Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence. | 47.6 | 27.8 | — |
VPCT VPCT (Visual Pattern Completion Test) · tests visual reasoning and pattern recognition by having models complete visual sequences and transformations. | 38.0 | — | 28.0 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.25 | $10.00 | 400K tokens (~200 books) | $34.38 | |
| $0.57 | $2.30 | 131K tokens (~66 books) | $10.03 | |
| $2.00 | $8.00 | 200K tokens (~100 books) | $35.00 |