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A head-to-head benchmark comparison of Kimi K2 vs GPT-5 across 59 evaluations. GPT-5 leads on the most benchmarks (39).
Kimi K2 is our latest Mixture-of-Experts model with 32 billion activated parameters and 1 trillion total parameters. It achieves state-of-the-art performance in.
Full Kimi K2 specs →"We are introducing GPT‑5, our best AI system yet. GPT‑5 is a significant leap in intelligence over all our previous models, featuring state-of-the-art performa.
Full GPT-5 specs →| Benchmark | Kimi K2 | GPT-5 |
|---|---|---|
| agentic | ||
| BrowseComp | 14.1% | 54.9% |
| BrowseComp-Zh | 28.8 | 63 |
| FinSearchComp-T3 | 10.4 | — |
| GAIA | 57.7 | — |
| MCP-Mark | — | 50.9 |
| MCP-Universe | — | 47.9 |
| Seal-0 | 25.2 | — |
| TAU-bench Airline | 51.2% | — |
| TAU-bench Retail | 73.9% | — |
| Terminal-Bench 2.0 | 44.5% | 35.2% |
| Tool-Decathlon | — | 29 |
| WebWalkerQA | 63 | — |
| xbench-DeepSearch | 50 | — |
| τ²-Bench (TAU2) | — | 80.2 |
| code | ||
| Codeforces Elo | 1,574 | 2,537 |
| FullStackBench | 54% | 50.92% |
| HumanEval | 87.1% | — |
| LiveCodeBench | 48.95% | 84.5% |
| LiveCodeBench v5 | — | 82.68% |
| LiveCodeBench v6 | 56.1% | 80.6% |
| MBPP | 89.96% | 91.72% |
| SWE-bench Verified | 69.2% | 74.9% |
| SciCode | 30.7% | — |
| coding | ||
| Aider Benchmark | 85.34 | 95.49 |
| Multi-SWE-bench | 33.5 | — |
| MultiPL-E | 73.54 | 76.66 |
| OJ-Bench (cpp) | 25.5 | — |
| SWE-Bench Multilingual | 55.9 | 55.3 |
| general | ||
| ArenaHard v2 | 69.88 | 82.91 |
| C-Eval | 91.12% | 88.27% |
| IFEval | 90.99% | 95.38% |
| Longform Writing | 62.8 | — |
| MMLU | 89.5% | — |
| MMLU-Pro | 81.9% | 87.5% |
| MMLU-Redux | 92.7% | 92.75% |
| MultiChallenge | 54.1% | 69.6% |
| knowledge | ||
| AGIEval | — | 88.56 |
| CMMLU | — | 87.09 |
| TriviaQA | — | 86.32 |
| long-context | ||
| Frames | 72 | — |
| math | ||
| AIME 2024 | 67.24% | 94.78% |
| AIME 2025 | 75.2% | 94.6% |
| CNMO 2024 | 68.92% | 87.93% |
| HMMT February 2025 | 70.4% | 93.3% |
| HMMT November 2025 | — | 89.2 |
| IMOAnswerBench | 45.8 | 76 |
| Omni-MATH | 62.42 | 82.9 |
| Omni-MATH-HARD | — | 73.61% |
| UGMathBench | 69.97 | 80.18 |
| reasoning | ||
| ARC-AGI-1 | 22.19 | 65.7 |
| BBEH | 34.83 | 72.78 |
| FinanceReasoning | 84.83 | 89.33 |
| GPQA Diamond | 74.2% | 86.05% |
| Humanity's Last Exam | 21.7% | 35.2% |
| ZebraLogic | 85.5% | 98% |
| safety | ||
| HealthBench | 43.8% | 67.2% |
| SimpleQA | 31% | — |
| science | ||
| PhyBench | — | 48.53 |
| tool-use | ||
| BFCL v3 | 71.1% | 57.21% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
Across the 59 benchmarks compared here, GPT-5 leads on the most (39). The right choice still depends on your task — check the per-benchmark table above for coding, reasoning and math.
GPT-5 has the strongest result on Codeforces Elo among the models compared here. See the code-category rows in the table for the full picture.
This comparison covers 59 benchmarks on which at least one of the selected models has a published score.
Scores are aggregated from official model cards, technical reports and standard public evaluations, and link back to each benchmark's source. They are updated as new results are published.
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