Language Proprietary
- Params
- β
- Context
- β
- Released
- Jun 2025
Gemini 2.5 Pro release date (per deprecations table)..
Full gemini-2.5-pro specs β
π
New: connect Claude & other AIs to GenAIList over MCP β research the catalog and contribute to the shared knowledge base. Learn how β
// COMPARISON
gemini-2.5-pro vs Kimi K2 Thinking
A head-to-head benchmark comparison of gemini-2.5-pro vs Kimi K2 Thinking across 56 evaluations. gemini-2.5-pro leads on the most benchmarks (32).
The models
Language Open (Restricted)
- Params
- 1T
- Context
- β
- Released
- Nov 2025
Today, we are introducing Kimi K2 Thinking, our best open-source thinking model. Built as a thinking agent, it reasons step by step while using tools, achieving.
Full Kimi K2 Thinking specs βBenchmark comparison
| Benchmark | gemini-2.5-pro | Kimi K2 Thinking |
|---|---|---|
| agentic | ||
| BrowseComp | 7.6% | 60.2% |
| BrowseComp-Zh | β | 62.3 |
| FinSearchComp-T3 | β | 47.4 |
| MCP-Mark | β | 20.4 |
| MCP-Universe | β | 35.6 |
| Seal-0 | β | 56.3 |
| TAU-bench Airline | 60% | β |
| TAU-bench Retail | 77% | β |
| Terminal-Bench 2.0 | 25.3% | 47.1% |
| Tool-Decathlon | β | 17.6 |
| ΟΒ²-Bench (TAU2) | β | 74.3 |
| code | ||
| Codeforces Elo | 1,837 | β |
| FullStackBench | 48.19% | β |
| LiveCodeBench | 80.1% | 82.6% |
| LiveCodeBench v5 | 69.1% | β |
| LiveCodeBench v6 | 70.65% | 83.1% |
| MBPP | 91.01% | β |
| SWE-bench Verified | 49% | 71.3% |
| SciCode | 42.8% | 44.8% |
| coding | ||
| Aider Benchmark | 94.36 | β |
| Multi-SWE-bench | β | 41.9 |
| MultiPL-E | 71.48 | β |
| OJ-Bench (cpp) | β | 48.7 |
| SWE-Bench Multilingual | β | 61.1 |
| general | ||
| ArenaHard v2 | 79.2 | β |
| C-Eval | 90.14% | β |
| IFEval | 90.8% | β |
| Longform Writing | β | 73.8 |
| MMLU | 91.9% | β |
| MMLU-Pro | 86.2% | 84.6% |
| MMLU-Redux | 94.67% | 94.4% |
| MultiChallenge | 57.5% | β |
| knowledge | ||
| AGIEval | 88.99 | β |
| CMMLU | 88.83 | β |
| TriviaQA | 84.45 | β |
| long-context | ||
| Frames | β | 87 |
| math | ||
| AIME 2024 | 92% | β |
| AIME 2025 | 88% | 100% |
| CNMO 2024 | 80.64% | β |
| HMMT February 2025 | 82.5% | 97.5% |
| HMMT November 2025 | β | 89.2 |
| IMOAnswerBench | β | 78.6 |
| MATH-500 | 96.7% | β |
| Omni-MATH | 82.14 | β |
| Omni-MATH-HARD | 69.36% | β |
| UGMathBench | 74.5 | β |
| reasoning | ||
| ARC-AGI-1 | 45.44 | β |
| BBEH | 51.51 | β |
| FinanceReasoning | 87.33 | β |
| GPQA Diamond | 86.4% | 84.5% |
| Humanity's Last Exam | 21.6% | 51% |
| ZebraLogic | 92.4% | β |
| safety | ||
| HealthBench | 49.39% | 58% |
| SimpleQA | 54% | β |
| science | ||
| PhyBench | 55.01 | β |
| tool-use | ||
| BFCL v3 | 63.31% | β |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
Frequently asked questions
Which model is best, gemini-2.5-pro vs Kimi K2 Thinking? βΆ
Across the 56 benchmarks compared here, gemini-2.5-pro leads on the most (32). The right choice still depends on your task β check the per-benchmark table above for coding, reasoning and math.
Which of these models is best for coding? βΆ
gemini-2.5-pro has the strongest result on Codeforces Elo among the models compared here. See the code-category rows in the table for the full picture.
How many benchmarks are compared? βΆ
This comparison covers 56 benchmarks on which at least one of the selected models has a published score.
Where do the benchmark scores come from? βΆ
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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