Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Kimi K2.5 (Reasoning)
76
Winner · 4/8 categoriesQwen3.6 Plus
69
3/8 categoriesKimi K2.5 (Reasoning)· Qwen3.6 Plus
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. Qwen3.6 Plus only becomes the better choice if agentic is the priority or you need the larger 1M context window.
Kimi K2.5 (Reasoning) is clearly ahead on the aggregate, 76 to 69. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in reasoning, where it averages 74.3 against 62. The single biggest benchmark swing on the page is DeepPlanning, 14.3% to 41.5%. Qwen3.6 Plus does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Qwen3.6 Plus gives you the larger context window at 1M, compared with 128K for Kimi K2.5 (Reasoning).
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | Kimi K2.5 (Reasoning) | Qwen3.6 Plus |
|---|---|---|
| AgenticQwen3.6 Plus wins | ||
| Terminal-Bench 2.0 | 50.8% | 61.6% |
| BrowseComp | 60.6% | — |
| OSWorld-Verified | 63.3% | 62.5% |
| DeepPlanning | 14.3% | 41.5% |
| Claw-Eval | — | 58.7% |
| QwenClawBench | — | 57.2% |
| QwenWebBench | — | 1502 |
| TAU3-Bench | — | 70.7% |
| VITA-Bench | — | 44.3% |
| Toolathlon | — | 39.8% |
| MCP Atlas | — | 48.2% |
| MCP-Tasks | — | 74.1% |
| WideResearch | — | 74.3% |
| CodingKimi K2.5 (Reasoning) wins | ||
| HumanEval | 99% | — |
| SWE-bench Verified | 76.8% | 78.8% |
| LiveCodeBench | 85% | — |
| SWE-bench Pro | 70% | 56.6% |
| SWE-Rebench | 57.4% | — |
| SWE Multilingual | — | 73.8% |
| LiveCodeBench v6 | — | 87.1% |
| NL2Repo | — | 37.9% |
| Multimodal & GroundedQwen3.6 Plus wins | ||
| MMMU-Pro | 78.5% | 78.8% |
| OfficeQA Pro | 77% | — |
| MMMU | — | 86.0% |
| RealWorldQA | — | 85.4% |
| OmniDocBench 1.5 | — | 91.2% |
| Video-MME (with subtitle) | — | 87.8% |
| Video-MME (w/o subtitle) | — | 84.2% |
| MathVision | — | 88.0% |
| We-Math | — | 89.0% |
| DynaMath | — | 88.0% |
| MStar | — | 83.3% |
| SimpleVQA | — | 67.3% |
| ChatCVQA | — | 81.5% |
| MMLongBench-Doc | — | 62.0% |
| CC-OCR | — | 83.4% |
| AI2D_TEST | — | 94.4% |
| CountBench | — | 97.6% |
| RefCOCO (avg) | — | 93.5% |
| ODINW13 | — | 51.8% |
| ERQA | — | 65.7% |
| VideoMMMU | — | 84.0% |
| MLVU (M-Avg) | — | 86.7% |
| ScreenSpot Pro | — | 68.2% |
| ReasoningKimi K2.5 (Reasoning) wins | ||
| MuSR | 86% | — |
| BBH | 91% | — |
| LongBench v2 | 61% | 62% |
| MRCRv2 | 81% | — |
| AI-Needle | — | 68.3% |
| KnowledgeKimi K2.5 (Reasoning) wins | ||
| MMLU | 92% | — |
| GPQA | 87.6% | 90.4% |
| SuperGPQA | 88% | 71.6% |
| MMLU-Pro | 87.1% | 88.5% |
| HLE | 27% | 28.8% |
| FrontierScience | 80% | — |
| SimpleQA | 54% | — |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| Instruction FollowingQwen3.6 Plus wins | ||
| IFEval | 94% | 94.3% |
| IFBench | — | 74.2% |
| MultilingualKimi K2.5 (Reasoning) wins | ||
| MGSM | 96% | — |
| MMLU-ProX | 86% | 84.7% |
| NOVA-63 | — | 57.9% |
| INCLUDE | — | 85.1% |
| PolyMath | — | 77.4% |
| VWT2k-lite | — | 84.3% |
| MAXIFE | — | 88.2% |
| Mathematics | ||
| AIME 2023 | 94% | — |
| AIME 2024 | 96% | — |
| AIME 2025 | 96.1% | — |
| HMMT Feb 2023 | 90% | — |
| HMMT Feb 2024 | 92% | — |
| HMMT Feb 2025 | 95.4% | — |
| BRUMO 2025 | 93% | — |
| MATH-500 | 92% | — |
| AIME26 | — | 95.3% |
| HMMT Feb 2025 | — | 96.7% |
| HMMT Nov 2025 | — | 94.6% |
| HMMT Feb 2026 | — | 87.8% |
| MMAnswerBench | — | 83.8% |
Kimi K2.5 (Reasoning) is ahead overall, 76 to 69. The biggest single separator in this matchup is DeepPlanning, where the scores are 14.3% and 41.5%.
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 67.9 versus 66. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 70.4 versus 64.9. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for reasoning in this comparison, averaging 74.3 versus 62. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for agentic tasks in this comparison, averaging 62 versus 57.6. Inside this category, DeepPlanning is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for multimodal and grounded tasks in this comparison, averaging 78.8 versus 77.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for instruction following in this comparison, averaging 94.3 versus 94. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 89.5 versus 84.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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