Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Kimi K2.5
71
3/8 categoriesQwen3.5-122B-A10B
71
4/8 categoriesKimi K2.5· Qwen3.5-122B-A10B
Treat this as a split decision. Kimi K2.5 makes more sense if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Qwen3.5-122B-A10B is the better fit if knowledge is the priority or you want the cheaper token bill.
Kimi K2.5 and Qwen3.5-122B-A10B finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
Kimi K2.5 is also the more expensive model on tokens at $0.50 input / $2.80 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-122B-A10B. That is roughly Infinityx on output cost alone. Qwen3.5-122B-A10B is the reasoning model in the pair, while Kimi K2.5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 128K for Kimi K2.5.
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 | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticKimi K2.5 wins | ||
| Terminal-Bench 2.0 | 50.8% | 49.4% |
| BrowseComp | 60.6% | 63.8% |
| OSWorld-Verified | 63.3% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 99% | — |
| SWE-bench Verified | 76.8% | 72% |
| LiveCodeBench | 85% | 78.9% |
| SWE-bench Pro | 40% | — |
| SWE-Rebench | 58.5% | — |
| React Native Evals | 74.9% | — |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 78.5% | 76.9% |
| OfficeQA Pro | 69% | — |
| ReasoningKimi K2.5 wins | ||
| MuSR | 72% | — |
| BBH | 81% | — |
| LongBench v2 | 67% | 60.2% |
| MRCRv2 | 70% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 77% | — |
| GPQA | 87.6% | 86.6% |
| SuperGPQA | 74% | 67.1% |
| MMLU-Pro | 87.1% | 86.7% |
| HLE | 11% | — |
| FrontierScience | 67% | — |
| SimpleQA | 74% | — |
| Instruction FollowingKimi K2.5 wins | ||
| IFEval | 94% | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MGSM | 83% | — |
| MMLU-ProX | 78% | 82.2% |
| Mathematics | ||
| AIME 2023 | 77% | — |
| AIME 2024 | 79% | — |
| AIME 2025 | 78% | — |
| HMMT Feb 2023 | 73% | — |
| HMMT Feb 2024 | 75% | — |
| HMMT Feb 2025 | 74% | — |
| BRUMO 2025 | 76% | — |
| MATH-500 | 82% | — |
Kimi K2.5 and Qwen3.5-122B-A10B are tied on overall score, so the right pick depends on which category matters most for your use case.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 62.8. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 76.3 versus 63.2. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for reasoning in this comparison, averaging 69.3 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for agentic tasks in this comparison, averaging 57.6 versus 56. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 74.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for instruction following in this comparison, averaging 94 versus 93.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for multilingual tasks in this comparison, averaging 82.2 versus 79.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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