Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Kimi K2.5
71
2/8 categoriesQwen3.5-27B
71
5/8 categoriesKimi K2.5· Qwen3.5-27B
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; Qwen3.5-27B is the better fit if knowledge is the priority or you want the cheaper token bill.
Kimi K2.5 and Qwen3.5-27B 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 Qwen3.5-27B. That is roughly Infinityx on output cost alone. Qwen3.5-27B 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. Qwen3.5-27B 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 | Qwen3.5-27B |
|---|---|---|
| AgenticKimi K2.5 wins | ||
| Terminal-Bench 2.0 | 50.8% | 41.6% |
| BrowseComp | 60.6% | 61% |
| OSWorld-Verified | 63.3% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 99% | — |
| SWE-bench Verified | 76.8% | 72.4% |
| LiveCodeBench | 85% | 80.7% |
| SWE-bench Pro | 40% | — |
| SWE-Rebench | 58.5% | — |
| React Native Evals | 74.9% | — |
| Multimodal & GroundedQwen3.5-27B wins | ||
| MMMU-Pro | 78.5% | 75% |
| OfficeQA Pro | 69% | — |
| ReasoningKimi K2.5 wins | ||
| MuSR | 72% | — |
| BBH | 81% | — |
| LongBench v2 | 67% | 60.6% |
| MRCRv2 | 70% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 77% | — |
| GPQA | 87.6% | 85.5% |
| SuperGPQA | 74% | 65.6% |
| MMLU-Pro | 87.1% | 86.1% |
| HLE | 11% | — |
| FrontierScience | 67% | — |
| SimpleQA | 74% | — |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 94% | 95% |
| MultilingualQwen3.5-27B 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 Qwen3.5-27B are tied on overall score, so the right pick depends on which category matters most for your use case.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 62.8. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for coding in this comparison, averaging 77.6 versus 63.2. Inside this category, SWE-bench Verified 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 60.6. 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 51.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for multimodal and grounded tasks in this comparison, averaging 75 versus 74.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 94. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-27B 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.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.