Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mistral Large 3
60
3/8 categoriesQwen3.5-27B
70
Winner · 4/8 categoriesMistral Large 3· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. Mistral Large 3 only becomes the better choice if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-27B is clearly ahead on the aggregate, 70 to 60. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-27B's sharpest advantage is in coding, where it averages 77.6 against 41.5. The single biggest benchmark swing on the page is LiveCodeBench, 39% to 80.7%. Mistral Large 3 does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Mistral Large 3 is also the more expensive model on tokens at $2.00 input / $6.00 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 Mistral Large 3 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 Mistral Large 3.
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 | Mistral Large 3 | Qwen3.5-27B |
|---|---|---|
| AgenticMistral Large 3 wins | ||
| Terminal-Bench 2.0 | 52% | 41.6% |
| BrowseComp | 58% | 61% |
| OSWorld-Verified | 49% | 56.2% |
| tau2-bench | — | 79% |
| CodingQwen3.5-27B wins | ||
| HumanEval | 92.3% | — |
| SWE-bench Verified | 45% | 72.4% |
| LiveCodeBench | 39% | 80.7% |
| SWE-bench Pro | 42% | — |
| Multimodal & GroundedMistral Large 3 wins | ||
| MMMU-Pro | 75% | 75% |
| OfficeQA Pro | 76% | — |
| ReasoningMistral Large 3 wins | ||
| MuSR | 71% | — |
| BBH | 81% | — |
| LongBench v2 | 67% | 60.6% |
| MRCRv2 | 67% | — |
| KnowledgeQwen3.5-27B wins | ||
| MMLU | 85.5% | — |
| GPQA | 43.9% | 85.5% |
| SuperGPQA | 73% | 65.6% |
| MMLU-Pro | 73.1% | 86.1% |
| HLE | 12% | — |
| FrontierScience | 67% | — |
| SimpleQA | 24% | — |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 89.4% | 95% |
| MultilingualQwen3.5-27B wins | ||
| MGSM | 82% | — |
| MMLU-ProX | 77% | 82.2% |
| Mathematics | ||
| AIME 2023 | 76% | — |
| AIME 2024 | 78% | — |
| AIME 2025 | 77% | — |
| HMMT Feb 2023 | 72% | — |
| HMMT Feb 2024 | 74% | — |
| HMMT Feb 2025 | 73% | — |
| BRUMO 2025 | 75% | — |
| MATH-500 | 93.6% | — |
Qwen3.5-27B is ahead overall, 70 to 60. The biggest single separator in this matchup is LiveCodeBench, where the scores are 39% and 80.7%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 48.1. Inside this category, GPQA 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 41.5. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for reasoning in this comparison, averaging 68.1 versus 60.6. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for agentic tasks in this comparison, averaging 52.5 versus 51.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for multimodal and grounded tasks in this comparison, averaging 75.5 versus 75. Qwen3.5-27B stays close enough that the answer can still flip depending on your workload.
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 89.4. 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 78.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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