Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mistral Medium 3
~52
0/8 categoriesQwen3.5-35B-A3B
67
Winner · 3/8 categoriesMistral Medium 3· Qwen3.5-35B-A3B
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. Mistral Medium 3 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-35B-A3B is clearly ahead on the aggregate, 67 to 52. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-35B-A3B's sharpest advantage is in coding, where it averages 72.6 against 30.3. The single biggest benchmark swing on the page is LiveCodeBench, 30.3% to 74.6%.
Mistral Medium 3 is also the more expensive model on tokens at $0.40 input / $2.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-35B-A3B. That is roughly Infinityx on output cost alone. Qwen3.5-35B-A3B is the reasoning model in the pair, while Mistral Medium 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. Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 128K for Mistral Medium 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 Medium 3 | Qwen3.5-35B-A3B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 40.5% |
| BrowseComp | — | 61% |
| OSWorld-Verified | — | 54.5% |
| tau2-bench | — | 81.2% |
| CodingQwen3.5-35B-A3B wins | ||
| HumanEval | 92.1% | — |
| LiveCodeBench | 30.3% | 74.6% |
| SWE-bench Verified | — | 69.2% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 75.1% |
| Reasoning | ||
| LongBench v2 | — | 59% |
| KnowledgeQwen3.5-35B-A3B wins | ||
| GPQA | 57.1% | 84.2% |
| MMLU-Pro | 77.2% | 85.3% |
| SuperGPQA | — | 63.4% |
| Instruction FollowingQwen3.5-35B-A3B wins | ||
| IFEval | 89.4% | 91.9% |
| Multilingual | ||
| MMLU-ProX | — | 81% |
| Mathematics | ||
| MATH-500 | 91% | — |
Qwen3.5-35B-A3B is ahead overall, 67 to 52. The biggest single separator in this matchup is LiveCodeBench, where the scores are 30.3% and 74.6%.
Qwen3.5-35B-A3B has the edge for knowledge tasks in this comparison, averaging 79.3 versus 70.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for coding in this comparison, averaging 72.6 versus 30.3. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen3.5-35B-A3B has the edge for instruction following in this comparison, averaging 91.9 versus 89.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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