Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mistral Small 4 (Reasoning)
~64
0/8 categoriesQwen3.5-122B-A10B
71
Winner · 3/8 categoriesMistral Small 4 (Reasoning)· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Mistral Small 4 (Reasoning) only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3.5-122B-A10B is clearly ahead on the aggregate, 71 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-122B-A10B's sharpest advantage is in multimodal & grounded, where it averages 76.9 against 60. The single biggest benchmark swing on the page is MMMU-Pro, 60% to 76.9%.
Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 256K for Mistral Small 4 (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 | Mistral Small 4 (Reasoning) | Qwen3.5-122B-A10B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 49.4% |
| BrowseComp | — | 63.8% |
| OSWorld-Verified | — | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| LiveCodeBench | 63.6% | 78.9% |
| SWE-bench Verified | — | 72% |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 60% | 76.9% |
| Reasoning | ||
| LongBench v2 | — | 60.2% |
| KnowledgeQwen3.5-122B-A10B wins | ||
| GPQA | 71.2% | 86.6% |
| MMLU-Pro | 78% | 86.7% |
| SuperGPQA | — | 67.1% |
| Instruction Following | ||
| IFEval | — | 93.4% |
| Multilingual | ||
| MMLU-ProX | — | 82.2% |
| Mathematics | ||
| AIME 2025 | 83.8% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 64. The biggest single separator in this matchup is MMMU-Pro, where the scores are 60% and 76.9%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 75.6. Inside this category, GPQA 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 63.6. Inside this category, LiveCodeBench 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 60. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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