Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mistral Small 4
~62
Winner · 0/8 categoriesSarvam 105B
60
2/8 categoriesMistral Small 4· Sarvam 105B
Pick Mistral Small 4 if you want the stronger benchmark profile. Sarvam 105B only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
Mistral Small 4 has the cleaner overall profile here, landing at 62 versus 60. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Sarvam 105B is the reasoning model in the pair, while Mistral Small 4 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. Mistral Small 4 gives you the larger context window at 256K, compared with 128K for Sarvam 105B.
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 | Sarvam 105B |
|---|---|---|
| Agentic | ||
| BrowseComp | — | 49.5% |
| Coding | ||
| HumanEval | 84.8% | — |
| LiveCodeBench v6 | — | 71.7% |
| SWE-bench Verified | — | 45% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| GPQA | 45.3% | — |
| MMLU-Pro | 66.3% | 81.7% |
| MMLU | — | 90.6% |
| Instruction FollowingSarvam 105B wins | ||
| IFEval | 82.9% | 84.8% |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| MATH-500 | — | 98.6% |
| AIME 2025 | — | 88.3% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
Mistral Small 4 is ahead overall, 62 to 60. The biggest single separator in this matchup is MMLU-Pro, where the scores are 66.3% and 81.7%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 58.9. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Sarvam 105B has the edge for instruction following in this comparison, averaging 84.8 versus 82.9. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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