Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mistral Large 3
58
1/8 categoriesSarvam 105B
60
Winner · 4/8 categoriesMistral Large 3· Sarvam 105B
Pick Sarvam 105B if you want the stronger benchmark profile. Mistral Large 3 only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Sarvam 105B has the cleaner overall profile here, landing at 60 versus 58. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Sarvam 105B's sharpest advantage is in knowledge, where it averages 81.7 against 48.1. The single biggest benchmark swing on the page is AIME 2025, 77% to 88.3%. Mistral Large 3 does hit back in instruction following, 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 Sarvam 105B. That is roughly Infinityx on output cost alone. Sarvam 105B 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.
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 | Sarvam 105B |
|---|---|---|
| AgenticSarvam 105B wins | ||
| OSWorld-Verified | 49% | — |
| BrowseComp | — | 49.5% |
| CodingSarvam 105B wins | ||
| HumanEval | 92.3% | — |
| SWE-bench Verified | 45% | 45% |
| LiveCodeBench | 39% | — |
| SWE-bench Pro | 42% | — |
| LiveCodeBench v6 | — | 71.7% |
| Multimodal & Grounded | ||
| MMMU-Pro | 75% | — |
| OfficeQA Pro | 76% | — |
| Reasoning | ||
| MuSR | 71% | — |
| BBH | 81% | — |
| LongBench v2 | 67% | — |
| MRCRv2 | 67% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| GPQA | 43.9% | — |
| SuperGPQA | 73% | — |
| MMLU-Pro | 73.1% | 81.7% |
| HLE | 12% | — |
| FrontierScience | 67% | — |
| SimpleQA | 24% | — |
| MMLU | — | 90.6% |
| Instruction FollowingMistral Large 3 wins | ||
| IFEval | 89.4% | 84.8% |
| Multilingual | ||
| MGSM | 82% | — |
| MMLU-ProX | 77% | — |
| MathematicsSarvam 105B wins | ||
| AIME 2023 | 76% | — |
| AIME 2024 | 78% | — |
| AIME 2025 | 77% | 88.3% |
| HMMT Feb 2023 | 72% | — |
| HMMT Feb 2024 | 74% | — |
| HMMT Feb 2025 | 73% | — |
| BRUMO 2025 | 75% | — |
| MATH-500 | 93.6% | 98.6% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
Sarvam 105B is ahead overall, 60 to 58. The biggest single separator in this matchup is AIME 2025, where the scores are 77% and 88.3%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 48.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Sarvam 105B has the edge for coding in this comparison, averaging 45 versus 41.5. Mistral Large 3 stays close enough that the answer can still flip depending on your workload.
Sarvam 105B has the edge for math in this comparison, averaging 92.3 versus 80.4. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
Sarvam 105B has the edge for agentic tasks in this comparison, averaging 49.5 versus 49. Mistral Large 3 stays close enough that the answer can still flip depending on your workload.
Mistral Large 3 has the edge for instruction following in this comparison, averaging 89.4 versus 84.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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