Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.1
41
0/8 categoriesSarvam 105B
60
Winner · 5/8 categoriesDeepSeek V3.1· Sarvam 105B
Pick Sarvam 105B if you want the stronger benchmark profile. DeepSeek V3.1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Sarvam 105B is clearly ahead on the aggregate, 60 to 41. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Sarvam 105B's sharpest advantage is in mathematics, where it averages 92.3 against 39.6. The single biggest benchmark swing on the page is MMLU, 33% to 90.6%.
Sarvam 105B is the reasoning model in the pair, while DeepSeek V3.1 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 | DeepSeek V3.1 | Sarvam 105B |
|---|---|---|
| AgenticSarvam 105B wins | ||
| Terminal-Bench 2.0 | 29% | — |
| BrowseComp | 39% | 49.5% |
| OSWorld-Verified | 33% | — |
| CodingSarvam 105B wins | ||
| HumanEval | 25% | — |
| SWE-bench Verified | 13% | 45% |
| LiveCodeBench | 15% | — |
| SWE-bench Pro | 15% | — |
| LiveCodeBench v6 | — | 71.7% |
| Multimodal & Grounded | ||
| MMMU-Pro | 35% | — |
| OfficeQA Pro | 45% | — |
| Reasoning | ||
| MuSR | 29% | — |
| BBH | 61% | — |
| LongBench v2 | 46% | — |
| MRCRv2 | 48% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 33% | 90.6% |
| GPQA | 32% | — |
| SuperGPQA | 30% | — |
| MMLU-Pro | 53% | 81.7% |
| HLE | 2% | — |
| FrontierScience | 37% | — |
| SimpleQA | 31% | — |
| Instruction FollowingSarvam 105B wins | ||
| IFEval | 67% | 84.8% |
| Multilingual | ||
| MGSM | 64% | — |
| MMLU-ProX | 59% | — |
| MathematicsSarvam 105B wins | ||
| AIME 2023 | 33% | — |
| AIME 2024 | 35% | — |
| AIME 2025 | 34% | 88.3% |
| HMMT Feb 2023 | 29% | — |
| HMMT Feb 2024 | 31% | — |
| HMMT Feb 2025 | 30% | — |
| BRUMO 2025 | 32% | — |
| MATH-500 | 59% | 98.6% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
Sarvam 105B is ahead overall, 60 to 41. The biggest single separator in this matchup is MMLU, where the scores are 33% and 90.6%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 30.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Sarvam 105B has the edge for coding in this comparison, averaging 45 versus 14.5. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Sarvam 105B has the edge for math in this comparison, averaging 92.3 versus 39.6. 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 32.9. Inside this category, BrowseComp 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 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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