Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o4-mini (high)
58
1/8 categoriesSarvam 105B
60
Winner · 4/8 categorieso4-mini (high)· Sarvam 105B
Pick Sarvam 105B if you want the stronger benchmark profile. o4-mini (high) only becomes the better choice if agentic is the priority or you need the larger 200K context window.
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 62.7. The single biggest benchmark swing on the page is SWE-bench Verified, 68.1% to 45%. o4-mini (high) does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
o4-mini (high) gives you the larger context window at 200K, 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 | o4-mini (high) | Sarvam 105B |
|---|---|---|
| Agentico4-mini (high) wins | ||
| Terminal-Bench 2.0 | 58% | — |
| BrowseComp | 64% | 49.5% |
| OSWorld-Verified | 55% | — |
| CodingSarvam 105B wins | ||
| HumanEval | 74% | — |
| SWE-bench Verified | 68.1% | 45% |
| LiveCodeBench | 34% | — |
| SWE-bench Pro | 42% | — |
| LiveCodeBench v6 | — | 71.7% |
| Multimodal & Grounded | ||
| MMMU-Pro | 66% | — |
| OfficeQA Pro | 71% | — |
| Reasoning | ||
| MuSR | 78% | — |
| BBH | 83% | — |
| LongBench v2 | 75% | — |
| MRCRv2 | 74% | — |
| ARC-AGI-2 | 2.4% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 82% | 90.6% |
| GPQA | 82% | — |
| SuperGPQA | 80% | — |
| MMLU-Pro | 76% | 81.7% |
| HLE | 13% | — |
| FrontierScience | 73% | — |
| SimpleQA | 80% | — |
| Instruction FollowingSarvam 105B wins | ||
| IFEval | 83% | 84.8% |
| Multilingual | ||
| MGSM | 83% | — |
| MMLU-ProX | 81% | — |
| MathematicsSarvam 105B wins | ||
| AIME 2023 | 83% | — |
| AIME 2024 | 93.4% | — |
| AIME 2025 | 92.7% | 88.3% |
| HMMT Feb 2023 | 79% | — |
| HMMT Feb 2024 | 81% | — |
| HMMT Feb 2025 | 80% | — |
| BRUMO 2025 | 82% | — |
| MATH-500 | 84% | 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 SWE-bench Verified, where the scores are 68.1% and 45%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 62.7. 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 44.9. 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 86.8. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
o4-mini (high) has the edge for agentic tasks in this comparison, averaging 58.5 versus 49.5. 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 83. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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