Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o1-preview
72
Winner · 4/8 categoriesSarvam 105B
60
1/8 categorieso1-preview· Sarvam 105B
Pick o1-preview if you want the stronger benchmark profile. Sarvam 105B only becomes the better choice if knowledge is the priority.
o1-preview is clearly ahead on the aggregate, 72 to 60. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1-preview's sharpest advantage is in agentic, where it averages 75.4 against 49.5. The single biggest benchmark swing on the page is BrowseComp, 79% to 49.5%. Sarvam 105B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o1-preview 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 | o1-preview | Sarvam 105B |
|---|---|---|
| Agentico1-preview wins | ||
| Terminal-Bench 2.0 | 77% | — |
| BrowseComp | 79% | 49.5% |
| OSWorld-Verified | 71% | — |
| Codingo1-preview wins | ||
| HumanEval | 86% | — |
| SWE-bench Verified | 65% | 45% |
| LiveCodeBench | 60% | — |
| SWE-bench Pro | 69% | — |
| LiveCodeBench v6 | — | 71.7% |
| Multimodal & Grounded | ||
| MMMU-Pro | 72% | — |
| OfficeQA Pro | 80% | — |
| Reasoning | ||
| MuSR | 86% | — |
| BBH | 93% | — |
| LongBench v2 | 87% | — |
| MRCRv2 | 83% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 92% | 90.6% |
| GPQA | 90% | — |
| SuperGPQA | 88% | — |
| MMLU-Pro | 80% | 81.7% |
| HLE | 32% | — |
| FrontierScience | 83% | — |
| SimpleQA | 88% | — |
| Instruction Followingo1-preview wins | ||
| IFEval | 88% | 84.8% |
| Multilingual | ||
| MGSM | 90% | — |
| MMLU-ProX | 86% | — |
| Mathematicso1-preview wins | ||
| AIME 2023 | 94% | — |
| AIME 2024 | 96% | — |
| AIME 2025 | 95% | 88.3% |
| HMMT Feb 2023 | 90% | — |
| HMMT Feb 2024 | 92% | — |
| HMMT Feb 2025 | 91% | — |
| BRUMO 2025 | 93% | — |
| MATH-500 | 94% | 98.6% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
o1-preview is ahead overall, 72 to 60. The biggest single separator in this matchup is BrowseComp, where the scores are 79% and 49.5%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 72.7. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
o1-preview has the edge for coding in this comparison, averaging 64.6 versus 45. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o1-preview has the edge for math in this comparison, averaging 94.1 versus 92.3. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
o1-preview has the edge for agentic tasks in this comparison, averaging 75.4 versus 49.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
o1-preview has the edge for instruction following in this comparison, averaging 88 versus 84.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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