Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o3-pro
67
Winner · 3/8 categoriesSarvam 30B
48
1/8 categorieso3-pro· Sarvam 30B
Pick o3-pro if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if knowledge is the priority.
o3-pro is clearly ahead on the aggregate, 67 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-pro's sharpest advantage is in agentic, where it averages 70.4 against 35.5. The single biggest benchmark swing on the page is BrowseComp, 76% to 35.5%. Sarvam 30B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o3-pro gives you the larger context window at 200K, compared with 64K for Sarvam 30B.
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 | o3-pro | Sarvam 30B |
|---|---|---|
| Agentico3-pro wins | ||
| Terminal-Bench 2.0 | 69% | — |
| BrowseComp | 76% | 35.5% |
| OSWorld-Verified | 68% | — |
| Codingo3-pro wins | ||
| HumanEval | 80% | 92.1% |
| SWE-bench Verified | 46% | 34% |
| LiveCodeBench | 44% | — |
| SWE-bench Pro | 55% | — |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| MMMU-Pro | 70% | — |
| OfficeQA Pro | 79% | — |
| Reasoning | ||
| MuSR | 84% | — |
| BBH | 89% | — |
| LongBench v2 | 81% | — |
| MRCRv2 | 81% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 88% | 85.1% |
| GPQA | 89% | — |
| SuperGPQA | 87% | — |
| MMLU-Pro | 75% | 80% |
| HLE | 26% | — |
| FrontierScience | 77% | — |
| SimpleQA | 86% | — |
| Instruction Following | ||
| IFEval | 82% | — |
| Multilingual | ||
| MGSM | 83% | — |
| MMLU-ProX | 80% | — |
| Mathematicso3-pro wins | ||
| AIME 2023 | 90% | — |
| AIME 2024 | 92% | — |
| AIME 2025 | 91% | 80% |
| HMMT Feb 2023 | 86% | — |
| HMMT Feb 2024 | 88% | — |
| HMMT Feb 2025 | 87% | — |
| BRUMO 2025 | 89% | — |
| MATH-500 | 89% | 97% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
o3-pro is ahead overall, 67 to 48. The biggest single separator in this matchup is BrowseComp, where the scores are 76% and 35.5%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 68.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
o3-pro has the edge for coding in this comparison, averaging 48.7 versus 34. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
o3-pro has the edge for math in this comparison, averaging 89.8 versus 86.5. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
o3-pro has the edge for agentic tasks in this comparison, averaging 70.4 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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