Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o3
64
Winner · 3/8 categoriesSarvam 30B
48
1/8 categorieso3· Sarvam 30B
Pick o3 if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
o3 is clearly ahead on the aggregate, 64 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3's sharpest advantage is in agentic, where it averages 69.9 against 35.5. The single biggest benchmark swing on the page is BrowseComp, 75% 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 is also the more expensive model on tokens at $10.00 input / $40.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Sarvam 30B. That is roughly Infinityx on output cost alone. o3 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 | Sarvam 30B |
|---|---|---|
| Agentico3 wins | ||
| Terminal-Bench 2.0 | 71% | — |
| BrowseComp | 75% | 35.5% |
| OSWorld-Verified | 65% | — |
| Codingo3 wins | ||
| HumanEval | 78% | 92.1% |
| SWE-bench Verified | 71.7% | 34% |
| LiveCodeBench | 40% | — |
| SWE-bench Pro | 58% | — |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| MMMU-Pro | 70% | — |
| OfficeQA Pro | 75% | — |
| VideoMMMU | 83.3% | — |
| Reasoning | ||
| MuSR | 82% | — |
| BBH | 86% | — |
| LongBench v2 | 82% | — |
| MRCRv2 | 81% | — |
| ARC-AGI-2 | 3% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 86% | 85.1% |
| MMLU-Pro | 75% | 80% |
| HLE | 24% | — |
| FrontierScience | 77% | — |
| Instruction Following | ||
| IFEval | 85% | — |
| Multilingual | ||
| MGSM | 83% | — |
| MMLU-ProX | 80% | — |
| Mathematicso3 wins | ||
| AIME 2023 | 88% | — |
| AIME 2024 | 90% | — |
| AIME 2025 | 89% | 80% |
| HMMT Feb 2023 | 84% | — |
| HMMT Feb 2024 | 86% | — |
| HMMT Feb 2025 | 85% | — |
| BRUMO 2025 | 87% | — |
| MATH-500 | 88% | 97% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
o3 is ahead overall, 64 to 48. The biggest single separator in this matchup is BrowseComp, where the scores are 75% and 35.5%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 57. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
o3 has the edge for coding in this comparison, averaging 54.2 versus 34. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
o3 has the edge for math in this comparison, averaging 88.1 versus 86.5. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
o3 has the edge for agentic tasks in this comparison, averaging 69.9 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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