Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-OSS 120B
50
Winner · 1/8 categoriesSarvam 30B
48
3/8 categoriesGPT-OSS 120B· Sarvam 30B
Pick GPT-OSS 120B if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if mathematics is the priority or you want the stronger reasoning-first profile.
GPT-OSS 120B has the cleaner overall profile here, landing at 50 versus 48. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-OSS 120B's sharpest advantage is in agentic, where it averages 44.8 against 35.5. The single biggest benchmark swing on the page is HumanEval, 43% to 92.1%. Sarvam 30B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Sarvam 30B is the reasoning model in the pair, while GPT-OSS 120B 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. GPT-OSS 120B gives you the larger context window at 128K, 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 | GPT-OSS 120B | Sarvam 30B |
|---|---|---|
| AgenticGPT-OSS 120B wins | ||
| Terminal-Bench 2.0 | 43% | — |
| BrowseComp | 50% | 35.5% |
| OSWorld-Verified | 43% | — |
| CodingSarvam 30B wins | ||
| HumanEval | 43% | 92.1% |
| SWE-bench Verified | 29% | 34% |
| LiveCodeBench | 25% | — |
| SWE-bench Pro | 31% | — |
| SWE-Rebench | 33.3% | — |
| React Native Evals | 66.4% | — |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| MMMU-Pro | 42% | — |
| OfficeQA Pro | 57% | — |
| Reasoning | ||
| MuSR | 47% | — |
| BBH | 73% | — |
| LongBench v2 | 58% | — |
| MRCRv2 | 59% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 90% | 85.1% |
| GPQA | 80.9% | — |
| SuperGPQA | 48% | — |
| MMLU-Pro | 90% | 80% |
| HLE | 5% | — |
| FrontierScience | 49% | — |
| SimpleQA | 49% | — |
| Instruction Following | ||
| IFEval | 79% | — |
| Multilingual | ||
| MGSM | 72% | — |
| MMLU-ProX | 70% | — |
| MathematicsSarvam 30B wins | ||
| AIME 2023 | 51% | — |
| AIME 2024 | 53% | — |
| AIME 2025 | 52% | 80% |
| HMMT Feb 2023 | 47% | — |
| HMMT Feb 2024 | 49% | — |
| HMMT Feb 2025 | 48% | — |
| BRUMO 2025 | 50% | — |
| MATH-500 | 71% | 97% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
GPT-OSS 120B is ahead overall, 50 to 48. The biggest single separator in this matchup is HumanEval, where the scores are 43% and 92.1%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 51.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Sarvam 30B has the edge for coding in this comparison, averaging 34 versus 30. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Sarvam 30B has the edge for math in this comparison, averaging 86.5 versus 56.1. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
GPT-OSS 120B has the edge for agentic tasks in this comparison, averaging 44.8 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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