Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Composer 2
~62
Winner · 1/8 categoriesSarvam 30B
48
0/8 categoriesComposer 2· Sarvam 30B
Pick Composer 2 if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if you want the cheaper token bill.
Composer 2 is clearly ahead on the aggregate, 62 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Composer 2's sharpest advantage is in agentic, where it averages 61.7 against 35.5.
Composer 2 is also the more expensive model on tokens at $0.50 input / $2.50 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. Composer 2 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 | Composer 2 | Sarvam 30B |
|---|---|---|
| AgenticComposer 2 wins | ||
| Terminal-Bench 2.0 | 61.7% | — |
| BrowseComp | — | 35.5% |
| Coding | ||
| SWE Multilingual | 73.7% | — |
| React Native Evals | 96.2% | — |
| HumanEval | — | 92.1% |
| LiveCodeBench v6 | — | 70.0% |
| SWE-bench Verified | — | 34% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| gpqaDiamond | — | 66.5% |
| Knowledge | ||
| MMLU | — | 85.1% |
| MMLU-Pro | — | 80% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| MATH-500 | — | 97% |
| AIME 2025 | — | 80% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
Composer 2 is ahead overall, 62 to 48.
Composer 2 has the edge for agentic tasks in this comparison, averaging 61.7 versus 35.5. Sarvam 30B stays close enough that the answer can still flip depending on your workload.
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