Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Kimi K2
53
Winner · 2/8 categoriesSarvam 30B
48
2/8 categoriesKimi K2· Sarvam 30B
Pick Kimi K2 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.
Kimi K2 is clearly ahead on the aggregate, 53 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2's sharpest advantage is in coding, where it averages 58.2 against 34. The single biggest benchmark swing on the page is SWE-bench Verified, 65.8% to 34%. 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 Kimi K2 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. Kimi K2 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 | Kimi K2 | Sarvam 30B |
|---|---|---|
| AgenticKimi K2 wins | ||
| Terminal-Bench 2.0 | 47.1% | — |
| BrowseComp | 60.2% | 35.5% |
| Tau2-Telecom | 66.1% | — |
| CodingKimi K2 wins | ||
| SWE-bench Verified | 65.8% | 34% |
| LiveCodeBench | 53.7% | — |
| HumanEval | — | 92.1% |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| hle | 44.9% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 89.5% | 85.1% |
| GPQA | 75.1% | — |
| SuperGPQA | 57.2% | — |
| MMLU-Pro | 81.1% | 80% |
| SimpleQA | 31% | — |
| Instruction Following | ||
| IFEval | 89.8% | — |
| Multilingual | ||
| sweMultilingual | 61.1% | — |
| MathematicsSarvam 30B wins | ||
| AIME 2024 | 69.6% | — |
| AIME 2025 | 49.5% | 80% |
| MATH-500 | 97.4% | 97% |
| HMMT Feb 2025 | 38.8% | — |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
Kimi K2 is ahead overall, 53 to 48. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 65.8% and 34%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 64. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Kimi K2 has the edge for coding in this comparison, averaging 58.2 versus 34. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Sarvam 30B has the edge for math in this comparison, averaging 86.5 versus 67.9. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
Kimi K2 has the edge for agentic tasks in this comparison, averaging 52.1 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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