Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Kimi K2.5 (Reasoning)
76
Winner · 3/8 categoriesSarvam 30B
48
1/8 categoriesKimi K2.5 (Reasoning)· Sarvam 30B
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if knowledge is the priority.
Kimi K2.5 (Reasoning) is clearly ahead on the aggregate, 76 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in coding, where it averages 70.4 against 34. The single biggest benchmark swing on the page is SWE-bench Verified, 76.8% to 34%. Sarvam 30B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Kimi K2.5 (Reasoning) 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 K2.5 (Reasoning) | Sarvam 30B |
|---|---|---|
| AgenticKimi K2.5 (Reasoning) wins | ||
| Terminal-Bench 2.0 | 50.8% | — |
| BrowseComp | 60.6% | 35.5% |
| OSWorld-Verified | 63.3% | — |
| DeepPlanning | 14.3% | — |
| CodingKimi K2.5 (Reasoning) wins | ||
| HumanEval | 99% | 92.1% |
| SWE-bench Verified | 76.8% | 34% |
| LiveCodeBench | 85% | — |
| SWE-bench Pro | 70% | — |
| SWE-Rebench | 57.4% | — |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| MMMU-Pro | 78.5% | — |
| OfficeQA Pro | 77% | — |
| Reasoning | ||
| MuSR | 86% | — |
| BBH | 91% | — |
| LongBench v2 | 61% | — |
| MRCRv2 | 81% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 92% | 85.1% |
| GPQA | 87.6% | — |
| SuperGPQA | 88% | — |
| MMLU-Pro | 87.1% | 80% |
| HLE | 27% | — |
| FrontierScience | 80% | — |
| SimpleQA | 54% | — |
| Instruction Following | ||
| IFEval | 94% | — |
| Multilingual | ||
| MGSM | 96% | — |
| MMLU-ProX | 86% | — |
| MathematicsKimi K2.5 (Reasoning) wins | ||
| AIME 2023 | 94% | — |
| AIME 2024 | 96% | — |
| AIME 2025 | 96.1% | 80% |
| HMMT Feb 2023 | 90% | — |
| HMMT Feb 2024 | 92% | — |
| HMMT Feb 2025 | 95.4% | — |
| BRUMO 2025 | 93% | — |
| MATH-500 | 92% | 97% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
Kimi K2.5 (Reasoning) is ahead overall, 76 to 48. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 76.8% and 34%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 67.9. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 70.4 versus 34. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for math in this comparison, averaging 94 versus 86.5. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for agentic tasks in this comparison, averaging 57.6 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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