Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mixtral 8x22B Instruct v0.1
36
1/8 categoriesSarvam 30B
48
Winner · 2/8 categoriesMixtral 8x22B Instruct v0.1· Sarvam 30B
Pick Sarvam 30B if you want the stronger benchmark profile. Mixtral 8x22B Instruct v0.1 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Sarvam 30B is clearly ahead on the aggregate, 48 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Sarvam 30B's sharpest advantage is in knowledge, where it averages 80 against 53. The single biggest benchmark swing on the page is HumanEval, 54.8% to 92.1%. Mixtral 8x22B Instruct v0.1 does hit back in coding, 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 Mixtral 8x22B Instruct v0.1 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.
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 | Mixtral 8x22B Instruct v0.1 | Sarvam 30B |
|---|---|---|
| AgenticSarvam 30B wins | ||
| Terminal-Bench 2.0 | 35% | — |
| BrowseComp | 32% | 35.5% |
| OSWorld-Verified | 28% | — |
| CodingMixtral 8x22B Instruct v0.1 wins | ||
| HumanEval | 54.8% | 92.1% |
| SWE-bench Pro | 40% | — |
| LiveCodeBench v6 | — | 70.0% |
| SWE-bench Verified | — | 34% |
| Multimodal & Grounded | ||
| MMMU-Pro | 35% | — |
| OfficeQA Pro | 36% | — |
| Reasoning | ||
| LongBench v2 | 39% | — |
| MRCRv2 | 38% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 77.8% | 85.1% |
| FrontierScience | 53% | — |
| MMLU-Pro | — | 80% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MMLU-ProX | 42% | — |
| Mathematics | ||
| MATH-500 | — | 97% |
| AIME 2025 | — | 80% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
Sarvam 30B is ahead overall, 48 to 36. The biggest single separator in this matchup is HumanEval, where the scores are 54.8% and 92.1%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 53. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Mixtral 8x22B Instruct v0.1 has the edge for coding in this comparison, averaging 40 versus 34. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Sarvam 30B has the edge for agentic tasks in this comparison, averaging 35.5 versus 31.8. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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