Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E4B
~47
1/8 categoriesSarvam 30B
48
Winner · 1/8 categoriesGemma 4 E4B· Sarvam 30B
Pick Sarvam 30B if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if coding is the priority or you need the larger 128K context window.
Sarvam 30B finishes one point ahead overall, 48 to 47. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Sarvam 30B's sharpest advantage is in knowledge, where it averages 80 against 65.6. The single biggest benchmark swing on the page is MMLU-Pro, 69.4% to 80%. Gemma 4 E4B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Gemma 4 E4B 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 | Gemma 4 E4B | Sarvam 30B |
|---|---|---|
| Agentic | ||
| BrowseComp | — | 35.5% |
| CodingGemma 4 E4B wins | ||
| LiveCodeBench | 52% | — |
| HumanEval | — | 92.1% |
| LiveCodeBench v6 | — | 70.0% |
| SWE-bench Verified | — | 34% |
| Multimodal & Grounded | ||
| MMMU-Pro | 52.6% | — |
| Reasoning | ||
| BBH | 33.1% | — |
| MRCRv2 | 25.4% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| GPQA | 58.6% | — |
| MMLU-Pro | 69.4% | 80% |
| MMLU | — | 85.1% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| MATH-500 | — | 97% |
| AIME 2025 | — | 80% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
Sarvam 30B is ahead overall, 48 to 47. The biggest single separator in this matchup is MMLU-Pro, where the scores are 69.4% and 80%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 65.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Gemma 4 E4B has the edge for coding in this comparison, averaging 52 versus 34. Sarvam 30B stays close enough that the answer can still flip depending on your workload.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.