Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
0/8 categoriesSarvam 30B
48
Winner · 2/8 categories1-bit Bonsai 4B· Sarvam 30B
Pick Sarvam 30B if you want the stronger benchmark profile. 1-bit Bonsai 4B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Sarvam 30B is clearly ahead on the aggregate, 48 to 44. 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 28.7. The single biggest benchmark swing on the page is MATH-500, 65.8% to 97%.
Sarvam 30B is the reasoning model in the pair, while 1-bit Bonsai 4B 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. Sarvam 30B gives you the larger context window at 64K, compared with 32K for 1-bit Bonsai 4B.
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 | 1-bit Bonsai 4B | Sarvam 30B |
|---|---|---|
| Agentic | ||
| BrowseComp | — | 35.5% |
| Coding | ||
| HumanEval | — | 92.1% |
| LiveCodeBench v6 | — | 70.0% |
| SWE-bench Verified | — | 34% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| MuSR | 41.4% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| GPQA | 28.7% | — |
| MMLU | — | 85.1% |
| MMLU-Pro | — | 80% |
| Instruction Following | ||
| IFEval | 69.6% | — |
| Multilingual | ||
| Coming soon | ||
| MathematicsSarvam 30B wins | ||
| MATH-500 | 65.8% | 97% |
| AIME 2025 | — | 80% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
Sarvam 30B is ahead overall, 48 to 44. The biggest single separator in this matchup is MATH-500, where the scores are 65.8% and 97%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 28.7. 1-bit Bonsai 4B stays close enough that the answer can still flip depending on your workload.
Sarvam 30B has the edge for math in this comparison, averaging 86.5 versus 65.8. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
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