Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 nano
44
1/8 categoriesSarvam 30B
48
Winner · 2/8 categoriesGPT-4.1 nano· Sarvam 30B
Pick Sarvam 30B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if agentic is the priority or you need the larger 1M context window.
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 50.7. The single biggest benchmark swing on the page is BrowseComp, 62% to 35.5%. GPT-4.1 nano does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Sarvam 30B. That is roughly Infinityx on output cost alone. Sarvam 30B is the reasoning model in the pair, while GPT-4.1 nano 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. GPT-4.1 nano gives you the larger context window at 1M, 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 | GPT-4.1 nano | Sarvam 30B |
|---|---|---|
| AgenticGPT-4.1 nano wins | ||
| Terminal-Bench 2.0 | 43% | — |
| BrowseComp | 62% | 35.5% |
| OSWorld-Verified | 42% | — |
| CodingSarvam 30B wins | ||
| SWE-bench Pro | 18% | — |
| HumanEval | — | 92.1% |
| LiveCodeBench v6 | — | 70.0% |
| SWE-bench Verified | — | 34% |
| Multimodal & Grounded | ||
| MMMU-Pro | 53% | — |
| OfficeQA Pro | 67% | — |
| Reasoning | ||
| LongBench v2 | 75% | — |
| MRCRv2 | 73% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 80.1% | 85.1% |
| GPQA | 50.3% | — |
| FrontierScience | 51% | — |
| MMLU-Pro | — | 80% |
| Instruction Following | ||
| IFEval | 83.2% | — |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| AIME 2024 | 9.8% | — |
| MATH-500 | — | 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 BrowseComp, where the scores are 62% and 35.5%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 50.7. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Sarvam 30B has the edge for coding in this comparison, averaging 34 versus 18. GPT-4.1 nano stays close enough that the answer can still flip depending on your workload.
GPT-4.1 nano has the edge for agentic tasks in this comparison, averaging 47.4 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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.