Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 mini
57
Winner · 1/8 categoriesSarvam 30B
48
2/8 categoriesGPT-4.1 mini· Sarvam 30B
Pick GPT-4.1 mini if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-4.1 mini is clearly ahead on the aggregate, 57 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1 mini's sharpest advantage is in agentic, where it averages 56.5 against 35.5. The single biggest benchmark swing on the page is BrowseComp, 71% to 35.5%. Sarvam 30B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 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 mini 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 mini 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 mini | Sarvam 30B |
|---|---|---|
| AgenticGPT-4.1 mini wins | ||
| Terminal-Bench 2.0 | 54% | — |
| BrowseComp | 71% | 35.5% |
| OSWorld-Verified | 49% | — |
| CodingSarvam 30B wins | ||
| SWE-bench Verified | 23.6% | 34% |
| SWE-bench Pro | 30% | — |
| HumanEval | — | 92.1% |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| OfficeQA Pro | 74% | — |
| Reasoning | ||
| LongBench v2 | 80% | — |
| MRCRv2 | 82% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 87.5% | 85.1% |
| GPQA | 64.2% | — |
| MMLU-Pro | — | 80% |
| Instruction Following | ||
| IFEval | 88.5% | — |
| Multilingual | ||
| MMLU-ProX | 72% | — |
| Mathematics | ||
| AIME 2024 | 23.1% | — |
| MATH-500 | — | 97% |
| AIME 2025 | — | 80% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
GPT-4.1 mini is ahead overall, 57 to 48. The biggest single separator in this matchup is BrowseComp, where the scores are 71% and 35.5%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 64.2. 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 27.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for agentic tasks in this comparison, averaging 56.5 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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