Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4o mini
54
Winner · 2/8 categoriesSarvam 30B
48
0/8 categoriesGPT-4o mini· Sarvam 30B
Pick GPT-4o mini if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
GPT-4o mini is clearly ahead on the aggregate, 54 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4o mini's sharpest advantage is in coding, where it averages 65 against 34. The single biggest benchmark swing on the page is BrowseComp, 49% to 35.5%.
GPT-4o mini is also the more expensive model on tokens at $0.15 input / $0.60 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-4o 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-4o mini 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 | GPT-4o mini | Sarvam 30B |
|---|---|---|
| AgenticGPT-4o mini wins | ||
| Terminal-Bench 2.0 | 58% | — |
| BrowseComp | 49% | 35.5% |
| OSWorld-Verified | 44% | — |
| CodingGPT-4o mini wins | ||
| HumanEval | 87.2% | 92.1% |
| SWE-bench Pro | 65% | — |
| LiveCodeBench v6 | — | 70.0% |
| SWE-bench Verified | — | 34% |
| Multimodal & Grounded | ||
| MMMU-Pro | 66% | — |
| OfficeQA Pro | 53% | — |
| Reasoning | ||
| LongBench v2 | 49% | — |
| MRCRv2 | 50% | — |
| gpqaDiamond | — | 66.5% |
| Knowledge | ||
| MMLU | 82% | 85.1% |
| MMLU-Pro | — | 80% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| MGSM | 87% | — |
| MMLU-ProX | 68% | — |
| Mathematics | ||
| MATH-500 | — | 97% |
| AIME 2025 | — | 80% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
GPT-4o mini is ahead overall, 54 to 48. The biggest single separator in this matchup is BrowseComp, where the scores are 49% and 35.5%.
GPT-4o mini has the edge for coding in this comparison, averaging 65 versus 34. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for agentic tasks in this comparison, averaging 50.9 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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