Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4o mini
54
2/8 categoriesSarvam 105B
60
Winner · 0/8 categoriesGPT-4o mini· Sarvam 105B
Pick Sarvam 105B if you want the stronger benchmark profile. GPT-4o mini only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Sarvam 105B is clearly ahead on the aggregate, 60 to 54. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
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 105B. That is roughly Infinityx on output cost alone. Sarvam 105B 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.
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 105B |
|---|---|---|
| AgenticGPT-4o mini wins | ||
| Terminal-Bench 2.0 | 58% | — |
| BrowseComp | 49% | 49.5% |
| OSWorld-Verified | 44% | — |
| CodingGPT-4o mini wins | ||
| HumanEval | 87.2% | — |
| SWE-bench Pro | 65% | — |
| LiveCodeBench v6 | — | 71.7% |
| SWE-bench Verified | — | 45% |
| Multimodal & Grounded | ||
| MMMU-Pro | 66% | — |
| OfficeQA Pro | 53% | — |
| Reasoning | ||
| LongBench v2 | 49% | — |
| MRCRv2 | 50% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| Knowledge | ||
| MMLU | 82% | 90.6% |
| MMLU-Pro | — | 81.7% |
| Instruction Following | ||
| IFEval | — | 84.8% |
| Multilingual | ||
| MGSM | 87% | — |
| MMLU-ProX | 68% | — |
| Mathematics | ||
| MATH-500 | — | 98.6% |
| AIME 2025 | — | 88.3% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
Sarvam 105B is ahead overall, 60 to 54. The biggest single separator in this matchup is MMLU, where the scores are 82% and 90.6%.
GPT-4o mini has the edge for coding in this comparison, averaging 65 versus 45. Sarvam 105B stays close enough that the answer can still flip depending on your workload.
GPT-4o mini has the edge for agentic tasks in this comparison, averaging 50.9 versus 49.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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