Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1
64
Winner · 3/8 categoriesSarvam 105B
60
1/8 categoriesGPT-4.1· Sarvam 105B
Pick GPT-4.1 if you want the stronger benchmark profile. Sarvam 105B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-4.1 is clearly ahead on the aggregate, 64 to 60. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1's sharpest advantage is in agentic, where it averages 64.7 against 49.5. The single biggest benchmark swing on the page is BrowseComp, 73% to 49.5%. Sarvam 105B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 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-4.1 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 gives you the larger context window at 1M, compared with 128K for Sarvam 105B.
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 | Sarvam 105B |
|---|---|---|
| AgenticGPT-4.1 wins | ||
| Terminal-Bench 2.0 | 61% | — |
| BrowseComp | 73% | 49.5% |
| OSWorld-Verified | 63% | — |
| CodingGPT-4.1 wins | ||
| SWE-bench Verified | 54.6% | 45% |
| SWE-bench Pro | 51% | — |
| LiveCodeBench v6 | — | 71.7% |
| Multimodal & Grounded | ||
| MMMU-Pro | 70% | — |
| OfficeQA Pro | 78% | — |
| Reasoning | ||
| LongBench v2 | 80% | — |
| MRCRv2 | 82% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 90.2% | 90.6% |
| GPQA | 66.3% | — |
| FrontierScience | 61% | — |
| MMLU-Pro | — | 81.7% |
| Instruction FollowingGPT-4.1 wins | ||
| IFEval | 87.4% | 84.8% |
| Multilingual | ||
| MMLU-ProX | 69% | — |
| Mathematics | ||
| AIME 2024 | 26.4% | — |
| MATH-500 | — | 98.6% |
| AIME 2025 | — | 88.3% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
GPT-4.1 is ahead overall, 64 to 60. The biggest single separator in this matchup is BrowseComp, where the scores are 73% and 49.5%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 63.1. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for coding in this comparison, averaging 52.4 versus 45. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for agentic tasks in this comparison, averaging 64.7 versus 49.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for instruction following in this comparison, averaging 87.4 versus 84.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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