Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 nano
44
0/8 categoriesSarvam 105B
60
Winner · 4/8 categoriesGPT-4.1 nano· Sarvam 105B
Pick Sarvam 105B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window 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 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Sarvam 105B's sharpest advantage is in knowledge, where it averages 81.7 against 50.7. The single biggest benchmark swing on the page is BrowseComp, 62% to 49.5%.
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 105B. That is roughly Infinityx on output cost alone. Sarvam 105B 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 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 nano | Sarvam 105B |
|---|---|---|
| AgenticSarvam 105B wins | ||
| Terminal-Bench 2.0 | 43% | — |
| BrowseComp | 62% | 49.5% |
| OSWorld-Verified | 42% | — |
| CodingSarvam 105B wins | ||
| SWE-bench Pro | 18% | — |
| LiveCodeBench v6 | — | 71.7% |
| SWE-bench Verified | — | 45% |
| Multimodal & Grounded | ||
| MMMU-Pro | 53% | — |
| OfficeQA Pro | 67% | — |
| Reasoning | ||
| LongBench v2 | 75% | — |
| MRCRv2 | 73% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 80.1% | 90.6% |
| GPQA | 50.3% | — |
| FrontierScience | 51% | — |
| MMLU-Pro | — | 81.7% |
| Instruction FollowingSarvam 105B wins | ||
| IFEval | 83.2% | 84.8% |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| AIME 2024 | 9.8% | — |
| 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 44. The biggest single separator in this matchup is BrowseComp, where the scores are 62% and 49.5%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 50.7. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Sarvam 105B has the edge for coding in this comparison, averaging 45 versus 18. GPT-4.1 nano stays close enough that the answer can still flip depending on your workload.
Sarvam 105B has the edge for agentic tasks in this comparison, averaging 49.5 versus 47.4. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Sarvam 105B has the edge for instruction following in this comparison, averaging 84.8 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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