Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4 nano
58
1/8 categoriesSarvam 105B
60
Winner · 2/8 categoriesGPT-5.4 nano· Sarvam 105B
Pick Sarvam 105B if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if coding is the priority or you need the larger 400K context window.
Sarvam 105B has the cleaner overall profile here, landing at 60 versus 58. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Sarvam 105B's sharpest advantage is in knowledge, where it averages 81.7 against 53.2. GPT-5.4 nano does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 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. GPT-5.4 nano gives you the larger context window at 400K, 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-5.4 nano | Sarvam 105B |
|---|---|---|
| AgenticSarvam 105B wins | ||
| Terminal-Bench 2.0 | 46.3% | — |
| OSWorld-Verified | 39% | — |
| MCP Atlas | 56.1% | — |
| Toolathlon | 35.5% | — |
| Tau2-Telecom | 92.5% | — |
| BrowseComp | — | 49.5% |
| CodingGPT-5.4 nano wins | ||
| SWE-bench Pro | 52.4% | — |
| LiveCodeBench v6 | — | 71.7% |
| SWE-bench Verified | — | 45% |
| Multimodal & Grounded | ||
| MMMU-Pro | 66.1% | — |
| MMMU-Pro w/ Python | 69.5% | — |
| Reasoning | ||
| MRCRv2 | 38.7% | — |
| MRCR v2 64K-128K | 44.2% | — |
| MRCR v2 128K-256K | 33.1% | — |
| Graphwalks BFS 128K | 73.4% | — |
| Graphwalks Parents 128K | 50.8% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| GPQA | 82.8% | — |
| HLE | 37.7% | — |
| HLE w/o tools | 24.3% | — |
| MMLU | — | 90.6% |
| MMLU-Pro | — | 81.7% |
| Instruction Following | ||
| IFEval | — | 84.8% |
| Multilingual | ||
| Coming soon | ||
| 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 58.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 53.2. GPT-5.4 nano stays close enough that the answer can still flip depending on your workload.
GPT-5.4 nano has the edge for coding in this comparison, averaging 52.4 versus 45. Sarvam 105B 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 42.9. GPT-5.4 nano stays close enough that the answer can still flip depending on your workload.
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