Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4 nano
58
Winner · 2/8 categoriesSarvam 30B
48
1/8 categoriesGPT-5.4 nano· Sarvam 30B
Pick GPT-5.4 nano if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-5.4 nano is clearly ahead on the aggregate, 58 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 nano's sharpest advantage is in coding, where it averages 52.4 against 34. Sarvam 30B does hit back in knowledge, 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 30B. That is roughly Infinityx on output cost alone. GPT-5.4 nano gives you the larger context window at 400K, 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-5.4 nano | Sarvam 30B |
|---|---|---|
| AgenticGPT-5.4 nano wins | ||
| Terminal-Bench 2.0 | 46.3% | — |
| OSWorld-Verified | 39% | — |
| MCP Atlas | 56.1% | — |
| Toolathlon | 35.5% | — |
| Tau2-Telecom | 92.5% | — |
| BrowseComp | — | 35.5% |
| CodingGPT-5.4 nano wins | ||
| SWE-bench Pro | 52.4% | — |
| HumanEval | — | 92.1% |
| LiveCodeBench v6 | — | 70.0% |
| SWE-bench Verified | — | 34% |
| 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 | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| GPQA | 82.8% | — |
| HLE | 37.7% | — |
| HLE w/o tools | 24.3% | — |
| MMLU | — | 85.1% |
| MMLU-Pro | — | 80% |
| Instruction Following | ||
| Coming soon | ||
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| MATH-500 | — | 97% |
| AIME 2025 | — | 80% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
GPT-5.4 nano is ahead overall, 58 to 48.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 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 34. Sarvam 30B stays close enough that the answer can still flip depending on your workload.
GPT-5.4 nano has the edge for agentic tasks in this comparison, averaging 42.9 versus 35.5. Sarvam 30B stays close enough that the answer can still flip depending on your workload.
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