Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4 mini
66
Winner · 3/8 categoriesSarvam 30B
48
1/8 categoriesGPT-5.4 mini· Sarvam 30B
Pick GPT-5.4 mini 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 mini is clearly ahead on the aggregate, 66 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 mini's sharpest advantage is in agentic, where it averages 65.6 against 35.5. The single biggest benchmark swing on the page is MATH-500, 97.4% to 97%. 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 mini is also the more expensive model on tokens at $0.75 input / $4.50 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 mini 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 mini | Sarvam 30B |
|---|---|---|
| AgenticGPT-5.4 mini wins | ||
| Terminal-Bench 2.0 | 60% | — |
| OSWorld-Verified | 72.1% | — |
| MCP Atlas | 57.7% | — |
| Toolathlon | 42.9% | — |
| Tau2-Telecom | 93.4% | — |
| BrowseComp | — | 35.5% |
| CodingGPT-5.4 mini wins | ||
| SWE-bench Pro | 54.4% | — |
| HumanEval | — | 92.1% |
| LiveCodeBench v6 | — | 70.0% |
| SWE-bench Verified | — | 34% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| MRCRv2 | 40.7% | — |
| MRCR v2 64K-128K | 47.7% | — |
| MRCR v2 128K-256K | 33.6% | — |
| Graphwalks Parents 128K | 71.5% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| GPQA | 88% | — |
| HLE | 41.5% | — |
| HLE w/o tools | 28.2% | — |
| MMLU | — | 85.1% |
| MMLU-Pro | — | 80% |
| Instruction Following | ||
| IFEval | 87.4% | — |
| Multilingual | ||
| Coming soon | ||
| MathematicsGPT-5.4 mini wins | ||
| MATH-500 | 97.4% | 97% |
| AIME 2025 | — | 80% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
GPT-5.4 mini is ahead overall, 66 to 48. The biggest single separator in this matchup is MATH-500, where the scores are 97.4% and 97%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 57.4. GPT-5.4 mini stays close enough that the answer can still flip depending on your workload.
GPT-5.4 mini has the edge for coding in this comparison, averaging 54.4 versus 34. Sarvam 30B stays close enough that the answer can still flip depending on your workload.
GPT-5.4 mini has the edge for math in this comparison, averaging 97.4 versus 86.5. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GPT-5.4 mini has the edge for agentic tasks in this comparison, averaging 65.6 versus 35.5. Sarvam 30B stays close enough that the answer can still flip depending on your workload.
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