Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4 Turbo
43
1/8 categoriesSarvam 30B
48
Winner · 3/8 categoriesGPT-4 Turbo· Sarvam 30B
Pick Sarvam 30B if you want the stronger benchmark profile. GPT-4 Turbo only becomes the better choice if agentic is the priority or you need the larger 128K context window.
Sarvam 30B is clearly ahead on the aggregate, 48 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Sarvam 30B's sharpest advantage is in knowledge, where it averages 80 against 42.5. The single biggest benchmark swing on the page is HumanEval, 52% to 92.1%. GPT-4 Turbo does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Sarvam 30B is the reasoning model in the pair, while GPT-4 Turbo 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 Turbo gives you the larger context window at 128K, 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-4 Turbo | Sarvam 30B |
|---|---|---|
| AgenticGPT-4 Turbo wins | ||
| Terminal-Bench 2.0 | 42% | — |
| BrowseComp | 54% | 35.5% |
| OSWorld-Verified | 41% | — |
| CodingSarvam 30B wins | ||
| HumanEval | 52% | 92.1% |
| SWE-bench Verified | 5% | 34% |
| SWE-bench Pro | 14% | — |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| MMMU-Pro | 53% | — |
| OfficeQA Pro | 58% | — |
| Reasoning | ||
| MuSR | 56% | — |
| BBH | 75% | — |
| LongBench v2 | 62% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeSarvam 30B wins | ||
| MMLU | 60% | 85.1% |
| GPQA | 60% | — |
| SuperGPQA | 58% | — |
| MMLU-Pro | 51% | 80% |
| HLE | 1% | — |
| FrontierScience | 52% | — |
| SimpleQA | 58% | — |
| Instruction Following | ||
| IFEval | 80% | — |
| Multilingual | ||
| MGSM | 75% | — |
| MMLU-ProX | 65% | — |
| MathematicsSarvam 30B wins | ||
| AIME 2023 | 60% | — |
| AIME 2024 | 62% | — |
| HMMT Feb 2025 | 57% | — |
| BRUMO 2025 | 59% | — |
| MATH-500 | — | 97% |
| AIME 2025 | — | 80% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
Sarvam 30B is ahead overall, 48 to 43. The biggest single separator in this matchup is HumanEval, where the scores are 52% and 92.1%.
Sarvam 30B has the edge for knowledge tasks in this comparison, averaging 80 versus 42.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Sarvam 30B has the edge for coding in this comparison, averaging 34 versus 10.6. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Sarvam 30B has the edge for math in this comparison, averaging 86.5 versus 59. GPT-4 Turbo stays close enough that the answer can still flip depending on your workload.
GPT-4 Turbo has the edge for agentic tasks in this comparison, averaging 44.7 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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