Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.2
82
Winner · 4/8 categoriesSarvam 30B
48
0/8 categoriesGPT-5.2· Sarvam 30B
Pick GPT-5.2 if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if you want the cheaper token bill.
GPT-5.2 is clearly ahead on the aggregate, 82 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2's sharpest advantage is in coding, where it averages 70.2 against 34. The single biggest benchmark swing on the page is SWE-bench Verified, 80% to 34%.
GPT-5.2 is also the more expensive model on tokens at $2.00 input / $8.00 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-5.2 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-5.2 | Sarvam 30B |
|---|---|---|
| AgenticGPT-5.2 wins | ||
| Terminal-Bench 2.0 | 83% | — |
| BrowseComp | 65.8% | 35.5% |
| OSWorld-Verified | 47.3% | — |
| CodingGPT-5.2 wins | ||
| SWE-bench Verified | 80% | 34% |
| LiveCodeBench | 79% | — |
| SWE-bench Pro | 55.6% | — |
| HumanEval | — | 92.1% |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| MMMU | 86.7% | — |
| MMMU-Pro | 79.5% | — |
| OfficeQA Pro | 95% | — |
| RealWorldQA | 83.3% | — |
| OmniDocBench 1.5 | 85.7% | — |
| Video-MME (with subtitle) | 86.0% | — |
| Video-MME (w/o subtitle) | 85.8% | — |
| MathVision | 83.0% | — |
| We-Math | 79.0% | — |
| DynaMath | 86.8% | — |
| MStar | 77.1% | — |
| SimpleVQA | 55.8% | — |
| ChatCVQA | 82.1% | — |
| CC-OCR | 70.3% | — |
| AI2D_TEST | 92.2% | — |
| CountBench | 91.9% | — |
| ERQA | 59.8% | — |
| VideoMMMU | 85.9% | — |
| MLVU (M-Avg) | 85.6% | — |
| Reasoning | ||
| MuSR | 93% | — |
| BBH | 96% | — |
| LongBench v2 | 91% | — |
| MRCRv2 | 93% | — |
| ARC-AGI-2 | 52.9% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeGPT-5.2 wins | ||
| MMLU | 99% | 85.1% |
| GPQA | 92.4% | — |
| SuperGPQA | 95% | — |
| MMLU-Pro | 88% | 80% |
| HLE | 42% | — |
| FrontierScience | 91% | — |
| SimpleQA | 95% | — |
| Instruction Following | ||
| IFEval | 94% | — |
| Multilingual | ||
| MGSM | 95% | — |
| MMLU-ProX | 91% | — |
| MathematicsGPT-5.2 wins | ||
| AIME 2023 | 99% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 98% | 80% |
| HMMT Feb 2023 | 95% | — |
| HMMT Feb 2024 | 97% | — |
| HMMT Feb 2025 | 96% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 98% | 97% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
GPT-5.2 is ahead overall, 82 to 48. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 80% and 34%.
GPT-5.2 has the edge for knowledge tasks in this comparison, averaging 80.2 versus 80. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for coding in this comparison, averaging 70.2 versus 34. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for math in this comparison, averaging 97.3 versus 86.5. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for agentic tasks in this comparison, averaging 66.2 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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