Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3 Codex
85
Winner · 4/8 categoriesSarvam 30B
48
0/8 categoriesGPT-5.3 Codex· Sarvam 30B
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Sarvam 30B only becomes the better choice if you want the cheaper token bill.
GPT-5.3 Codex is clearly ahead on the aggregate, 85 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex's sharpest advantage is in agentic, where it averages 74.4 against 35.5. The single biggest benchmark swing on the page is BrowseComp, 88% to 35.5%.
GPT-5.3 Codex is also the more expensive model on tokens at $2.50 input / $10.00 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-5.3 Codex 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-5.3 Codex | Sarvam 30B |
|---|---|---|
| AgenticGPT-5.3 Codex wins | ||
| BrowseComp | 88% | 35.5% |
| OSWorld-Verified | 64.7% | — |
| CodingGPT-5.3 Codex wins | ||
| HumanEval | 95% | 92.1% |
| SWE-bench Verified | 85% | 34% |
| LiveCodeBench | 85% | — |
| SWE-bench Pro | 56.8% | — |
| SWE-Rebench | 58.2% | — |
| React Native Evals | 80.9% | — |
| LiveCodeBench v6 | — | 70.0% |
| Multimodal & Grounded | ||
| MMMU-Pro | 89% | — |
| OfficeQA Pro | 94% | — |
| Reasoning | ||
| MuSR | 93% | — |
| BBH | 98% | — |
| LongBench v2 | 92% | — |
| MRCRv2 | 93% | — |
| gpqaDiamond | — | 66.5% |
| KnowledgeGPT-5.3 Codex wins | ||
| MMLU | 99% | 85.1% |
| GPQA | 97% | — |
| SuperGPQA | 95% | — |
| MMLU-Pro | 90% | 80% |
| HLE | 44% | — |
| FrontierScience | 90% | — |
| SimpleQA | 95% | — |
| Instruction Following | ||
| IFEval | 93% | — |
| Multilingual | ||
| MGSM | 96% | — |
| MMLU-ProX | 91% | — |
| MathematicsGPT-5.3 Codex 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 | 99% | 97% |
| HMMT Feb 2025 | — | 73.3% |
| HMMT Nov 2025 | — | 74.2% |
GPT-5.3 Codex is ahead overall, 85 to 48. The biggest single separator in this matchup is BrowseComp, where the scores are 88% and 35.5%.
GPT-5.3 Codex has the edge for knowledge tasks in this comparison, averaging 81.5 versus 80. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for coding in this comparison, averaging 68.6 versus 34. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for math in this comparison, averaging 97.6 versus 86.5. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 74.4 versus 35.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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