Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3 Codex
85
Winner · 3/8 categoriesGranite-4.0-H-350M
~24
0/8 categoriesGPT-5.3 Codex· Granite-4.0-H-350M
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Granite-4.0-H-350M only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.3 Codex is clearly ahead on the aggregate, 85 to 24. 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 multilingual, where it averages 92.8 against 14.7. The single biggest benchmark swing on the page is MGSM, 96% to 14.7%.
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 Granite-4.0-H-350M. That is roughly Infinityx on output cost alone. GPT-5.3 Codex is the reasoning model in the pair, while Granite-4.0-H-350M 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-5.3 Codex gives you the larger context window at 400K, compared with 32K for Granite-4.0-H-350M.
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 | Granite-4.0-H-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 77.3% | — |
| BrowseComp | 88% | — |
| OSWorld-Verified | 64.7% | — |
| Coding | ||
| SWE-bench Verified | 85% | — |
| LiveCodeBench | 85% | — |
| SWE-bench Pro | 56.8% | — |
| SWE-Rebench | 58.2% | — |
| React Native Evals | 80.9% | — |
| HumanEval | — | 39% |
| Multimodal & Grounded | ||
| MMMU-Pro | 89% | — |
| OfficeQA Pro | 94% | — |
| Reasoning | ||
| BBH | 98% | 33.1% |
| LongBench v2 | 92% | — |
| MRCRv2 | 93% | — |
| KnowledgeGPT-5.3 Codex wins | ||
| MMLU-Pro | 90% | 12.1% |
| HLE | 44% | — |
| FrontierScience | 90% | — |
| SimpleQA | 95% | — |
| MMLU | — | 35.0% |
| GPQA | — | 24.1% |
| Instruction FollowingGPT-5.3 Codex wins | ||
| IFEval | 93% | 55.4% |
| MultilingualGPT-5.3 Codex wins | ||
| MGSM | 96% | 14.7% |
| MMLU-ProX | 91% | — |
| Mathematics | ||
| AIME 2023 | 99% | — |
| AIME 2024 | 99% | — |
| AIME 2025 | 98% | — |
| HMMT Feb 2023 | 95% | — |
| HMMT Feb 2024 | 97% | — |
| HMMT Feb 2025 | 96% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 99% | — |
GPT-5.3 Codex is ahead overall, 85 to 24. The biggest single separator in this matchup is MGSM, where the scores are 96% and 14.7%.
GPT-5.3 Codex has the edge for knowledge tasks in this comparison, averaging 76.9 versus 16.4. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for instruction following in this comparison, averaging 93 versus 55.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for multilingual tasks in this comparison, averaging 92.8 versus 14.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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