Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 (high)
82
Winner · 2/8 categoriesLFM2.5-350M
~39
0/8 categoriesGPT-5 (high)· LFM2.5-350M
Pick GPT-5 (high) if you want the stronger benchmark profile. LFM2.5-350M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5 (high) is clearly ahead on the aggregate, 82 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5 (high)'s sharpest advantage is in knowledge, where it averages 72.6 against 23.8. The single biggest benchmark swing on the page is MMLU-Pro, 83% to 20.0%.
GPT-5 (high) is the reasoning model in the pair, while LFM2.5-350M 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 (high) gives you the larger context window at 128K, compared with 32K for LFM2.5-350M.
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 (high) | LFM2.5-350M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 78% | — |
| BrowseComp | 75% | — |
| OSWorld-Verified | 72% | — |
| Coding | ||
| HumanEval | 85% | — |
| SWE-bench Verified | 67% | — |
| LiveCodeBench | 62% | — |
| SWE-bench Pro | 70% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 93% | — |
| OfficeQA Pro | 85% | — |
| Reasoning | ||
| MuSR | 87% | — |
| BBH | 94% | — |
| LongBench v2 | 83% | — |
| MRCRv2 | 80% | — |
| KnowledgeGPT-5 (high) wins | ||
| MMLU | 93% | — |
| GPQA | 91% | 30.6% |
| SuperGPQA | 89% | — |
| MMLU-Pro | 83% | 20.0% |
| HLE | 27% | — |
| FrontierScience | 83% | — |
| SimpleQA | 89% | — |
| Instruction FollowingGPT-5 (high) wins | ||
| IFEval | 91% | 77.0% |
| Multilingual | ||
| MGSM | 89% | — |
| MMLU-ProX | 85% | — |
| Mathematics | ||
| AIME 2023 | 95% | — |
| AIME 2024 | 97% | — |
| AIME 2025 | 96% | — |
| HMMT Feb 2023 | 91% | — |
| HMMT Feb 2024 | 93% | — |
| HMMT Feb 2025 | 92% | — |
| BRUMO 2025 | 94% | — |
| MATH-500 | 94% | — |
GPT-5 (high) is ahead overall, 82 to 39. The biggest single separator in this matchup is MMLU-Pro, where the scores are 83% and 20.0%.
GPT-5 (high) has the edge for knowledge tasks in this comparison, averaging 72.6 versus 23.8. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for instruction following in this comparison, averaging 91 versus 77. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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