Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 (high) is clearly ahead on the aggregate, 79 to 38. 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 coding, where it averages 66.1 against 18. The single biggest benchmark swing on the page is MMMU-Pro, 93 to 39.
GPT-5 (high) is the reasoning model in the pair, while LFM2-24B-A2B 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 LFM2-24B-A2B.
Pick GPT-5 (high) if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5 (high)
75.2
LFM2-24B-A2B
33.4
GPT-5 (high)
66.1
LFM2-24B-A2B
18
GPT-5 (high)
89.4
LFM2-24B-A2B
41.7
GPT-5 (high)
85.7
LFM2-24B-A2B
46.6
GPT-5 (high)
71.1
LFM2-24B-A2B
35.6
GPT-5 (high)
91
LFM2-24B-A2B
68
GPT-5 (high)
86.4
LFM2-24B-A2B
61.4
GPT-5 (high)
94
LFM2-24B-A2B
50.4
GPT-5 (high) is ahead overall, 79 to 38. The biggest single separator in this matchup is MMMU-Pro, where the scores are 93 and 39.
GPT-5 (high) has the edge for knowledge tasks in this comparison, averaging 71.1 versus 35.6. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for coding in this comparison, averaging 66.1 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for math in this comparison, averaging 94 versus 50.4. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for reasoning in this comparison, averaging 85.7 versus 46.6. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for agentic tasks in this comparison, averaging 75.2 versus 33.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for multimodal and grounded tasks in this comparison, averaging 89.4 versus 41.7. Inside this category, MMMU-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 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for multilingual tasks in this comparison, averaging 86.4 versus 61.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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