Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4 Pro is clearly ahead on the aggregate, 91 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 Pro's sharpest advantage is in coding, where it averages 87.4 against 18. The single biggest benchmark swing on the page is SWE-bench Pro, 89 to 19.
GPT-5.4 Pro is also the more expensive model on tokens at $30.00 input / $180.00 output per 1M tokens, versus $0.03 input / $0.12 output per 1M tokens for LFM2-24B-A2B. That is roughly 1500.0x on output cost alone. GPT-5.4 Pro 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-5.4 Pro gives you the larger context window at 1.05M, compared with 32K for LFM2-24B-A2B.
Pick GPT-5.4 Pro if you want the stronger benchmark profile. LFM2-24B-A2B 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-5.4 Pro
87.4
LFM2-24B-A2B
33.4
GPT-5.4 Pro
87.4
LFM2-24B-A2B
18
GPT-5.4 Pro
94.9
LFM2-24B-A2B
41.7
GPT-5.4 Pro
96.1
LFM2-24B-A2B
46.6
GPT-5.4 Pro
83.8
LFM2-24B-A2B
35.6
GPT-5.4 Pro
97
LFM2-24B-A2B
68
GPT-5.4 Pro
95.7
LFM2-24B-A2B
61.4
GPT-5.4 Pro
98.2
LFM2-24B-A2B
50.4
GPT-5.4 Pro is ahead overall, 91 to 38. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 89 and 19.
GPT-5.4 Pro has the edge for knowledge tasks in this comparison, averaging 83.8 versus 35.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for coding in this comparison, averaging 87.4 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for math in this comparison, averaging 98.2 versus 50.4. Inside this category, HMMT Feb 2023 is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for reasoning in this comparison, averaging 96.1 versus 46.6. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for agentic tasks in this comparison, averaging 87.4 versus 33.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 94.9 versus 41.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for instruction following in this comparison, averaging 97 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for multilingual tasks in this comparison, averaging 95.7 versus 61.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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