Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.4 is clearly ahead on the aggregate, 90 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4's sharpest advantage is in coding, where it averages 84.5 against 18. The single biggest benchmark swing on the page is LiveCodeBench, 84 to 17.
GPT-5.4 is also the more expensive model on tokens at $2.50 input / $15.00 output per 1M tokens, versus $0.03 input / $0.12 output per 1M tokens for LFM2-24B-A2B. That is roughly 125.0x on output cost alone. GPT-5.4 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 gives you the larger context window at 1.05M, compared with 32K for LFM2-24B-A2B.
Pick GPT-5.4 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
87.8
LFM2-24B-A2B
33.4
GPT-5.4
84.5
LFM2-24B-A2B
18
GPT-5.4
95.5
LFM2-24B-A2B
41.7
GPT-5.4
95.8
LFM2-24B-A2B
46.6
GPT-5.4
82.6
LFM2-24B-A2B
35.6
GPT-5.4
96
LFM2-24B-A2B
68
GPT-5.4
94.7
LFM2-24B-A2B
61.4
GPT-5.4
98.2
LFM2-24B-A2B
50.4
GPT-5.4 is ahead overall, 90 to 38. The biggest single separator in this matchup is LiveCodeBench, where the scores are 84 and 17.
GPT-5.4 has the edge for knowledge tasks in this comparison, averaging 82.6 versus 35.6. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for coding in this comparison, averaging 84.5 versus 18. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5.4 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 has the edge for reasoning in this comparison, averaging 95.8 versus 46.6. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for agentic tasks in this comparison, averaging 87.8 versus 33.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for multimodal and grounded tasks in this comparison, averaging 95.5 versus 41.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for instruction following in this comparison, averaging 96 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for multilingual tasks in this comparison, averaging 94.7 versus 61.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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