Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.1-Codex-Max is clearly ahead on the aggregate, 84 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.1-Codex-Max's sharpest advantage is in coding, where it averages 75.5 against 18. The single biggest benchmark swing on the page is SWE-bench Pro, 84 to 19.
GPT-5.1-Codex-Max is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.03 input / $0.12 output per 1M tokens for LFM2-24B-A2B. That is roughly 66.7x on output cost alone. GPT-5.1-Codex-Max 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-5.1-Codex-Max gives you the larger context window at 400K, compared with 32K for LFM2-24B-A2B.
Pick GPT-5.1-Codex-Max 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-5.1-Codex-Max
86
LFM2-24B-A2B
33.4
GPT-5.1-Codex-Max
75.5
LFM2-24B-A2B
18
GPT-5.1-Codex-Max
88.2
LFM2-24B-A2B
41.7
GPT-5.1-Codex-Max
92.1
LFM2-24B-A2B
46.6
GPT-5.1-Codex-Max
72.6
LFM2-24B-A2B
35.6
GPT-5.1-Codex-Max
91
LFM2-24B-A2B
68
GPT-5.1-Codex-Max
87.7
LFM2-24B-A2B
61.4
GPT-5.1-Codex-Max
94.9
LFM2-24B-A2B
50.4
GPT-5.1-Codex-Max is ahead overall, 84 to 38. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 84 and 19.
GPT-5.1-Codex-Max has the edge for knowledge tasks in this comparison, averaging 72.6 versus 35.6. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for coding in this comparison, averaging 75.5 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for math in this comparison, averaging 94.9 versus 50.4. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for reasoning in this comparison, averaging 92.1 versus 46.6. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for agentic tasks in this comparison, averaging 86 versus 33.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for multimodal and grounded tasks in this comparison, averaging 88.2 versus 41.7. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max 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-5.1-Codex-Max has the edge for multilingual tasks in this comparison, averaging 87.7 versus 61.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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