Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4o mini is clearly ahead on the aggregate, 52 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4o mini's sharpest advantage is in coding, where it averages 65 against 18. The single biggest benchmark swing on the page is SWE-bench Pro, 65 to 19.
GPT-4o mini is also the more expensive model on tokens at $0.15 input / $0.60 output per 1M tokens, versus $0.03 input / $0.12 output per 1M tokens for LFM2-24B-A2B. That is roughly 5.0x on output cost alone. GPT-4o mini gives you the larger context window at 128K, compared with 32K for LFM2-24B-A2B.
Pick GPT-4o mini if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if you want the cheaper token bill.
GPT-4o mini
50.9
LFM2-24B-A2B
33.4
GPT-4o mini
65
LFM2-24B-A2B
18
GPT-4o mini
60.2
LFM2-24B-A2B
41.7
GPT-4o mini
49.4
LFM2-24B-A2B
46.6
GPT-4o mini
62
LFM2-24B-A2B
35.6
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GPT-4o mini
74.7
LFM2-24B-A2B
61.4
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GPT-4o mini is ahead overall, 52 to 38. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 65 and 19.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 62 versus 35.6. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for coding in this comparison, averaging 65 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for reasoning in this comparison, averaging 49.4 versus 46.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for agentic tasks in this comparison, averaging 50.9 versus 33.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for multimodal and grounded tasks in this comparison, averaging 60.2 versus 41.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for multilingual tasks in this comparison, averaging 74.7 versus 61.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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