Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4.1 mini is clearly ahead on the aggregate, 58 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4.1 mini's sharpest advantage is in reasoning, where it averages 80.9 against 46.6. The single biggest benchmark swing on the page is MMLU, 87.5 to 46. LFM2-24B-A2B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 output per 1M tokens, versus $0.03 input / $0.12 output per 1M tokens for LFM2-24B-A2B. That is roughly 13.3x on output cost alone. GPT-4.1 mini gives you the larger context window at 1M, compared with 32K for LFM2-24B-A2B.
Pick GPT-4.1 mini if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
GPT-4.1 mini
56.5
LFM2-24B-A2B
33.4
GPT-4.1 mini
28.8
LFM2-24B-A2B
18
GPT-4.1 mini
69.6
LFM2-24B-A2B
41.7
GPT-4.1 mini
80.9
LFM2-24B-A2B
46.6
GPT-4.1 mini
62.4
LFM2-24B-A2B
35.6
GPT-4.1 mini
88.5
LFM2-24B-A2B
68
GPT-4.1 mini
72
LFM2-24B-A2B
61.4
GPT-4.1 mini
23.1
LFM2-24B-A2B
50.4
GPT-4.1 mini is ahead overall, 58 to 38. The biggest single separator in this matchup is MMLU, where the scores are 87.5 and 46.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 62.4 versus 35.6. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for coding in this comparison, averaging 28.8 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for math in this comparison, averaging 50.4 versus 23.1. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for reasoning in this comparison, averaging 80.9 versus 46.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for agentic tasks in this comparison, averaging 56.5 versus 33.4. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for multimodal and grounded tasks in this comparison, averaging 69.6 versus 41.7. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for instruction following in this comparison, averaging 88.5 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for multilingual tasks in this comparison, averaging 72 versus 61.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.