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 33. 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 38.4. The single biggest benchmark swing on the page is MMLU, 87.5 to 27. LFM2.5-1.2B-Thinking 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 $0.00 input / $0.00 output per 1M tokens for LFM2.5-1.2B-Thinking. That is roughly Infinityx on output cost alone. LFM2.5-1.2B-Thinking is the reasoning model in the pair, while GPT-4.1 mini 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-4.1 mini gives you the larger context window at 1M, compared with 32K for LFM2.5-1.2B-Thinking.
Pick GPT-4.1 mini if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
GPT-4.1 mini
56.5
LFM2.5-1.2B-Thinking
34.1
GPT-4.1 mini
28.8
LFM2.5-1.2B-Thinking
8.2
GPT-4.1 mini
69.6
LFM2.5-1.2B-Thinking
32.4
GPT-4.1 mini
80.9
LFM2.5-1.2B-Thinking
38.4
GPT-4.1 mini
62.4
LFM2.5-1.2B-Thinking
27
GPT-4.1 mini
88.5
LFM2.5-1.2B-Thinking
72
GPT-4.1 mini
72
LFM2.5-1.2B-Thinking
60.7
GPT-4.1 mini
23.1
LFM2.5-1.2B-Thinking
42.3
GPT-4.1 mini is ahead overall, 58 to 33. The biggest single separator in this matchup is MMLU, where the scores are 87.5 and 27.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 62.4 versus 27. 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 8.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for math in this comparison, averaging 42.3 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 38.4. Inside this category, LongBench v2 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 34.1. 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 32.4. Inside this category, MMMU-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 72. 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 60.7. 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.