Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
LFM2-24B-A2B is clearly ahead on the aggregate, 38 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
LFM2-24B-A2B's sharpest advantage is in reasoning, where it averages 46.6 against 32.1. The single biggest benchmark swing on the page is HumanEval, 42 to 14. LFM2.5-1.2B-Instruct does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
LFM2-24B-A2B is also the more expensive model on tokens at $0.03 input / $0.12 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-1.2B-Instruct. That is roughly Infinityx on output cost alone.
Pick LFM2-24B-A2B if you want the stronger benchmark profile. LFM2.5-1.2B-Instruct only becomes the better choice if instruction following is the priority or you want the cheaper token bill.
LFM2-24B-A2B
33.4
LFM2.5-1.2B-Instruct
25.7
LFM2-24B-A2B
18
LFM2.5-1.2B-Instruct
7.2
LFM2-24B-A2B
41.7
LFM2.5-1.2B-Instruct
32.4
LFM2-24B-A2B
46.6
LFM2.5-1.2B-Instruct
32.1
LFM2-24B-A2B
35.6
LFM2.5-1.2B-Instruct
26
LFM2-24B-A2B
68
LFM2.5-1.2B-Instruct
80
LFM2-24B-A2B
61.4
LFM2.5-1.2B-Instruct
60.7
LFM2-24B-A2B
50.4
LFM2.5-1.2B-Instruct
37
LFM2-24B-A2B is ahead overall, 38 to 30. The biggest single separator in this matchup is HumanEval, where the scores are 42 and 14.
LFM2-24B-A2B has the edge for knowledge tasks in this comparison, averaging 35.6 versus 26. Inside this category, MMLU is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for coding in this comparison, averaging 18 versus 7.2. Inside this category, HumanEval 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 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for reasoning in this comparison, averaging 46.6 versus 32.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for agentic tasks in this comparison, averaging 33.4 versus 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for multimodal and grounded tasks in this comparison, averaging 41.7 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Instruct has the edge for instruction following in this comparison, averaging 80 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for multilingual tasks in this comparison, averaging 61.4 versus 60.7. Inside this category, MGSM 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.