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 31. 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 multimodal & grounded, where it averages 41.7 against 30.4. The single biggest benchmark swing on the page is HumanEval, 42 to 16. Ministral 3 3B (Reasoning) does hit back in agentic, 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 Ministral 3 3B (Reasoning). That is roughly Infinityx on output cost alone. Ministral 3 3B (Reasoning) 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. Ministral 3 3B (Reasoning) gives you the larger context window at 128K, compared with 32K for LFM2-24B-A2B.
Pick LFM2-24B-A2B if you want the stronger benchmark profile. Ministral 3 3B (Reasoning) only becomes the better choice if agentic is the priority or you want the cheaper token bill.
LFM2-24B-A2B
33.4
Ministral 3 3B (Reasoning)
34
LFM2-24B-A2B
18
Ministral 3 3B (Reasoning)
7.2
LFM2-24B-A2B
41.7
Ministral 3 3B (Reasoning)
30.4
LFM2-24B-A2B
46.6
Ministral 3 3B (Reasoning)
35.3
LFM2-24B-A2B
35.6
Ministral 3 3B (Reasoning)
25.2
LFM2-24B-A2B
68
Ministral 3 3B (Reasoning)
68
LFM2-24B-A2B
61.4
Ministral 3 3B (Reasoning)
59.7
LFM2-24B-A2B
50.4
Ministral 3 3B (Reasoning)
40.9
LFM2-24B-A2B is ahead overall, 38 to 31. The biggest single separator in this matchup is HumanEval, where the scores are 42 and 16.
LFM2-24B-A2B has the edge for knowledge tasks in this comparison, averaging 35.6 versus 25.2. 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 40.9. 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 35.3. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
Ministral 3 3B (Reasoning) has the edge for agentic tasks in this comparison, averaging 34 versus 33.4. 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 30.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
LFM2-24B-A2B and Ministral 3 3B (Reasoning) are effectively tied for instruction following here, both landing at 68 on average.
LFM2-24B-A2B has the edge for multilingual tasks in this comparison, averaging 61.4 versus 59.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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