Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
LFM2-24B-A2B has the cleaner overall profile here, landing at 38 versus 36. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
LFM2-24B-A2B's sharpest advantage is in multimodal & grounded, where it averages 41.7 against 33.4. The single biggest benchmark swing on the page is HumanEval, 42 to 24. Ministral 3 8B (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 8B (Reasoning). That is roughly Infinityx on output cost alone. Ministral 3 8B (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 8B (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 8B (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 8B (Reasoning)
38.5
LFM2-24B-A2B
18
Ministral 3 8B (Reasoning)
15.2
LFM2-24B-A2B
41.7
Ministral 3 8B (Reasoning)
33.4
LFM2-24B-A2B
46.6
Ministral 3 8B (Reasoning)
42.1
LFM2-24B-A2B
35.6
Ministral 3 8B (Reasoning)
30
LFM2-24B-A2B
68
Ministral 3 8B (Reasoning)
70
LFM2-24B-A2B
61.4
Ministral 3 8B (Reasoning)
61.7
LFM2-24B-A2B
50.4
Ministral 3 8B (Reasoning)
47.8
LFM2-24B-A2B is ahead overall, 38 to 36. The biggest single separator in this matchup is HumanEval, where the scores are 42 and 24.
LFM2-24B-A2B has the edge for knowledge tasks in this comparison, averaging 35.6 versus 30. 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 15.2. 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 47.8. 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 42.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
Ministral 3 8B (Reasoning) has the edge for agentic tasks in this comparison, averaging 38.5 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 33.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Ministral 3 8B (Reasoning) has the edge for instruction following in this comparison, averaging 70 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Ministral 3 8B (Reasoning) has the edge for multilingual tasks in this comparison, averaging 61.7 versus 61.4. 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.