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 35. 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 multilingual, where it averages 61.4 against 42. The single biggest benchmark swing on the page is MMLU, 46 to 71.4. Mixtral 8x22B Instruct v0.1 does hit back in coding, 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 Mixtral 8x22B Instruct v0.1. That is roughly Infinityx on output cost alone. Mixtral 8x22B Instruct v0.1 gives you the larger context window at 64K, compared with 32K for LFM2-24B-A2B.
Pick LFM2-24B-A2B if you want the stronger benchmark profile. Mixtral 8x22B Instruct v0.1 only becomes the better choice if coding is the priority or you want the cheaper token bill.
LFM2-24B-A2B
33.4
Mixtral 8x22B Instruct v0.1
31.8
LFM2-24B-A2B
18
Mixtral 8x22B Instruct v0.1
40
LFM2-24B-A2B
41.7
Mixtral 8x22B Instruct v0.1
35.5
LFM2-24B-A2B
46.6
Mixtral 8x22B Instruct v0.1
38.6
LFM2-24B-A2B
35.6
Mixtral 8x22B Instruct v0.1
53
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
LFM2-24B-A2B
61.4
Mixtral 8x22B Instruct v0.1
42
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
LFM2-24B-A2B is ahead overall, 38 to 35. The biggest single separator in this matchup is MMLU, where the scores are 46 and 71.4.
Mixtral 8x22B Instruct v0.1 has the edge for knowledge tasks in this comparison, averaging 53 versus 35.6. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Mixtral 8x22B Instruct v0.1 has the edge for coding in this comparison, averaging 40 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 reasoning in this comparison, averaging 46.6 versus 38.6. Inside this category, LongBench v2 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 31.8. Inside this category, BrowseComp 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 35.5. Inside this category, OfficeQA Pro 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 42. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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