Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
LFM2.5-1.2B-Thinking finishes one point ahead overall, 33 to 32. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
LFM2.5-1.2B-Thinking's sharpest advantage is in agentic, where it averages 34.1 against 27.9. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 34 to 24. Mistral 8x7B v0.2 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
LFM2.5-1.2B-Thinking is the reasoning model in the pair, while Mistral 8x7B v0.2 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.
Pick LFM2.5-1.2B-Thinking if you want the stronger benchmark profile. Mistral 8x7B v0.2 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
LFM2.5-1.2B-Thinking
34.1
Mistral 8x7B v0.2
27.9
LFM2.5-1.2B-Thinking
8.2
Mistral 8x7B v0.2
13.3
LFM2.5-1.2B-Thinking
32.4
Mistral 8x7B v0.2
32.3
LFM2.5-1.2B-Thinking
38.4
Mistral 8x7B v0.2
35.2
LFM2.5-1.2B-Thinking
27
Mistral 8x7B v0.2
28.9
LFM2.5-1.2B-Thinking
72
Mistral 8x7B v0.2
67
LFM2.5-1.2B-Thinking
60.7
Mistral 8x7B v0.2
58.8
LFM2.5-1.2B-Thinking
42.3
Mistral 8x7B v0.2
42
LFM2.5-1.2B-Thinking is ahead overall, 33 to 32. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 34 and 24.
Mistral 8x7B v0.2 has the edge for knowledge tasks in this comparison, averaging 28.9 versus 27. Inside this category, FrontierScience is the benchmark that creates the most daylight between them.
Mistral 8x7B v0.2 has the edge for coding in this comparison, averaging 13.3 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 42. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for reasoning in this comparison, averaging 38.4 versus 35.2. Inside this category, MuSR is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for agentic tasks in this comparison, averaging 34.1 versus 27.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for multimodal and grounded tasks in this comparison, averaging 32.4 versus 32.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for instruction following in this comparison, averaging 72 versus 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for multilingual tasks in this comparison, averaging 60.7 versus 58.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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