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 26.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 34 to 24. Mistral 7B v0.3 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 7B v0.3 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 7B v0.3 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 7B v0.3
26.4
LFM2.5-1.2B-Thinking
8.2
Mistral 7B v0.3
13.2
LFM2.5-1.2B-Thinking
32.4
Mistral 7B v0.3
32.4
LFM2.5-1.2B-Thinking
38.4
Mistral 7B v0.3
36.1
LFM2.5-1.2B-Thinking
27
Mistral 7B v0.3
30
LFM2.5-1.2B-Thinking
72
Mistral 7B v0.3
68
LFM2.5-1.2B-Thinking
60.7
Mistral 7B v0.3
60.7
LFM2.5-1.2B-Thinking
42.3
Mistral 7B v0.3
43
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 7B v0.3 has the edge for knowledge tasks in this comparison, averaging 30 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Mistral 7B v0.3 has the edge for coding in this comparison, averaging 13.2 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Mistral 7B v0.3 has the edge for math in this comparison, averaging 43 versus 42.3. Inside this category, AIME 2023 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 36.1. 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 26.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking and Mistral 7B v0.3 are effectively tied for multimodal and grounded tasks here, both landing at 32.4 on average.
LFM2.5-1.2B-Thinking has the edge for instruction following in this comparison, averaging 72 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking and Mistral 7B v0.3 are effectively tied for multilingual tasks here, both landing at 60.7 on average.
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