Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Kimi K2 finishes one point ahead overall, 34 to 33. 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.
Kimi K2's sharpest advantage is in multimodal & grounded, where it averages 39.5 against 32.4. The single biggest benchmark swing on the page is MMMU-Pro, 35 to 27. LFM2.5-1.2B-Thinking does hit back in instruction following, 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 Kimi K2 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. Kimi K2 gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Thinking.
Pick Kimi K2 if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if instruction following is the priority or you want the stronger reasoning-first profile.
Kimi K2
29.3
LFM2.5-1.2B-Thinking
34.1
Kimi K2
12.8
LFM2.5-1.2B-Thinking
8.2
Kimi K2
39.5
LFM2.5-1.2B-Thinking
32.4
Kimi K2
40.9
LFM2.5-1.2B-Thinking
38.4
Kimi K2
29.3
LFM2.5-1.2B-Thinking
27
Kimi K2
67
LFM2.5-1.2B-Thinking
72
Kimi K2
59.7
LFM2.5-1.2B-Thinking
60.7
Kimi K2
42.7
LFM2.5-1.2B-Thinking
42.3
Kimi K2 is ahead overall, 34 to 33. The biggest single separator in this matchup is MMMU-Pro, where the scores are 35 and 27.
Kimi K2 has the edge for knowledge tasks in this comparison, averaging 29.3 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Kimi K2 has the edge for coding in this comparison, averaging 12.8 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Kimi K2 has the edge for math in this comparison, averaging 42.7 versus 42.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Kimi K2 has the edge for reasoning in this comparison, averaging 40.9 versus 38.4. Inside this category, LongBench v2 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 29.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Kimi K2 has the edge for multimodal and grounded tasks in this comparison, averaging 39.5 versus 32.4. 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 59.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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