Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Kimi K2.5 (Reasoning) is clearly ahead on the aggregate, 76 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in coding, where it averages 64.1 against 7.2. The single biggest benchmark swing on the page is HumanEval, 84 to 14.
Kimi K2.5 (Reasoning) is the reasoning model in the pair, while LFM2.5-1.2B-Instruct 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 K2.5 (Reasoning) gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Instruct.
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. LFM2.5-1.2B-Instruct only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Kimi K2.5 (Reasoning)
73.1
LFM2.5-1.2B-Instruct
25.7
Kimi K2.5 (Reasoning)
64.1
LFM2.5-1.2B-Instruct
7.2
Kimi K2.5 (Reasoning)
74.3
LFM2.5-1.2B-Instruct
32.4
Kimi K2.5 (Reasoning)
84.9
LFM2.5-1.2B-Instruct
32.1
Kimi K2.5 (Reasoning)
69.7
LFM2.5-1.2B-Instruct
26
Kimi K2.5 (Reasoning)
91
LFM2.5-1.2B-Instruct
80
Kimi K2.5 (Reasoning)
86.7
LFM2.5-1.2B-Instruct
60.7
Kimi K2.5 (Reasoning)
92.6
LFM2.5-1.2B-Instruct
37
Kimi K2.5 (Reasoning) is ahead overall, 76 to 30. The biggest single separator in this matchup is HumanEval, where the scores are 84 and 14.
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 69.7 versus 26. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 64.1 versus 7.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for math in this comparison, averaging 92.6 versus 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for reasoning in this comparison, averaging 84.9 versus 32.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for agentic tasks in this comparison, averaging 73.1 versus 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for multimodal and grounded tasks in this comparison, averaging 74.3 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for instruction following in this comparison, averaging 91 versus 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 86.7 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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