Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Phi-4 is clearly ahead on the aggregate, 42 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Phi-4's sharpest advantage is in coding, where it averages 55 against 8.2. The single biggest benchmark swing on the page is HumanEval, 82.6 to 17. LFM2.5-1.2B-Thinking does hit back in reasoning, 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 Phi-4 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. LFM2.5-1.2B-Thinking gives you the larger context window at 32K, compared with 16K for Phi-4.
Pick Phi-4 if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if reasoning is the priority or you need the larger 32K context window.
Phi-4
38.3
LFM2.5-1.2B-Thinking
34.1
Phi-4
55
LFM2.5-1.2B-Thinking
8.2
Phi-4
46.8
LFM2.5-1.2B-Thinking
32.4
Phi-4
31.3
LFM2.5-1.2B-Thinking
38.4
Phi-4
53.8
LFM2.5-1.2B-Thinking
27
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Phi-4
67.2
LFM2.5-1.2B-Thinking
60.7
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Phi-4 is ahead overall, 42 to 33. The biggest single separator in this matchup is HumanEval, where the scores are 82.6 and 17.
Phi-4 has the edge for knowledge tasks in this comparison, averaging 53.8 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Phi-4 has the edge for coding in this comparison, averaging 55 versus 8.2. Inside this category, HumanEval 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 31.3. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Phi-4 has the edge for agentic tasks in this comparison, averaging 38.3 versus 34.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Phi-4 has the edge for multimodal and grounded tasks in this comparison, averaging 46.8 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Phi-4 has the edge for multilingual tasks in this comparison, averaging 67.2 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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