Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 (high) is clearly ahead on the aggregate, 79 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5 (high)'s sharpest advantage is in coding, where it averages 66.1 against 7.2. The single biggest benchmark swing on the page is HumanEval, 85 to 14.
GPT-5 (high) 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. GPT-5 (high) gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Instruct.
Pick GPT-5 (high) 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.
GPT-5 (high)
75.2
LFM2.5-1.2B-Instruct
25.7
GPT-5 (high)
66.1
LFM2.5-1.2B-Instruct
7.2
GPT-5 (high)
89.4
LFM2.5-1.2B-Instruct
32.4
GPT-5 (high)
85.7
LFM2.5-1.2B-Instruct
32.1
GPT-5 (high)
71.1
LFM2.5-1.2B-Instruct
26
GPT-5 (high)
91
LFM2.5-1.2B-Instruct
80
GPT-5 (high)
86.4
LFM2.5-1.2B-Instruct
60.7
GPT-5 (high)
94
LFM2.5-1.2B-Instruct
37
GPT-5 (high) is ahead overall, 79 to 30. The biggest single separator in this matchup is HumanEval, where the scores are 85 and 14.
GPT-5 (high) has the edge for knowledge tasks in this comparison, averaging 71.1 versus 26. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for coding in this comparison, averaging 66.1 versus 7.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for math in this comparison, averaging 94 versus 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for reasoning in this comparison, averaging 85.7 versus 32.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for agentic tasks in this comparison, averaging 75.2 versus 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for multimodal and grounded tasks in this comparison, averaging 89.4 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5 (high) 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.
GPT-5 (high) has the edge for multilingual tasks in this comparison, averaging 86.4 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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