Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4 Turbo is clearly ahead on the aggregate, 47 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4 Turbo's sharpest advantage is in reasoning, where it averages 61 against 32.1. The single biggest benchmark swing on the page is HumanEval, 52 to 14.
GPT-4 Turbo gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Instruct.
Pick GPT-4 Turbo if you want the stronger benchmark profile. LFM2.5-1.2B-Instruct only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GPT-4 Turbo
44.7
LFM2.5-1.2B-Instruct
25.7
GPT-4 Turbo
17.2
LFM2.5-1.2B-Instruct
7.2
GPT-4 Turbo
55.3
LFM2.5-1.2B-Instruct
32.4
GPT-4 Turbo
61
LFM2.5-1.2B-Instruct
32.1
GPT-4 Turbo
41.1
LFM2.5-1.2B-Instruct
26
GPT-4 Turbo
80
LFM2.5-1.2B-Instruct
80
GPT-4 Turbo
68.5
LFM2.5-1.2B-Instruct
60.7
GPT-4 Turbo
64.4
LFM2.5-1.2B-Instruct
37
GPT-4 Turbo is ahead overall, 47 to 30. The biggest single separator in this matchup is HumanEval, where the scores are 52 and 14.
GPT-4 Turbo has the edge for knowledge tasks in this comparison, averaging 41.1 versus 26. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for coding in this comparison, averaging 17.2 versus 7.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for math in this comparison, averaging 64.4 versus 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for reasoning in this comparison, averaging 61 versus 32.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for agentic tasks in this comparison, averaging 44.7 versus 25.7. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for multimodal and grounded tasks in this comparison, averaging 55.3 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-4 Turbo and LFM2.5-1.2B-Instruct are effectively tied for instruction following here, both landing at 80 on average.
GPT-4 Turbo has the edge for multilingual tasks in this comparison, averaging 68.5 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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