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 38. 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 46.6. The single biggest benchmark swing on the page is MRCRv2, 62 to 45. LFM2-24B-A2B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-4 Turbo gives you the larger context window at 128K, compared with 32K for LFM2-24B-A2B.
Pick GPT-4 Turbo if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if coding is the priority.
GPT-4 Turbo
44.7
LFM2-24B-A2B
33.4
GPT-4 Turbo
17.2
LFM2-24B-A2B
18
GPT-4 Turbo
55.3
LFM2-24B-A2B
41.7
GPT-4 Turbo
61
LFM2-24B-A2B
46.6
GPT-4 Turbo
41.1
LFM2-24B-A2B
35.6
GPT-4 Turbo
80
LFM2-24B-A2B
68
GPT-4 Turbo
68.5
LFM2-24B-A2B
61.4
GPT-4 Turbo
64.4
LFM2-24B-A2B
50.4
GPT-4 Turbo is ahead overall, 47 to 38. The biggest single separator in this matchup is MRCRv2, where the scores are 62 and 45.
GPT-4 Turbo has the edge for knowledge tasks in this comparison, averaging 41.1 versus 35.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for coding in this comparison, averaging 18 versus 17.2. Inside this category, SWE-bench Verified 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 50.4. 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 46.6. Inside this category, MRCRv2 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 33.4. 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 41.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for instruction following in this comparison, averaging 80 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for multilingual tasks in this comparison, averaging 68.5 versus 61.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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