Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Step 3.5 Flash is clearly ahead on the aggregate, 66 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Step 3.5 Flash's sharpest advantage is in coding, where it averages 47.1 against 32.2. The single biggest benchmark swing on the page is SWE-bench Verified, 48 to 20. GPT-4o does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
GPT-4o is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.10 input / $0.30 output per 1M tokens for Step 3.5 Flash. That is roughly 33.3x on output cost alone. Step 3.5 Flash gives you the larger context window at 256K, compared with 128K for GPT-4o.
Pick Step 3.5 Flash if you want the stronger benchmark profile. GPT-4o only becomes the better choice if multimodal & grounded is the priority.
Step 3.5 Flash
60.2
GPT-4o
51.2
Step 3.5 Flash
47.1
GPT-4o
32.2
Step 3.5 Flash
66.7
GPT-4o
72.2
Step 3.5 Flash
78.3
GPT-4o
64.6
Step 3.5 Flash
60.8
GPT-4o
47.4
Step 3.5 Flash
87
GPT-4o
82
Step 3.5 Flash
82.8
GPT-4o
75.5
Step 3.5 Flash
84.5
GPT-4o
71.8
Step 3.5 Flash is ahead overall, 66 to 56. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 48 and 20.
Step 3.5 Flash has the edge for knowledge tasks in this comparison, averaging 60.8 versus 47.4. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for coding in this comparison, averaging 47.1 versus 32.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for math in this comparison, averaging 84.5 versus 71.8. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for reasoning in this comparison, averaging 78.3 versus 64.6. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for agentic tasks in this comparison, averaging 60.2 versus 51.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-4o has the edge for multimodal and grounded tasks in this comparison, averaging 72.2 versus 66.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for instruction following in this comparison, averaging 87 versus 82. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for multilingual tasks in this comparison, averaging 82.8 versus 75.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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