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 45. 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 multilingual, where it averages 82.8 against 48. The single biggest benchmark swing on the page is MMLU-ProX, 81 to 48. GPT-5 nano does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-5 nano is also the more expensive model on tokens at $0.05 input / $0.40 output per 1M tokens, versus $0.10 input / $0.30 output per 1M tokens for Step 3.5 Flash. GPT-5 nano is the reasoning model in the pair, while Step 3.5 Flash 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 nano gives you the larger context window at 400K, compared with 256K for Step 3.5 Flash.
Pick Step 3.5 Flash if you want the stronger benchmark profile. GPT-5 nano only becomes the better choice if knowledge is the priority or you need the larger 400K context window.
Step 3.5 Flash
60.2
GPT-5 nano
37.7
Step 3.5 Flash
47.1
GPT-5 nano
22
Step 3.5 Flash
66.7
GPT-5 nano
56.7
Step 3.5 Flash
78.3
GPT-5 nano
58.8
Step 3.5 Flash
60.8
GPT-5 nano
63.7
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Step 3.5 Flash
82.8
GPT-5 nano
48
Step 3.5 Flash
84.5
GPT-5 nano
85.2
Step 3.5 Flash is ahead overall, 66 to 45. The biggest single separator in this matchup is MMLU-ProX, where the scores are 81 and 48.
GPT-5 nano has the edge for knowledge tasks in this comparison, averaging 63.7 versus 60.8. Inside this category, FrontierScience 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 22. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for math in this comparison, averaging 85.2 versus 84.5. Inside this category, AIME 2025 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 58.8. Inside this category, LongBench v2 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 37.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for multimodal and grounded tasks in this comparison, averaging 66.7 versus 56.7. Inside this category, OfficeQA Pro 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 48. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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