Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.1-Codex-Max is clearly ahead on the aggregate, 84 to 66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.1-Codex-Max's sharpest advantage is in coding, where it averages 75.5 against 47.1. The single biggest benchmark swing on the page is SWE-bench Pro, 84 to 49.
GPT-5.1-Codex-Max is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.10 input / $0.30 output per 1M tokens for Step 3.5 Flash. That is roughly 26.7x on output cost alone. GPT-5.1-Codex-Max 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-5.1-Codex-Max gives you the larger context window at 400K, compared with 256K for Step 3.5 Flash.
Pick GPT-5.1-Codex-Max if you want the stronger benchmark profile. Step 3.5 Flash only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.1-Codex-Max
86
Step 3.5 Flash
60.2
GPT-5.1-Codex-Max
75.5
Step 3.5 Flash
47.1
GPT-5.1-Codex-Max
88.2
Step 3.5 Flash
66.7
GPT-5.1-Codex-Max
92.1
Step 3.5 Flash
78.3
GPT-5.1-Codex-Max
72.6
Step 3.5 Flash
60.8
GPT-5.1-Codex-Max
91
Step 3.5 Flash
87
GPT-5.1-Codex-Max
87.7
Step 3.5 Flash
82.8
GPT-5.1-Codex-Max
94.9
Step 3.5 Flash
84.5
GPT-5.1-Codex-Max is ahead overall, 84 to 66. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 84 and 49.
GPT-5.1-Codex-Max has the edge for knowledge tasks in this comparison, averaging 72.6 versus 60.8. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for coding in this comparison, averaging 75.5 versus 47.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for math in this comparison, averaging 94.9 versus 84.5. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for reasoning in this comparison, averaging 92.1 versus 78.3. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for agentic tasks in this comparison, averaging 86 versus 60.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for multimodal and grounded tasks in this comparison, averaging 88.2 versus 66.7. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for instruction following in this comparison, averaging 91 versus 87. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.1-Codex-Max has the edge for multilingual tasks in this comparison, averaging 87.7 versus 82.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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