Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3-Codex-Spark is clearly ahead on the aggregate, 87 to 66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3-Codex-Spark's sharpest advantage is in coding, where it averages 82.3 against 47.1. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 49.
GPT-5.3-Codex-Spark 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-5.3-Codex-Spark 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.
Pick GPT-5.3-Codex-Spark 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-5.3-Codex-Spark
85.6
Step 3.5 Flash
60.2
GPT-5.3-Codex-Spark
82.3
Step 3.5 Flash
47.1
GPT-5.3-Codex-Spark
88.3
Step 3.5 Flash
66.7
GPT-5.3-Codex-Spark
92.7
Step 3.5 Flash
78.3
GPT-5.3-Codex-Spark
78.3
Step 3.5 Flash
60.8
GPT-5.3-Codex-Spark
92
Step 3.5 Flash
87
GPT-5.3-Codex-Spark
90.8
Step 3.5 Flash
82.8
GPT-5.3-Codex-Spark
96.7
Step 3.5 Flash
84.5
GPT-5.3-Codex-Spark is ahead overall, 87 to 66. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 49.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 60.8. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for coding in this comparison, averaging 82.3 versus 47.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for math in this comparison, averaging 96.7 versus 84.5. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for reasoning in this comparison, averaging 92.7 versus 78.3. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for agentic tasks in this comparison, averaging 85.6 versus 60.2. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for multimodal and grounded tasks in this comparison, averaging 88.3 versus 66.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for instruction following in this comparison, averaging 92 versus 87. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for multilingual tasks in this comparison, averaging 90.8 versus 82.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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