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 68. 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 48.4. The single biggest benchmark swing on the page is SWE-bench Verified, 80 to 41. o1 does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $2.00 input / $8.00 output per 1M tokens for GPT-5.3-Codex-Spark. That is roughly 7.5x on output cost alone. GPT-5.3-Codex-Spark gives you the larger context window at 256K, compared with 200K for o1.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. o1 only becomes the better choice if instruction following is the priority.
GPT-5.3-Codex-Spark
85.6
o1
65.4
GPT-5.3-Codex-Spark
82.3
o1
48.4
GPT-5.3-Codex-Spark
88.3
o1
70.7
GPT-5.3-Codex-Spark
92.7
o1
78.1
GPT-5.3-Codex-Spark
78.3
o1
69.6
GPT-5.3-Codex-Spark
92
o1
92.2
GPT-5.3-Codex-Spark
90.8
o1
77
GPT-5.3-Codex-Spark
96.7
o1
74.3
GPT-5.3-Codex-Spark is ahead overall, 87 to 68. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 80 and 41.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 69.6. Inside this category, FrontierScience 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 48.4. Inside this category, SWE-bench Verified 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 74.3. Inside this category, AIME 2024 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 78.1. 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 65.4. Inside this category, Terminal-Bench 2.0 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 70.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 92. 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 77. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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