Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3-Codex-Spark has the cleaner overall profile here, landing at 87 versus 85. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-5.3-Codex-Spark's sharpest advantage is in coding, where it averages 82.3 against 76. The single biggest benchmark swing on the page is HLE, 42 to 26. GPT-5.2-Codex does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GPT-5.2-Codex gives you the larger context window at 400K, compared with 256K for GPT-5.3-Codex-Spark.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. GPT-5.2-Codex only becomes the better choice if agentic is the priority or you need the larger 400K context window.
GPT-5.3-Codex-Spark
85.6
GPT-5.2-Codex
87
GPT-5.3-Codex-Spark
82.3
GPT-5.2-Codex
76
GPT-5.3-Codex-Spark
88.3
GPT-5.2-Codex
87.6
GPT-5.3-Codex-Spark
92.7
GPT-5.2-Codex
92
GPT-5.3-Codex-Spark
78.3
GPT-5.2-Codex
72.5
GPT-5.3-Codex-Spark
92
GPT-5.2-Codex
92
GPT-5.3-Codex-Spark
90.8
GPT-5.2-Codex
88.4
GPT-5.3-Codex-Spark
96.7
GPT-5.2-Codex
95.4
GPT-5.3-Codex-Spark is ahead overall, 87 to 85. The biggest single separator in this matchup is HLE, where the scores are 42 and 26.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 72.5. 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 76. Inside this category, LiveCodeBench 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 95.4. Inside this category, MATH-500 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 92. Inside this category, BBH is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for agentic tasks in this comparison, averaging 87 versus 85.6. Inside this category, BrowseComp 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 87.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark and GPT-5.2-Codex are effectively tied for instruction following here, both landing at 92 on average.
GPT-5.3-Codex-Spark has the edge for multilingual tasks in this comparison, averaging 90.8 versus 88.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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