Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Sibling matchup inside the GPT-5.3 Codex family.
GPT-5.3 Codex and GPT-5.3-Codex-Spark sit in the same GPT-5.3 Codex family. This page is less about two unrelated model lineages and more about how the siblings trade off on benchmark shape, token costs, and practical limits like context window.
GPT-5.3 Codex has the cleaner overall profile here, landing at 89 versus 87. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-5.3 Codex's sharpest advantage is in coding, where it averages 87.3 against 82.3. The single biggest benchmark swing on the page is BrowseComp, 88 to 82.
GPT-5.3 Codex is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $2.00 input / $8.00 output per 1M tokens for GPT-5.3-Codex-Spark. GPT-5.3 Codex gives you the larger context window at 400K, compared with 256K for GPT-5.3-Codex-Spark.
GPT-5.3 Codex makes more sense if coding is the priority or you need the larger 400K context window, while GPT-5.3-Codex-Spark is the cleaner fit if you want the cheaper token bill.
GPT-5.3 Codex
88.1
GPT-5.3-Codex-Spark
85.6
GPT-5.3 Codex
87.3
GPT-5.3-Codex-Spark
82.3
GPT-5.3 Codex
91.3
GPT-5.3-Codex-Spark
88.3
GPT-5.3 Codex
93.7
GPT-5.3-Codex-Spark
92.7
GPT-5.3 Codex
80.3
GPT-5.3-Codex-Spark
78.3
GPT-5.3 Codex
93
GPT-5.3-Codex-Spark
92
GPT-5.3 Codex
92.8
GPT-5.3-Codex-Spark
90.8
GPT-5.3 Codex
97.7
GPT-5.3-Codex-Spark
96.7
GPT-5.3 Codex and GPT-5.3-Codex-Spark are sibling variants in the GPT-5.3 Codex family, so the right pick depends on whether you value the better benchmark line, cheaper tokens, or the larger context window. GPT-5.3 Codex is ahead overall 89 to 87.
GPT-5.3 Codex has the edge for knowledge tasks in this comparison, averaging 80.3 versus 78.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for coding in this comparison, averaging 87.3 versus 82.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for math in this comparison, averaging 97.7 versus 96.7. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for reasoning in this comparison, averaging 93.7 versus 92.7. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 88.1 versus 85.6. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for multimodal and grounded tasks in this comparison, averaging 91.3 versus 88.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for instruction following in this comparison, averaging 93 versus 92. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for multilingual tasks in this comparison, averaging 92.8 versus 90.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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