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 75.1. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 74. Claude Opus 4.6 does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
Claude Opus 4.6 is also the more expensive model on tokens at $15.00 input / $75.00 output per 1M tokens, versus $2.00 input / $8.00 output per 1M tokens for GPT-5.3-Codex-Spark. That is roughly 9.4x on output cost alone. GPT-5.3-Codex-Spark is the reasoning model in the pair, while Claude Opus 4.6 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. Claude Opus 4.6 gives you the larger context window at 1M, compared with 256K for GPT-5.3-Codex-Spark.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Claude Opus 4.6 only becomes the better choice if multimodal & grounded is the priority or you need the larger 1M context window.
GPT-5.3-Codex-Spark
85.6
Claude Opus 4.6
79.2
GPT-5.3-Codex-Spark
82.3
Claude Opus 4.6
75.1
GPT-5.3-Codex-Spark
88.3
Claude Opus 4.6
94.6
GPT-5.3-Codex-Spark
92.7
Claude Opus 4.6
93.1
GPT-5.3-Codex-Spark
78.3
Claude Opus 4.6
78.9
GPT-5.3-Codex-Spark
92
Claude Opus 4.6
95
GPT-5.3-Codex-Spark
90.8
Claude Opus 4.6
94.7
GPT-5.3-Codex-Spark
96.7
Claude Opus 4.6
97.2
GPT-5.3-Codex-Spark is ahead overall, 87 to 85. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 74.
Claude Opus 4.6 has the edge for knowledge tasks in this comparison, averaging 78.9 versus 78.3. Inside this category, MMLU-Pro 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 75.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Claude Opus 4.6 has the edge for math in this comparison, averaging 97.2 versus 96.7. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Claude Opus 4.6 has the edge for reasoning in this comparison, averaging 93.1 versus 92.7. Inside this category, BBH 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 79.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Claude Opus 4.6 has the edge for multimodal and grounded tasks in this comparison, averaging 94.6 versus 88.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Claude Opus 4.6 has the edge for instruction following in this comparison, averaging 95 versus 92. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Claude Opus 4.6 has the edge for multilingual tasks in this comparison, averaging 94.7 versus 90.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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