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 76. 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 57.5. The single biggest benchmark swing on the page is LiveCodeBench, 80 to 53. Claude Sonnet 4.5 does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
Claude Sonnet 4.5 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $2.00 input / $8.00 output per 1M tokens for GPT-5.3-Codex-Spark. GPT-5.3-Codex-Spark is the reasoning model in the pair, while Claude Sonnet 4.5 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. GPT-5.3-Codex-Spark gives you the larger context window at 256K, compared with 200K for Claude Sonnet 4.5.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Claude Sonnet 4.5 only becomes the better choice if multimodal & grounded is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.3-Codex-Spark
85.6
Claude Sonnet 4.5
70.3
GPT-5.3-Codex-Spark
82.3
Claude Sonnet 4.5
57.5
GPT-5.3-Codex-Spark
88.3
Claude Sonnet 4.5
91.4
GPT-5.3-Codex-Spark
92.7
Claude Sonnet 4.5
86
GPT-5.3-Codex-Spark
78.3
Claude Sonnet 4.5
70.7
GPT-5.3-Codex-Spark
92
Claude Sonnet 4.5
90
GPT-5.3-Codex-Spark
90.8
Claude Sonnet 4.5
88.4
GPT-5.3-Codex-Spark
96.7
Claude Sonnet 4.5
92.4
GPT-5.3-Codex-Spark is ahead overall, 87 to 76. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80 and 53.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 70.7. 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 57.5. 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 92.4. 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 86. 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 70.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Claude Sonnet 4.5 has the edge for multimodal and grounded tasks in this comparison, averaging 91.4 versus 88.3. 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 90. 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 88.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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