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 75. 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 62.3. The single biggest benchmark swing on the page is MMMU-Pro, 86 to 64.
GPT-5.3-Codex-Spark gives you the larger context window at 256K, compared with 128K for Qwen3.5 397B (Reasoning).
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Qwen3.5 397B (Reasoning) only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GPT-5.3-Codex-Spark
85.6
Qwen3.5 397B (Reasoning)
74.8
GPT-5.3-Codex-Spark
82.3
Qwen3.5 397B (Reasoning)
62.3
GPT-5.3-Codex-Spark
88.3
Qwen3.5 397B (Reasoning)
70.8
GPT-5.3-Codex-Spark
92.7
Qwen3.5 397B (Reasoning)
84.2
GPT-5.3-Codex-Spark
78.3
Qwen3.5 397B (Reasoning)
70.1
GPT-5.3-Codex-Spark
92
Qwen3.5 397B (Reasoning)
89
GPT-5.3-Codex-Spark
90.8
Qwen3.5 397B (Reasoning)
87.8
GPT-5.3-Codex-Spark
96.7
Qwen3.5 397B (Reasoning)
92.5
GPT-5.3-Codex-Spark is ahead overall, 87 to 75. The biggest single separator in this matchup is MMMU-Pro, where the scores are 86 and 64.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 70.1. 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 62.3. 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 92.5. Inside this category, AIME 2023 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 84.2. Inside this category, LongBench v2 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 74.8. 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 70.8. 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 89. 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 87.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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