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 40. 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 14.1. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 15.
GPT-5.3-Codex-Spark is the reasoning model in the pair, while Llama 4 Behemoth 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 32K for Llama 4 Behemoth.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Llama 4 Behemoth only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.3-Codex-Spark
85.6
Llama 4 Behemoth
34.6
GPT-5.3-Codex-Spark
82.3
Llama 4 Behemoth
14.1
GPT-5.3-Codex-Spark
88.3
Llama 4 Behemoth
55.1
GPT-5.3-Codex-Spark
92.7
Llama 4 Behemoth
47.1
GPT-5.3-Codex-Spark
78.3
Llama 4 Behemoth
36.7
GPT-5.3-Codex-Spark
92
Llama 4 Behemoth
68
GPT-5.3-Codex-Spark
90.8
Llama 4 Behemoth
62.8
GPT-5.3-Codex-Spark
96.7
Llama 4 Behemoth
52.9
GPT-5.3-Codex-Spark is ahead overall, 87 to 40. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 15.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 36.7. Inside this category, MMLU 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 14.1. Inside this category, SWE-bench Pro 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 52.9. 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 47.1. Inside this category, SimpleQA 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 34.6. 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 55.1. Inside this category, OfficeQA 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 68. 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 62.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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