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 42. 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 12.9. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 15.
GPT-5.3-Codex-Spark is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Llama 4 Scout. That is roughly Infinityx on output cost alone. GPT-5.3-Codex-Spark is the reasoning model in the pair, while Llama 4 Scout 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. Llama 4 Scout gives you the larger context window at 10M, compared with 256K for GPT-5.3-Codex-Spark.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Llama 4 Scout only becomes the better choice if you want the cheaper token bill or you need the larger 10M context window.
GPT-5.3-Codex-Spark
85.6
Llama 4 Scout
40.6
GPT-5.3-Codex-Spark
82.3
Llama 4 Scout
12.9
GPT-5.3-Codex-Spark
88.3
Llama 4 Scout
57.8
GPT-5.3-Codex-Spark
92.7
Llama 4 Scout
55
GPT-5.3-Codex-Spark
78.3
Llama 4 Scout
35.6
GPT-5.3-Codex-Spark
92
Llama 4 Scout
68
GPT-5.3-Codex-Spark
90.8
Llama 4 Scout
59.8
GPT-5.3-Codex-Spark
96.7
Llama 4 Scout
51
GPT-5.3-Codex-Spark is ahead overall, 87 to 42. 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 35.6. 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 12.9. 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 51. 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 55. 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 40.6. 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 57.8. 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 59.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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