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 43. 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 15.7. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 17.
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 Maverick. That is roughly Infinityx on output cost alone. GPT-5.3-Codex-Spark is the reasoning model in the pair, while Llama 4 Maverick 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 Maverick 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. Llama 4 Maverick only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
GPT-5.3-Codex-Spark
85.6
Llama 4 Maverick
40.9
GPT-5.3-Codex-Spark
82.3
Llama 4 Maverick
15.7
GPT-5.3-Codex-Spark
88.3
Llama 4 Maverick
56.8
GPT-5.3-Codex-Spark
92.7
Llama 4 Maverick
54
GPT-5.3-Codex-Spark
78.3
Llama 4 Maverick
36.5
GPT-5.3-Codex-Spark
92
Llama 4 Maverick
68
GPT-5.3-Codex-Spark
90.8
Llama 4 Maverick
59.8
GPT-5.3-Codex-Spark
96.7
Llama 4 Maverick
51.3
GPT-5.3-Codex-Spark is ahead overall, 87 to 43. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 17.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 36.5. 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 15.7. 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 51.3. 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 54. 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 40.9. 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 56.8. 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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