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 61. 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 41. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 42.
GPT-5.3-Codex-Spark is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $2.00 input / $6.00 output per 1M tokens for Mistral Large 3. GPT-5.3-Codex-Spark is the reasoning model in the pair, while Mistral Large 3 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 128K for Mistral Large 3.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Mistral Large 3 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.3-Codex-Spark
85.6
Mistral Large 3
52.5
GPT-5.3-Codex-Spark
82.3
Mistral Large 3
41
GPT-5.3-Codex-Spark
88.3
Mistral Large 3
75.5
GPT-5.3-Codex-Spark
92.7
Mistral Large 3
70.6
GPT-5.3-Codex-Spark
78.3
Mistral Large 3
57.1
GPT-5.3-Codex-Spark
92
Mistral Large 3
83
GPT-5.3-Codex-Spark
90.8
Mistral Large 3
78.8
GPT-5.3-Codex-Spark
96.7
Mistral Large 3
77.3
GPT-5.3-Codex-Spark is ahead overall, 87 to 61. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 42.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 57.1. 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 41. 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 77.3. 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 70.6. 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 52.5. 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 75.5. 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 83. 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 78.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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