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 27. 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 6.2. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 5.
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 Ministral 3 3B. That is roughly Infinityx on output cost alone. GPT-5.3-Codex-Spark is the reasoning model in the pair, while Ministral 3 3B 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 Ministral 3 3B.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Ministral 3 3B 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
Ministral 3 3B
22.9
GPT-5.3-Codex-Spark
82.3
Ministral 3 3B
6.2
GPT-5.3-Codex-Spark
88.3
Ministral 3 3B
30.4
GPT-5.3-Codex-Spark
92.7
Ministral 3 3B
30.1
GPT-5.3-Codex-Spark
78.3
Ministral 3 3B
24.5
GPT-5.3-Codex-Spark
92
Ministral 3 3B
67
GPT-5.3-Codex-Spark
90.8
Ministral 3 3B
59.7
GPT-5.3-Codex-Spark
96.7
Ministral 3 3B
36
GPT-5.3-Codex-Spark is ahead overall, 87 to 27. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 5.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 24.5. 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 6.2. 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 36. 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 30.1. 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 22.9. 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 30.4. 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 67. 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 59.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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