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 55. 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 33. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 34.
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 14B. That is roughly Infinityx on output cost alone. GPT-5.3-Codex-Spark is the reasoning model in the pair, while Ministral 3 14B 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 14B.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Ministral 3 14B 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 14B
48.4
GPT-5.3-Codex-Spark
82.3
Ministral 3 14B
33
GPT-5.3-Codex-Spark
88.3
Ministral 3 14B
70.5
GPT-5.3-Codex-Spark
92.7
Ministral 3 14B
63.6
GPT-5.3-Codex-Spark
78.3
Ministral 3 14B
50.1
GPT-5.3-Codex-Spark
92
Ministral 3 14B
80
GPT-5.3-Codex-Spark
90.8
Ministral 3 14B
76.8
GPT-5.3-Codex-Spark
96.7
Ministral 3 14B
69.7
GPT-5.3-Codex-Spark is ahead overall, 87 to 55. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 34.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 50.1. 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 33. 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 69.7. 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 63.6. 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 48.4. 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 70.5. 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 80. 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 76.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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