Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3 Codex is clearly ahead on the aggregate, 89 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's sharpest advantage is in coding, where it averages 87.3 against 33. The single biggest benchmark swing on the page is SWE-bench Pro, 90 to 34.
GPT-5.3 Codex is also the more expensive model on tokens at $2.50 input / $10.00 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 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 gives you the larger context window at 400K, compared with 128K for Ministral 3 14B.
Pick GPT-5.3 Codex 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
88.1
Ministral 3 14B
48.4
GPT-5.3 Codex
87.3
Ministral 3 14B
33
GPT-5.3 Codex
91.3
Ministral 3 14B
70.5
GPT-5.3 Codex
93.7
Ministral 3 14B
63.6
GPT-5.3 Codex
80.3
Ministral 3 14B
50.1
GPT-5.3 Codex
93
Ministral 3 14B
80
GPT-5.3 Codex
92.8
Ministral 3 14B
76.8
GPT-5.3 Codex
97.7
Ministral 3 14B
69.7
GPT-5.3 Codex is ahead overall, 89 to 55. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 90 and 34.
GPT-5.3 Codex has the edge for knowledge tasks in this comparison, averaging 80.3 versus 50.1. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for coding in this comparison, averaging 87.3 versus 33. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for math in this comparison, averaging 97.7 versus 69.7. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for reasoning in this comparison, averaging 93.7 versus 63.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 88.1 versus 48.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for multimodal and grounded tasks in this comparison, averaging 91.3 versus 70.5. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for instruction following in this comparison, averaging 93 versus 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for multilingual tasks in this comparison, averaging 92.8 versus 76.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.