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codex-mini-latest
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codex-mini-latest is a closed-weights language model from OpenAI, released May 2025. Licence: proprietary.
Catalogue identity notes
Comparison-only: retired or no longer listed API model, kept so its independent runs can be scored. OpenAI API model codex-mini-latest. OpenAI API changelog (May 15, 2025): 'Launched codex-mini-latest in the API, optimized for use with the Codex CLI'; OpenAI deprecations page (2025-11-17 notice): removal from the API on February 12, 2026. OpenAI documents no second model behind the id during its life, so the single served checkpoint is pinned by name. Distinct from gpt-5-codex / gpt-5.1-codex-mini.
$ how it compares
The LLMs ranked either side of codex-mini-latest, out of 1132 with a score, then more from OpenAI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 281 |  DeepSeek V3 0324 | 60±1 | local | ||
| 282 |  Qwen3 30B-A3B Instruct 2507 | 60±1 | api $0.15/M +local | ||
| 283 | codex-mini-latest | 60±4 | comparison only | ||
| 284 | Qwen3-VL 32B Instruct | 60±1 | api $0.28/M +local | ||
| 285 |  Cogito v1 Preview Qwen 14B | 60±7 | local | ||
| more from OpenAI | |||||
| 1 | GPT-6 Astra | 87±1 | api $20.00/M | ||
| 3 | GPT-6.1 Sol | 85±1 | api $4.00/M | ||
| 7 | GPT-6 Sol | 83±1 | api $4.00/M | ||
$ what it's good at
coding▁▁▁▃▅▇█▇▅▄▂▁ #195 of 549 * · 91% of its score · 1 test
Beats Gemini 2.5 Pro, Qwen3-Coder-Next (80B-A3B), Qwen3-VL 235B-A22B Instruct here, though they rank higher overall. Trails QwQ-32B-Preview, Ministral 3 8B Reasoning 2512, Phi-4-reasoning 14B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| WebApp1K-Duo-React pass@1 (onekq-ai) | 57.5% ✎ | ▁··▁·▁▁▁▆█▇█ beats 9/45 | 91% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · WebApp1K models leaderboard (… 91%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 WebApp1K models leaderboard (onekq-ai) | 1 | 91% |
| prior · pull toward the middle | 9% | |
| weighted average | = 60.2 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json