GPT-5.1 codex mini
Smaller, more cost-effective, less-capable version of GPT-5.1-Codex. 400K context, 128K max output.
About GPT-5.1 codex mini
Built specifically for the volume and variety of real development work, GPT-5.1 Codex Mini earns its place through a combination of coding-tuned reasoning and aggressive pricing — at $0.25 per million input tokens, it delivers roughly four times the usage of its full-size sibling while still scoring 83.6% on LiveCodeBench and 91.7% on AIME 2025. Developers consistently praise its code "taste" and architectural thinking, and its 400,000-token context window makes it genuinely useful for multi-file refactoring, dependency updates, and repository-wide changes without hitting truncation walls. That said, it is notably verbose — it consumed 75 million output tokens in benchmarking where the median was 29 million — which can quietly inflate bills despite low input costs. If you are running repetitive coding pipelines, powering an IDE assistant, or building developer productivity tools where throughput matters more than maximum capability, this is a strong, cost-conscious choice.
Best for
- High-volume routine coding tasks: bug fixes, test generation, and straightforward feature implementation where cost efficiency is the priority
- Repository-wide changes: multi-file refactoring, dependency updates, and coordinated modifications across large codebases using its 400k-token context window
- IDE integrations and coding assistants where fast throughput and affordability matter more than top-of-range capability
- Cost-sensitive automation pipelines running repetitive coding or review tasks at scale
- Educational projects and prototyping where high output volume needs to stay within budget
Specs & capabilities
How GPT-5.1 codex mini stacks up — intelligence, speed, context, and modalities.
Intelligence
Medium
Speed
Fast
Context window
400,000 tokens
Max output
128,000 tokens
Knowledge cutoff
September 30, 2024
Frequently asked questions
How does pricing compare to the full GPT-5.1 Codex?
Input costs $0.25 per million tokens versus the full model, giving roughly 4x more usage within the same budget. Output is $2.00 per million tokens, which is above the cross-model average of $0.87, so verbosity can eat into those savings.
What is the context window?
400,000 tokens input, with a maximum of 128,000 tokens of output per response.
What is it genuinely good at?
Coding-specific tasks: multi-file refactoring, bug fixes, test generation, and repository-wide changes. Users also praise its code design sense and architectural thinking relative to general-purpose models at similar price points.
What are the honest limitations?
The model is verbose by nature, which inflates output costs. Latency is also higher than you might expect from a 'mini' model — developers have reported it feeling slow on the API, and time-to-first-token sits at the upper end for its price tier. It can also struggle with fundamentally rethinking broken architectures, tending instead to patch problems locally.
Who should choose Codex Mini over a general-purpose model like GPT-5.1?
Developers focused primarily on coding workflows who need high throughput at lower cost. For open-ended writing, analysis, or tasks requiring broad general knowledge, a general-purpose model will usually serve better.
What modalities and features does it support?
Text and image inputs, text-only output. Supports streaming, function calling, structured outputs, and tool use. Does not support fine-tuning, audio, or video processing. Knowledge cutoff is September 30, 2024.