技术规格

GPT-5.1 Codex Mini

GPT-5.1 Codex Mini is a specialized, lightweight large language model engineered to facilitate rapid software development and streamlined coding workflows. As a high-efficiency variant within the GPT-5.1 series, it is optimized for low-latency performance in environments requiring immediate feedback, such as real-time code completion, inline refactoring, and interactive debugging within integrated development environments (IDEs). The model is designed to handle routine programming tasks with a focus on high throughput and reduced computational overhead, making it a cost-effective alternative for developers who require consistent assistance without the resource requirements of larger reasoning models.

Technically, the model employs a dense transformer architecture utilizing Multi-Head Attention (MHA) and absolute position embeddings. This design choice ensures predictable and deterministic outputs critical for syntax-heavy tasks where structural accuracy is paramount. It supports a substantial context window of 400,000 tokens, enabling it to ingest large portions of a codebase or extensive documentation for more contextualized generation. The model's training focuses on code-specific datasets, including a vast corpus of multi-language repositories and software documentation, which allows it to maintain precision in logic and syntax across common programming languages like Python, JavaScript, and C++.

Functionally, GPT-5.1 Codex Mini operates as a workhorse for developer-centric applications, supporting advanced features such as function calling, structured outputs, and vision-integrated UI development. It is capable of processing multimodal inputs, specifically interpreting screenshots or design mockups to generate corresponding frontend code or assist in visual debugging. By balancing raw generation speed with reliable instruction following, the model serves as a core component for agentic coding tools and CI/CD pipelines where automated code review and unit test generation are performed at scale.