OpenAI: GPT-5.1 Codex Mini

GPT-5.1 Codex Mini sits inside OpenAI's Codex line as a streamlined sibling to the full GPT-5.1 Codex, designed for coding workloads where low latency and predictable cost matter more than pushing the absolute frontier of reasoning quality. According to a third-party listing, the model is positioned as a smaller and faster variant aimed at high-volume agent loops and routine code edits, suggesting an architectural emphasis on inference efficiency rather than maximum parameter count. Its capability profile extends well beyond pure text completion, covering vision, audio input, video input, function and tool calling, reasoning, prompt caching, and web search, which makes it adaptable to mixed-modality developer assistants and retrieval-augmented pipelines rather than narrow code completion alone.

In practical terms, the model fits well into cost-sensitive production agents: the same listing places it at a competitive per-million-token input and output price with cheap cache reads, a context window suited to sizable codebases and multi-file reasoning, and a substantial output ceiling that supports long refactors or generated documentation in a single pass. It is exposed through an OpenAI-compatible API surface, so existing OpenAI SDKs and Azure-hosted deployments can adopt it without changing integration patterns, which lowers the engineering cost of switching from larger Codex variants. Overall, GPT-5.1 Codex Mini reads as a workhorse model for teams that want Codex-family coding behavior with broader multimodal inputs and faster, cheaper inference for everyday development tasks.