Introducing GPT-5.4 mini and nano

Today we’re releasing GPT‑5.4 mini and nano, our most capable small models yet. They bring many of the strengths of GPT‑5.4 to faster, more efficient models designed for high-volume workloads.

GPT‑5.4 mini significantly improves over GPT‑5 mini across coding, reasoning, multimodal understanding, and tool use, while running more than 2x faster. It also approaches the performance of the larger GPT‑5.4 model on several evaluations, including SWE-Bench Pro and OSWorld-Verified.

GPT‑5.4 nano is the smallest, cheapest version of GPT‑5.4 for tasks where speed and cost matter most. It is also a significant upgrade over GPT‑5 nano. We recommend it for classification, data extraction, ranking, and coding subagents that handle simpler supporting tasks.

We estimate latency by looking at the production behavior of our models, and simulating this offline. The latency estimate accounts for tool call duration (code execution time), sampled tokens, and input tokens. Real-world latency may vary substantially, and depends on many factors not captured in our simulation. Similarly, costs are estimated based on API pricing of these models at the time of writing. Costs may change in the future. Reasoning efforts were swept from low to xhigh.

This pattern becomes more useful as smaller models get faster and more capable. Instead of using one model for everything, developers can compose systems where larger models decide what to do and smaller models execute quickly at scale. GPT‑5.4 mini is our strongest mini model yet for that style of workflow.

GPT‑5.4 mini is available today in the API, Codex, and ChatGPT.

These models are built for the kinds of workloads where latency directly shapes the product experience: coding assistants that need to feel responsive, subagents that quickly complete supporting tasks, computer-using systems that capture and interpret screenshots, and multimodal applications that can reason over images in real-time. In these settings, the best model is often not the largest one—it’s the one that can respond quickly, use tools reliably, and still perform well on complex professional tasks.

Here’s what our customers think after testing GPT‑5.4 mini and nano in their workflows:

GPT‑5.4 mini and nano are especially effective in coding workflows that benefit from fast iteration. The models handle targeted edits, codebase navigation, front-end generation, and debugging loops with low latency, making them a strong fit for coding tasks that need to be completed at faster speeds and lower costs.