How GPT-5.6 fuses frontier intelligence with frontier efficiency

We designed the GPT‑5.6 model family to balance capability and cost across the spectrum of tasks people use our models for. Our flagship model, GPT‑5.6 Sol, with max reasoning outperforms Claude Fable 5 on the Artificial Analysis Coding Agent Index at less than half of the cost. Terra performs as well as GPT‑5.5 on intelligence benchmarks at half the price, and Luna is our fastest and most affordable model, priced 80% less than the cost of Sol. To deliver these efficiencies, our research and technical teams have made significant optimizations at every major layer of our stack. These improvements span our models, inference (how we run models to generate output), and our agentic harness, which is used by both Codex and ChatGPT Work.

As we’ve scaled our models to 1 billion active users and more than 2 million businesses over the past four years, efficiency has been central to distributing the benefits of intelligence to everyone. Our mission is to ensure that artificial general intelligence benefits all of humanity. Over these years, we’ve worked to continuously unlock greater optimizations across our stack in order to offer the most performant models at every point in the cost-intelligence curve. We achieved our greatest intelligence-per-token efficiency yet through GPT‑5.6, which is trained to achieve more work per token. In training, we optimize for both task success and efficiency, shaping the model to take a more direct path through a task.

Accelerating inference with GPT‑5.6 Sol

In a compute-constrained world where model demand is growing faster than capacity, efficiency is core to every system design. That’s especially true in our inference stack, which runs trained models to generate responses. Our primary objective is to serve more tokens with the same hardware, while preserving the intelligence, latency, availability, and reliability users expect.