OpenAI releases GPT-5.6 with variable reasoning effort modes

Reasoning models have become a standard feature of modern LLM releases following OpenAI's introduction of o1 nearly two years ago. DeepSeek-R1, released about four months after o1, advanced the field by publishing a detailed recipe for training such models using reinforcement learning with verifiable rewards (RLVR)—a technique that provides reward signals (correct or incorrect) for verifiable domains like mathematics and code. The key insight from DeepSeek-R1's work is that models trained this way learn to generate reasoning traces, backtrack, and self-correct without explicitly training on the intermediate reasoning steps themselves; the final-answer reward signal alone is sufficient.

OpenAI's GPT-5.6 release reflects a maturation of this approach by introducing multiple reasoning-effort levels within the same model family. Earlier dedicated reasoning models like DeepSeek-R1 were monolithic—they always generated verbose outputs with no option to disable reasoning mode. Newer models like Qwen3 demonstrated that a single model can support both reasoning and non-reasoning modes via supervised fine-tuning and reinforcement learning stages, with a toggle flag to switch between them at inference time. GPT-5.6 extends this pattern further by allowing fine-grained control over reasoning intensity, giving users and developers the ability to optimize for their specific latency and cost constraints rather than accepting a one-size-fits-all reasoning depth.