gpt5-router-fast-vs-reasoning

IN premise — entries/2026/06/21/wiki-Generative_pre-trained_transformer.md

Created 2026-06-21T09:50:09+00:00

GPT-5 (August 7, 2025) introduced a router that automatically selects between a fast model and a slower reasoning model based on task complexity

Summary

GPT-5 handles the speed-versus-depth tradeoff on its own, sensing how hard a given problem is and routing it to a quick responder or a slower, more deliberate thinker without the user having to make that choice. This means applications built on top of it inherit that adaptive behavior automatically, removing a layer of manual orchestration that previously had to be built by hand.

Dependents

These beliefs depend on this one:

IN compute-routing-operates-at-multiple-architectural-granularities — Compute routing in LLMs operates at two distinct architectural granularities: intra-model routing (MoE activating expert subnetwork subsets per token, as in Llama 4) and inter-model routing (GPT-5 selecting between fast and reasoning models per task), demonstrating that conditional computation is a general efficiency principle applicable at both the parameter level and the model-selection level.