---
type: "GlossaryTerm"
title: "Non-Autoregressive Model (System One Class)"
description: "A non-autoregressive model does not generate its output token by token in a chain; instead, it decides all positions simultaneously in a single parallel pass. R"
resource: "https://www.contextstudios.ai/glossary/non-autoregressive-model"
language: "en"
tags: ["tech"]
generated:
  by: "process:contextstudios-md/1"
  at: "2026-10-08T20:46:19.246Z"
status: "stable"
---

# Non-Autoregressive Model (System One Class)

A non-autoregressive model does not generate its output token by token in a chain; instead, it decides all positions simultaneously in a single parallel pass. Rather than conditioning each token on the previous one, it fills the entire response field in one or a few rounds — it does not write, it decides. The class is named System One in analogy to Kahneman's fast, intuitive thinking: direct mapping instead of stepwise derivation. The distinction from the autoregressive class (GPT, Claude, Gemini in standard mode) shows in three points. First, latency: non-autoregressive models reach 70–500 ms per response because the number of decoding steps no longer grows with output length. Second, cost: a single forward pass per decision replaces many sequential steps, which clearly raises throughput in batch inference. Third, failure mode: because positions arise independently next to each other, long strongly interdependent texts suffer from repetitions and jumps, while short structured outputs such as classifications or JSON fields are handled reliably. Practical examples include Apple embeLLM, which reconstructs all tokens in parallel through an embedding bottleneck, and full-sequence diffusion models, which Google integrated as a fast mode in Gemini. For agent pipelines with strict structured-output contracts, they are the natural choice for preprocessing, routing, and fast classification.