GPT-6 Astra

OpenAI general-purpose model for complex reasoning, coding, computer use, research and document creation

Published

GPT-6 Astra is an OpenAI general-purpose model for complex reasoning, coding agents, computer use, research and document creation.

starsCapabilities

visibilityVision understandingcodeFunction callingdata_objectStructured output

paymentsContext and pricing

Context limit1,050,000
Max output128,000
Knowledge cutoff2026-04-30
Input price$10/ 1M tokens
Output price$50/ 1M tokens
Cached input price$1/ 1M tokens

descriptionOverview

Overview

GPT-6 Astra is a general-purpose model in the official OpenAI model catalog, with model ID gpt-6-astra. It should be evaluated for text understanding, generation, coding assistance, structured processing and multimodal product capabilities rather than single-purpose image generation, transcription or TTS.

Best for

Use GPT-6 Astra as a core candidate for assistants, complex Q&A, coding, long-form analysis, workflow automation and vision-enabled applications. Before production, compare context length, pricing, latency, tool support and real output quality.

Official specifications and pricing

Supports text and image input with text output, a 1,050,000-token context window and up to 128,000 output tokens. The knowledge cutoff is 2026-04-30; the official model page does not specify a release date.

reasoning.effort supports low, medium, high, xhigh and max. Streaming, function calling and structured outputs are supported, with tools available through the Responses API.

Standard text pricing per million tokens is $10 input, $1 cached input, $12.50 cache writes and $50 output. Prompts exceeding 272K input tokens incur 2x input and cache rates and 1.5x output rates for the full request. Batch and Flex cost 50% of Standard rates; Fast mode costs 2x the applicable rates. Tool calls may incur additional fees.

Audio, video and fine-tuning are not supported. Image generation is a Responses API tool capability, not direct image output from the model.

lightbulbUse cases

  • Assistants and complex Q&A
  • Code generation and code review
  • Content generation, summarization and rewriting
  • Structured extraction and automation

thumb_upStrengths

  • Useful for general production workloads
  • Covers text understanding and generation
  • Easy to compare with smaller variants
  • Good core model for agent and tool workflows

infoLimitations

  • Capabilities and pricing depend on official OpenAI documentation
  • Higher-quality models usually cost more and add latency
  • Complex workflows still need prompts, tools and evaluation
  • Not a replacement for dedicated image, transcription or TTS models

linkReferences

This content is compiled from official documentation and public sources. Always refer to official documentation for final details