GPT-6 Astra
⧉GPT-6 Astra is the next-generation flagship OpenAI released in September 2026: a 1,050,000-token context window, up to 128,000 output tokens, reasoning_effort up to xhigh, image input, tool calling and prompt caching. Fully OpenAI-SDK compatible - set model to gpt-6-astra - and served on both /chat/completions and /responses. Pay-as-you-go, failed calls never billed; cached input bills at one tenth, and we do not add the long-context surcharge the official API applies.
Live Test · Playground
Try out GPT-6 Astra right here (available after login)。
Input
Advanced
This model is served over a ChatGPT-subscription upstream that does not accept temperature / top_p / max_tokens (they are silently ignored). Use reasoning_effort below for thinking depth, and prompt wording for length.
Conversation
About GPT-6 Astra
GPT-6 Astra is the next-generation flagship OpenAI released in September 2026, served on NezhaGate through the OpenAI-compatible API: a 1,050,000-token context window, up to 128,000 output tokens, reasoning_effort for thinking depth (up to xhigh), image input, tool calling and prompt caching, on both /chat/completions and /responses. Set model to gpt-6-astra and keep your SDK and request shape. Pay-as-you-go, failed calls never billed, and cached input settles at one tenth of the input rate.
Use cases
1,050,000 tokens holds a whole repository, a contract set or a long report in one call, with no chunking and stitching of your own.
It speaks the OpenAI-compatible endpoint, so Codex, Cursor and Claude Code switch to GPT-6 Astra by changing the model field alone.
reasoning_effort goes up to xhigh, so you can spend the thinking budget on the hardest part and keep simple requests on low.
Image input with text output: screenshot understanding, chart questions and document parsing all use the same endpoint.
How to choose
Pick GPT-6 Astra when you need the strongest reasoning or a very long context; gpt-5.6-terra is cheaper for everyday development and gpt-5.6-luna for high-volume low-latency work. All three use the OpenAI-compatible endpoint, so switching is a one-field change.
FAQ
What are the context and output limits?
How do I get cache hits?
How does the price compare with the official API?
Is reasoning_effort supported?
Can I use /v1/responses?
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