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OpenAI

GPT-6 Astra

GPT-6 Astra is a chat model from OpenAI tracked in our catalog; provider availability and pricing are compared in the table on this page.

Input from
$2.50 / 1M tokens
across 8 providers

API Pricing

Cheapest on HiAPI — 71% below avg
ProviderInput / 1MOutput / 1MCached / 1M
$2.50$12.50$0.250
$5.00$25.00$0.500
$8.00$40.00$0.800
$10.00$50.00$1.00
$10.00$50.00$1.00
$10.00$50.00-
$10.00$50.00$1.00
$10.00$50.00$1.00
$12.00$60.00$1.20

Prices updated daily. Last check: Oct 20, 2026

Compare API pricing for every OpenAI model →

GPT-6 Astra pricing by provider

Input, output, and batch rates, plus alternatives, for one provider at a time.

Performance & Benchmarks

Source: Artificial Analysis →
Intelligence
52.7 / 100
Coding
76.9 / 100
Output Speed
68.5 t/s
Latency (TTFT)
238.9s

Reasoning & Knowledge

  • GPQA Diamond96.1%
  • Humanity's Last Exam54.7%

Coding

  • SciCode56.5%

Agentic & Tool Use

  • Terminal-Bench v2.188.4%
  • τ-bench Banking41.4%

Instruction & Long Context

  • Long-Context Reasoning80.7%

Benchmarks measured Oct 2026. Scores are independent evaluations, not vendor-reported.

Model Details

General

Creator
OpenAI
Modalities
Text

Capabilities

Open Source
No

Strengths & Limitations

Strengths

  • Chat-completion style interface, so it slots into existing OpenAI-compatible client libraries and prompt tooling
  • Attributed to OpenAI, whose chat APIs are widely supported by SDKs, agent frameworks, and observability tools
  • Listed in our catalog with provider pricing tracked side by side, making cost comparison against other chat models straightforward
  • Text generation focus suits conversational assistants, drafting, and summarization workloads without special infrastructure
  • Can be swapped into an existing OpenAI-format pipeline for A/B evaluation with minimal code change

Limitations

  • We do not have a confirmed context window length for this model in our database
  • No verified benchmark scores are available for GPT-6 Astra — the throughput and time-to-first-token values in our records are unpopulated placeholders, not measurements
  • Supported input modalities are not confirmed in our data, so vision or audio handling should be verified with the provider
  • Tool calling, structured output, and other API feature support are not tracked for this entry
  • Provider availability may be narrow, which limits fallback options and regional routing

Key Features

•Chat/instruction-following text generation
•OpenAI-compatible chat-completions request format
•Provider pricing tracked in the comparison table on this page
•Suitable for multi-turn dialogue with system and user roles
•Drop-in evaluation against other OpenAI chat models in the same pipeline
•Text output for drafting, summarization, and Q&A workloads

About GPT-6 Astra

GPT-6 Astra is a chat-oriented large language model attributed to OpenAI in our catalog. It is listed as a conversational text model, meaning it is designed to be prompted with instructions or dialogue turns and to return generated text responses through a chat-completions style API. Our database currently holds limited verified specifications for GPT-6 Astra. We do not have a confirmed context window length, modality list, tool-calling specification, or benchmark scores for it — the throughput and latency figures associated with this entry are unpopulated rather than measured, so they should not be read as performance claims. Anyone evaluating GPT-6 Astra for a production workload should confirm context limits, supported input types, and rate limits directly with the provider serving it. In practice, a model in this position is evaluated the same way as any other OpenAI chat endpoint: by running your own prompts against it and comparing response quality, latency, and cost per request to the other chat models you already use. The pricing table on this page tracks which providers list GPT-6 Astra and how their rates compare, which is the most reliable signal we can offer until fuller specifications are confirmed.

Common Use Cases

GPT-6 Astra is catalogued as a general chat model, which makes it a candidate for the standard conversational workloads: customer-facing assistants, internal question answering over supplied context, drafting and rewriting text, summarization, and as the response layer in a retrieval-augmented pipeline. Because we do not have confirmed context window or modality data for it, it is best treated as a candidate to benchmark rather than a known quantity — run it against a representative slice of your own prompts and compare output quality, latency, and cost to the chat models you already have in production before committing to it for long-document processing, agentic tool use, or high-volume batch jobs where unverified limits could cause failures.

Frequently Asked Questions

How much does GPT-6 Astra cost?

Pricing varies by provider and by pricing type — input tokens, output tokens, and any cached or batch rates are billed separately and change frequently. Check the pricing table on this page for the current rates from each provider that lists GPT-6 Astra.

What is GPT-6 Astra best used for?

It is catalogued as a chat model, so it fits conversational assistants, drafting and editing text, summarization, and question answering over supplied context. Since we do not have confirmed context or modality specs for it, validate it on your own prompts before using it for long-document or agentic workloads.

What context window does GPT-6 Astra support?

We do not currently have a confirmed context window length for GPT-6 Astra in our database. Check the documentation of the provider you plan to use, since serving providers sometimes cap context below a model's maximum.

Does GPT-6 Astra accept image input?

Supported input modalities are not tracked for this entry in our database. If multimodal input matters for your application, confirm it with the provider's API documentation rather than assuming either way.

How fast is GPT-6 Astra?

The throughput and time-to-first-token fields for this entry are unpopulated in our records, so we have no measured speed figures to report. Latency also depends heavily on which provider serves the model, the region, and your prompt length, so measuring against your own workload is the most reliable approach.