GPT-6 Astra

What Is GPT-6 Astra?

GPT-6 Astra is OpenAI's flagship model, released in September 2026 for the hardest end-to-end work across complex reasoning, coding, computer use, research and document creation. OpenAI describes it as its most intelligent and best-aligned model, and its documentation lists a 1,050,000-token context window.

What It's Good For

Astra suits advanced analysis, software engineering, deep research, scientific work and document creation, with particular strength in long-horizon agentic tasks that involve computer and browser use. OpenAI reports these results:

• Mathematics: 97.6% on FrontierMath Tier 4 (v2), against 87.8% for Claude Fable 5.1 and 90.2% for Claude Fable 5.
• Science: 64.6% on Terminal-Bench Science 0.1, against 52.6% for Fable 5.1 and 30.0% for Claude Opus 5.
• Computer and browser use: 72.6% on OSWorld 2.0, in roughly 47% less time per task than GPT-5.6 Sol.
• Hallucination: on OpenAI's internal benchmark Astra scores 4.2% against 12.2% for GPT-5.6 Sol, where lower is better.

For developers building autonomous agents, native computer use, browser automation and tool calling make Astra a strong candidate. It is also offered through AWS Bedrock and Microsoft Foundry (Azure AI Foundry) for enterprise deployments. Teams comparing it with other frontier models may also look at Claude Opus 5.

Who It's a Good Fit For

The practical question for most teams is whether the capability jump justifies the cost. OpenAI prices Astra at a premium, with higher rates for very long prompts. Astra is the right fit for:

• Research teams running multi-step scientific or mathematical workflows that need top benchmark performance.
• Enterprise software teams building agentic pipelines with long context, computer use or browser automation requirements.
• Data-intensive document work where the long context window allows large codebases, legal files or scientific corpora to be processed in a single pass.

Limitations & Where It Falls Short

On Humanity's Last Exam (with tools), OpenAI's own table has Claude Fable 5.1 ahead of Astra, 65.0% to 57.2%, one of the few academic rows where an Anthropic model leads. On FrontierCode 1.1, scores across the leading models cluster within a few points of each other, a small gap for a model OpenAI presents as a new generation.

Cost is the most significant practical constraint. Artificial Analysis's independent cost measurements put Astra above GPT-5.6 Sol per task, even though Astra uses fewer output tokens. For teams whose workloads do not need frontier-level math or agentic computer use, a lower-cost model remains the more economical choice.

OpenAI also says Astra's cyber capabilities reach the Critical threshold in its Preparedness Framework. As a result Astra refuses advanced exploit-building tasks, and its safeguards can pause or stop legitimate work; in the API a flagged task is stopped.