GPT-6 Astra, the OpenAI model in numbers

What you need to know

OpenAI released GPT-6 Astra on September 4, 2026. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money. Its weights are not published, so it only runs remotely, through the OpenAI API or partner hosts (38 listed on models.dev). It is a very recent release.

We file it under frontier models (24 models in this category on the site).

What it costs

GPT-6 Astra costs $10 per million input tokens and $50 per million output tokens (developer's price, checked on October 22, 2026).

In practice, an agent task that reads 20,000 tokens and writes 2,000 costs about $0.30, or $300 for 1,000 such tasks.

That is 3.4 times the median output price of frontier models ($14.5).

Tokens read back from cache cost $1 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

Benchmark results

According to results published by Epoch AI (checked on October 22, 2026), GPT-6 Astra has a capability index of 94.7 out of 100, computed on 5 benchmarks from our basket.

That puts it at #4 in our weekly model ranking.

• GPQA Diamond (PhD-level science questions): 95.8% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 100% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 93.7% — best score among tracked models
• FrontierCode (advanced programming tasks): 53.3% — the best tracked model, Claude Opus 5.5, reaches 54.6%

What it can do

Its context window reaches 1,050,000 tokens, roughly 1,575 pages of text at 500 words per page: enough to load a whole book or a mid-sized codebase. A single answer can run to 128,000 tokens.

It accepts as input: text, image and PDF. Its knowledge stops at April 30, 2026: beyond that, it needs web search or your documents.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

When to choose it

Given its category, GPT-6 Astra is mainly aimed at hard tasks where quality matters more than cost: deep reasoning, complex code, long analyses.

Its FrontierCode result (53.3%, above the median of tracked models) makes it a serious candidate for coding agents.

Its very large context window makes it worth considering for analysing large sets of documents in a single request.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Alternatives

In the same category, the models closest to GPT-6 Astra are:

• Gemini 4 Argon (Google DeepMind, index 95.3) — see the comparison
• Claude Sonnet 5.5 (Anthropic, index 95.9) — see the comparison
• Claude Opus 5.5 (Anthropic, index 95.9) — see the comparison
• GPT-5.5 Pro (OpenAI, index 96.9) — see the comparison