GPT-6 Astra: Benchmarks, vs Fable 5.1 & How to Use It

GPT-6 Astra is OpenAI's flagship model, released on September 3, 2026. OpenAI calls it "the most intelligent and aligned model in the world," and it is the top tier above GPT-6 Sol and GPT-6 Luna.

It is built for "the hardest end-to-end work": computer use, long coding sessions, research, and document work. It lists at $10 per million input tokens and $50 per million output tokens.

Astra wins most of OpenAI's own charts, but not all of them. Claude Fable 5.1 costs the same and still leads on some knowledge benchmarks. This page covers where the lead is real, and how to call the model.

GPT-6 Astra at a Glance

GPT-6 Astra Benchmarks

Scores are from OpenAI's launch post, at maximum reasoning effort.

The biggest jumps are in computer use, terminal work, and long-context retrieval. Plain coding moved less: DeepSWE went up 1.4 points.

OpenAI also reports speed. On OSWorld 2.0, Astra averaged about 40 minutes per task against 75 minutes for GPT-5.6 Sol.

GPT-6 Astra vs Claude Fable 5.1

Both models list at $10 input and $50 output per million tokens. That makes this the comparison most teams actually face.

Astra leads on agentic and coding work. Fable 5.1 leads on broad knowledge and the composite index.

A second chart points the same way. In SpaceXAI's Grok 4.7 launch post, GDPval puts Fable 5.1 at 1,735 Elo and Astra at 1,542, below both Grok models tested.

Price is not identical either. Cached reads cost $1.00 per million on Astra and $0.25 on Fable 5.1, so cache-heavy agents pay up to four times more for reused context on Astra. Our GPT-6 Astra pricing breakdown works through that math.

When Is Astra Worth It Over Sol?

Astra costs five times as much as GPT-6 Sol per token. Its lead is largest where a task runs long and a mistake is expensive.

• Computer use: 72.6% on OSWorld 2.0 against Sol's 60.5%.
• Very long context: strong retrieval between 512K and 1M tokens.
• Open-ended work where the model has to decide what to check.

On shorter, well-scoped jobs, Sol often gets there first. OpenAI's own AutomationBench chart shows Sol at xhigh beating Astra at low effort for about a quarter of the cost per task.

A practical rule: run the task on Sol, and move only the failures to Astra. For bulk work, look at GPT-6 Luna instead.

What Can You Build with GPT-6 Astra

Astra belongs in products where a task runs long, touches real systems, and a mistake is expensive.

• Migration review assistant. In a developer portal, an engineer describes a schema change and Astra drafts the SQL plus the assumption most likely to change the result (see the test below).
• Front-end QA agent. Astra operates the browser, clicks through a staging site, and reports what broke, which is where its OSWorld lead matters.
• Due-diligence reader. Contracts and filings up to a million tokens go in, and the output is a list of risks with the passages they came from.
• Template-bound documents. Board decks and spreadsheets that must follow a company template, which OpenAI calls out as an Astra strength.

What we saw in a small test

We gave openai/gpt-6-astra a PostgreSQL 16 orders table with paid_at, shipped_at, and cancelled_at columns, and asked it to add and backfill a status column.

Astra opened with one line stating its assumption: cancelled beats shipped, shipped beats paid, and new rows default to pending. It then returned one transaction with the column, a CASE backfill, a default, NOT NULL, and a CHECK constraint.

We checked the backfill logic on five sample rows and each landed on the expected status. Run the full migration on a copy of your data before production. The call took about 46 seconds, most of it reasoning.

How to Use GPT-6 Astra

In ChatGPT, Astra is on Plus, Pro, Business, and Enterprise plans. Enterprise admins have to switch it on.

In the API, you can call it directly on OpenAI as gpt-6-astra, or through an OpenAI-compatible gateway such as Modellix, where it is openai/gpt-6-astra. Three things to plan for:

1. No none effort. The lowest setting is low, so every call carries some reasoning tokens.
2. Fast mode runs up to twice as fast at twice the price.
3. Security-adjacent prompts can stop. Build a fallback path if your product touches vulnerability work.

The example below uses the OpenAI Python SDK with Modellix. Create a key in the Modellix console, then change two lines from a standard OpenAI setup: base_url and the model name. To call OpenAI directly instead, drop base_url and use gpt-6-astra.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://llm.modellix.ai/v1",
    api_key=os.environ["MODELLIX_API_KEY"],
)

resp = client.responses.create(
    model="openai/gpt-6-astra",
    reasoning={"effort": "high"},
    input="Plan the migration from our monolith's orders module to a separate service. "
          "State every assumption that could change the plan.",
)
print(resp.output_text)

Try GPT-6 Astra with One API Key for Every Model

Astra is the model you escalate to, which means it never runs alone. The cheaper tiers, a Claude comparison, and whatever image model your product uses all end up on separate accounts and bills.

However, the Modellix key from the example above covers that whole stack. It also reaches GPT-6 Sol, GPT-6 Luna, Claude Fable 5.1, Gemini, and Grok, so "Sol first, Astra for the failures" is a one-string change to model.

Same tokens, lower bill

OpenAI's official API pricing lists GPT-6 Astra at $10 per million input tokens and $50 per million output tokens. For one call with 8,000 input tokens, 8,000 output tokens, and no cache, that is about $0.48 on OpenAI and $0.432 on Modellix, as the Modellix LLM calculator shows below.

At 1,000 calls with that mix, Modellix bills $48 less than OpenAI direct. At 100,000 calls the gap is $4,800. This is a price comparison at the same billed token counts, not a quality benchmark, and it stays inside the 272K input tier.

The same key also runs image and video models. Once Astra has drafted and checked a launch brief, GPT Image 2 can produce the hero image and Seedance 2.0 a short clip from it.

Pin openai/gpt-6-astra for evaluations, or use ~openai/gpt-astra-latest to pick up future Astra releases without a code change.