GPT-6 Astra vs Claude Fable 5.1: which model should you use for coding?

GPT-6 Astra is the stronger starting point for missions that combine code, browsing, computer use, and a finished deliverable. Claude Fable 5.1 remains a top choice for very long agentic projects, document-heavy work, and teams already built around Claude Code.

There is no universal winner. Both models have exactly the same standard API token price — $10 per million input tokens and $50 per million output tokens — and both accept roughly one million tokens of context. The decision therefore turns on success in your environment, tools, caching, governance, and final quality rather than a brand hierarchy.

This comparison uses technical specifications and results published by OpenAI and Anthropic, reviewed on September 7, 2026. It does not replace testing on your own repository.

Astra and Fable 5.1 at a glance

The GPT-6 Astra specifications list a 1,050,000-token context window, 128,000 maximum output tokens, and five effort settings from low to max. The Claude Fable 5.1 specifications list a one-million-token context window, 128,000 maximum output tokens, and adaptive thinking that is always on.

The matching headline price hides meaningful differences. OpenAI charges twice the input and cache rate, plus 1.5 times the output rate, for the full request when input exceeds 272,000 tokens. Anthropic keeps Fable 5.1’s standard rate across its long context and charges $0.25 per million tokens for cache reads. A workflow that repeatedly reuses the same codebase can therefore have a very different cost profile from a one-off run.

What the benchmarks actually show

OpenAI’s Astra launch page compares Astra and Fable 5.1 directly on several evaluations. Astra scores 57.9% versus 55.8% on Terminal-Bench 4.0, 74.1% versus 67.4% on DeepSWE 1.1, and 63.9% versus 57.8% on OpenAI’s internal database migration tasks.

Astra remains slightly ahead on FrontierCode 1.1 Extended: 64.5% versus 63.6% for Fable 5.1. On Humanity’s Last Exam with tools, Fable 5.1 reaches 65.0% versus 57.2% for Astra. Astra leads the published comparison on Terminal-Bench Science 0.1, AutomationBench, and BenchCAD.

These numbers are not a universal championship.

• The vendor selects settings, harnesses, tools, and sometimes internal tasks.
• The highest effort setting may not have the same cost or latency.
• Close results can fall within statistical uncertainty.
• A terminal benchmark does not automatically predict interface quality, review quality, or product judgment.
• Safeguards can alter or interrupt some trajectories.

The defensible reading is: Astra holds an advantage across several end-to-end agentic tasks; Fable 5.1 remains highly competitive and wins some demanding evaluations. Anything broader needs independent evaluation and, most importantly, your own work samples.

Astra has a slight generalist edge for coding

OpenAI positions Astra for complete software engineering: understand a repository, change multiple files, operate the terminal, navigate applications, and inspect the result. Through the Responses API, the model natively supports web search, file search, hosted shell, apply_patch, computer use, MCP, and tool search.

Inside Codex, Astra can also preserve notes beyond one context window and search previous windows. This experimental feature targets a real problem in long missions: compaction can omit why a fix failed, a constraint, or a test already performed.

That makes Astra especially appealing for a mission such as:

The advantage is not just code generation. It is continuity across analysis, execution, and inspection. For a team already using Codex, ChatGPT Work, or the Responses API, that integration can matter more than one benchmark point.

Fable 5.1 remains formidable on long projects

Anthropic describes Fable 5.1 as its model for ambitious projects lasting several hours or days: codebase-wide features, code review, performance work, multi-step research, and polished document deliverables. It can vary effort per message, emit readable progress updates between tool calls, and preserve thinking blocks across some model transitions.

Fable 5.1 is naturally attractive when a team has already built prompts, hooks, permissions, and evaluations around Claude Code or the Messages API. Switching providers also means changing tool behavior, cache formats, observability, and sometimes governance.

Its cache is another concrete advantage: reads at $0.25 per million tokens are four times cheaper than Astra’s standard $1 cached-input rate. On a mission that repeatedly reuses a large documentary base, the difference can become material despite identical headline input and output prices.

Fable 5.1 is not “a worse Astra.” It optimizes another set of constraints: agentic continuity, reused context, and Anthropic integration.

Effort is part of the model configuration

Comparing model names alone hides a second variable: how much reasoning the mission receives. Astra offers five effort levels. Fable uses always-on adaptive thinking and also lets developers steer effort, with high as the default.

Maximum effort can help a difficult investigation, but it will often increase elapsed time and reasoning tokens. A bounded change may reach the same result faster at a middle setting. A delicate migration or intermittent bug may justify extra effort because it funds more hypotheses and verification passes.

Your evaluation should therefore compare configurations, not only models: Astra high against Fable high, then each model’s best setting under the same cost ceiling. If you compare Astra max with Fable high without counting tokens and duration, the result will be hard to use.

Why this is not Astra vs Mythos 5.1

Anthropic says Mythos 5.1 uses the same underlying model as Fable 5.1. The difference is its reduced cybersecurity and biology safeguards. Mythos remains invite-only for a small group of vetted organizations.

For an ordinary developer, agency, or product team, Mythos is neither an accessible option nor a third general-purpose profile. The useful comparison is Astra against Fable 5.1. Mythos only becomes relevant to an eligible organization conducting advanced defensive security or biology research through a trusted-access program.

Safeguards, privacy, and work continuity

Both models have reached a capability level that requires stronger controls.

OpenAI classifies Astra at the “Critical” level for cybersecurity. Additional checks can slow, pause, or stop legitimate work; in the API, a blocked task stops. In return, Astra supports Zero Data Retention for eligible API customers.

Fable 5.1 applies classifiers to cyber, biology, and chemistry topics. Some requests are routed to a less capable Opus model; Anthropic says those requests are not billed at the Fable rate. Fable uses 30-day safety-monitoring retention by default, with exceptions for some Enterprise customers.

These are not footnotes. If your workflow touches proprietary code, security, or regulated data, retention policy and blocked-task behavior can decide the choice before raw capability does.

How to choose without fooling yourself

Do not ask both models to “build this feature” and compare only their written answers. Give them the same environment and require an observable outcome.

1. 01

   Sample

   Select 10 to 20 representative missions: bug, feature, review, migration, research, and visual inspection.

2. 02

   Align

   Use the same repository revision, permissions, tools, criteria, and budget limit.

3. 03

   Measure

   Record success, quality, duration, tokens, cost, retries, tool errors, and human interventions.

4. 04

   Replay

   Repeat critical missions: one run can reward luck or a particular route.

5. 05

   Decide

   Choose by mission category rather than forcing one model on the whole team.

The central metric is cost per correctly completed mission. A cheaper run that requires two retries, a human review, and a manual patch costs more than its API bill. With equal token prices, cache behavior, output length, latency, and success rate matter even more.

Score the accepted result, not the most confident report. A run only passes when its code, tests, rendered interface, and expected side effects satisfy the same criteria. Record partial completion separately: a model that produces an excellent plan but leaves the final integration untested did not complete the same job as one that proved the user journey.

Start with an AI-ready project brief and use a clear method to verify an AI agent’s work. When several agents contribute, establish boundaries with our guide to coordinating multiple AI coding agents.

A more realistic strategy than one winner

A team can use Astra for missions spanning browser, terminal, and applications, then Fable 5.1 for long document research or review inside an already optimized Claude environment. It can also standardize on one provider to reduce operational complexity. Both strategies are rational when they follow measured outcomes.

In Stellary, the model can follow the mission instead of becoming one global preference. The board retains the objective, owner, and work state; the knowledge base keeps useful decisions; agents receive the right context and tools. That management layer remains necessary whichever model wins: neither Astra nor Fable replaces the brief, permissions, or validation.