Anthropic documentation overview for Claude Fable 5.1
Claude Fable 5.1 overview, captured 2026-11-01. Image: AppFor.ai screenshot of Anthropic's page. Image source ↗

The short answer: decide by ecosystem and access, then test

On the API the two cost the same, so pick by where your work runs. In ChatGPT, Astra is part of Plus (with limited usage), Pro, Business and Enterprise. In Claude, Fable 5.1 is Anthropic's option for tasks where Opus 5.5 at high effort still fails your checks, and it costs extra usage on Pro.

Scope and evidence

Task: choosing a top-tier model for long agentic work and research. Fable facts come from Anthropic's Fable 5.1 overview and Claude pricing; Astra facts from OpenAI's model page and ChatGPT pricing. Pages read between October 28 and November 1, 2026.

No shared benchmark table with both models was available to us, so this page compares documented price, limits and access. AppFor.ai did not run either model.

Compare the same criteria

Each line sets Anthropic's documentation for Fable against OpenAI's for Astra.

  • API price per million tokens. Fable 5.1: $10 input, $50 output, Batch 50% off. Astra: $10 input, $50 output; prompts over 272K input tokens cost 2x input and 1.5x output. What this means: equal at normal prompt sizes, Astra costs more on very long prompts.
  • Context and output. Fable: 1M context, 128K output. Astra: 1.05M context, 128K output. What this means: practically equal.
  • Latency and effort. Fable: Slower, adaptive thinking always on, default effort high. Astra: OpenAI lists speed as Fast, reasoning effort from low to max. What this means: Fable suits unattended jobs.
  • App access. Fable: not on Claude Free, via usage credits on Pro, up to 50% of weekly limits on Max, and on Ultra also up to half of the weekly limit. Astra: included in ChatGPT Plus (limited), Pro, Business and Enterprise. What this means: Astra is easier to reach in an app.
  • Knowledge cutoff. Fable: June 2026. Astra: April 30, 2026. What this means: Fable is two months fresher.
OpenAI API model page for GPT-6 Astra
GPT-6 Astra model page on OpenAI, captured 2026-11-01. Image: AppFor.ai screenshot of OpenAI's page. Image source ↗

Which fits your real work?

A researcher on ChatGPT Plus. Astra is included in Work and Codex with limited usage; more Astra use needs credits.

A team whose agents run on Claude and where Opus 5.5 misses the quality bar. Fable 5.1 fits as the next step.

A developer choosing an API for long unattended runs. Price is equal; run the trial below.

Cost of getting the work done

API: 2 million input and 200,000 output tokens cost $30 on either model at standard rates, before caching or batch discounts.

Apps: ChatGPT Plus is $20 a month and includes limited Astra usage in Work and Codex, with credits beyond that; on Claude Pro, Fable is paid through usage credits.

Try both on the same small task

Suggested check: give both models the task that your current model fails.

  • Every number has a citation that supports it.
  • All sources are addressed.
  • No figure appears that is not in the sources.
  • Note tokens and time per run.

Decision checklist

Use the lines that match your situation. More in our Claude Fable 5.1 and GPT-6 Astra profiles.

  • If you work mainly in ChatGPT, use Astra.
  • If you are on Claude and Opus 5.5 falls short, try Fable 5.1.
  • If prompts exceed 272K tokens, Fable is cheaper per token.
  • If latency matters, check Fable's Slower label first.
  • If you are on Claude Pro, budget usage credits for Fable.

Sources & further reading

Based on the official documentation below. Features and access can change; check the provider before making a decision.

  1. Anthropic: Claude Fable 5.1 overview (opens in a new tab)
  2. Anthropic: Claude pricing (opens in a new tab)
  3. OpenAI: GPT-6 Astra model page (opens in a new tab)
  4. OpenAI: ChatGPT pricing (opens in a new tab)
Something out of date or incorrect?Report a correction

Was this page useful?

Help us make our coverage more useful. This is feedback on the page, not a rating of the tool.

Share this page

Related reading