Model comparison
Compare GPT-6 Astra and Claude Fable 5.1 using the same provider-sourced text & reasoning rubric. No mystery score and no invented benchmark ranking.
Facts checked September 4, 2026
Set your usage. Your estimate updates as you type.
Example: 6,000 input tokens for 10 pages, plus a 500-token summary. Page lengths vary; adjust the numbers below.
One run sends your input to the model once and receives an answer. Tokens are pieces of text: input is what you send, output is the answer you receive.
Estimates exclude taxes, tools, cache storage/writes, free allowances and custom discounts. Image estimates cover output only, not prompt or reference-image charges. Quality modes differ by model. Unlisted settings are not treated as free.
| Model | Access | Estimated total (USD) |
|---|---|---|
| Claude Fable 5.1Anthropic | Anthropic | $0.085 |
| GPT-6 AstraOpenAI | OpenAI | $0.085 |
Quick take
OpenAI's most capable model for difficult end-to-end work, combining frontier reasoning with a million-token context window and a broad set of agent tools.
Access is still rolling out, fine-tuning is not supported, and requests above 272K input tokens use higher rates across the full request. Confirm access and measure total task cost before standardizing on Astra.
Anthropic's frontier model for ambitious, long-running agentic and coding work, with strong vision and enterprise marketplace availability.
Confirm whether the default 30-day retention, fallback safeguards, and selected cloud marketplace meet your governance requirements before adoption.
Compare the published facts
Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.
| Text & reasoning | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Context windowMaximum combined prompt and working context documented by the provider. | 1.05M tokens | 1M tokens |
| Maximum outputProvider-published response limit, where available. | 128K tokens | 128K tokens |
| Knowledge cutoffLatest reliable knowledge date explicitly published by the model provider. Search and connected tools can retrieve newer information but do not change the model's built-in cutoff. | April 30, 2026 | June 2026 |
| Input priceCurrent standard list price per million input tokens unless noted. | $10 / 1M | $10 / 1M |
| Output priceCurrent standard list price per million output tokens unless noted. | $50 / 1M | $50 / 1M |
| InputsMedia types accepted by the listed model endpoint. | Text, image | Text, image, PDF |
| Tools & agentsSelected native tools and agent-building capabilities, not an exhaustive list. | Functions, search, code, computer, image, MCP | Tool use, agents, vision, coding workflows |
How to choose
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
OpenAI says API content is not used for model training by default. Standard abuse-monitoring logs may be retained for up to 30 days, with additional controls available to qualifying organizations.
Provider and API links
Fable requires 30-day retention for safety monitoring by default. Anthropic says enterprise data is not used for training without explicit permission; eligible enterprises can use EFS/customer-controlled storage or temporary zero-retention arrangements.
Frequently asked questions
GPT-6 Astra: OpenAI's most capable model for difficult end-to-end work, combining frontier reasoning with a million-token context window and a broad set of agent tools. Claude Fable 5.1: Anthropic's frontier model for ambitious, long-running agentic and coding work, with strong vision and enterprise marketplace availability.
Consider GPT-6 Astra when your priority is Complex, long-running agent workflows. Consider Claude Fable 5.1 when your priority is Long-running autonomous projects. Test both with your own data and provider route before committing.
No. This comparison aligns provider-published facts for the Text & reasoning category. It does not claim a universal winner or combine incompatible third-party benchmark scores.
