GPT-6 Astra vs Claude Fable 5.1: Which One Is Better?

Two flagship AI models landed within days of each other this September, and they cost exactly the same. Anthropic released Claude Fable 5.1 on September 1, and OpenAI announced GPT-6 Astra on September 3, with the API following a day later. So if the sticker price is a tie, how do you choose?

The answer sits in the fine print. The two models handle long prompts, caching, computer control and safety limits in different ways, and those details change what you actually pay and what you can actually do. This comparison walks through each one using the figures the companies and independent trackers have published, and it flags which numbers are self-reported so you can weigh them accordingly.

GPT-6 Astra vs Claude Fable 5.1 at a Glance

The context and output figures come from third-party trackers rather than the two launch pages, so treat them as reported rather than official. The cached-input gap is the first sign that the two aren’t as identical as they look.

Pricing: Same Sticker, Different Fine Print

Both cost $10 | AED 37 per million input tokens and $50 | AED 184 per million output tokens.

At the headline rate, you pay the same for a short prompt on either model. The differences show up when you send the same material over and over, or when a prompt gets very long.

Start with caching. Claude Fable 5.1 charges $0.25 per million tokens for cached input, which Anthropic says is 75 percent cheaper than Fable 5. OpenAI lists cached input for Astra at $1 per million tokens, four times higher. If your app keeps sending the same system prompt, the same codebase or the same set of documents, that gap adds up quickly. Anthropic estimates typical workloads cost about 25 percent less than before, and highly agentic, cache-heavy ones up to roughly 45 percent less.

Now the long-prompt catch. According to the LLM Stats breakdown of OpenAI’s pricing, a request over 272,000 input tokens on Astra is billed at double the input and cache rates and 1.5 times the output rate, and it applies to the whole request, not only the tokens past the line. That works out to $20 input and $75 output per million tokens. To make that concrete, a single 300,000-token prompt costs about $6 in input on Astra, against about $3 at the standard $10 rate that Anthropic lists for Fable 5.1. One analyst pointed out that 272,000 is only about 26 percent of the advertised window, so the million-token headline is cheaper to claim than to use. Anthropic’s page lists a different extra: inference restricted to the US costs 1.1 times the standard rate.

Both companies offer 50 percent off through batch processing. OpenAI also sells a fast mode at double the rates and a reasoning-effort dial that runs from low up to max. Anthropic offers three effort levels: low, medium and high.

Context Window and Output Limits

Both models can take roughly a million tokens in one go and return up to 128,000 tokens in a single answer. That is enough to feed in a large codebase or a stack of long reports and ask for a detailed rewrite in one pass. If the term is new to you, our guide on what a context window is in AI explains why this number matters and where it stops being useful.

One practical point: a big window is not the same as perfect recall across all of it. Test your own documents before you trust either model with a huge input. And on Astra, remember the pricing step-up past 272,000 tokens, which makes very long single requests noticeably more expensive.

What GPT-6 Astra Brings

Astra’s headline feature is autonomous computer use. In OpenAI’s Responses API, the model can work through a browser, spreadsheets and other desktop software on its own to finish multi-step jobs, alongside web search, file search, a code interpreter and MCP connections. It takes text and images as input and returns text only. Fine-tuning isn’t supported.

OpenAI’s self-reported numbers are strong. It lists 57.7 on Terminal-Bench 4.0, 64.6 on Terminal-Bench-Science 0.1, 91.5 on BrowseComp and 41.4 on AutomationBench. On the abstract reasoning tests, it reports 99.9 on ARC-AGI-3 and 96.0 on GPQA Diamond, though the LLM Stats write-up notes those tests are close to their ceiling and tell you less than they once did.

There is a catch for security work. OpenAI rates Astra at the “Critical” cybersecurity capability level under its Preparedness Framework, and advanced defender workflows are limited to vetted programs. The public version refuses requests that go deep into that territory. Whether Astra is included in specific ChatGPT plans wasn’t clear from the sources I could check, so look at your own plan page before you count on it.

What Claude Fable 5.1 Brings

Fable 5.1 replaces the original Fable 5 from June and sits at the top of Anthropic’s public lineup. Anthropic pitches it for long jobs that run for hours across many apps: codebase-wide changes, code review, multi-stage knowledge work and agents that keep going with little supervision. It can also read diagrams, charts and tables inside files and PDFs. If agents are new to you, our explainer on AI agents covers what that word actually means.

Anthropic’s own reported results, as summarized by DataCamp, include 52.6 percent on Terminal-Bench-Science 0.1, 55.8 percent on Terminal-Bench 4.0, 65.0 percent on Humanity’s Last Exam with tools and 41.7 percent on the strict version of OSWorld 2.0. It also leads its sibling Opus 5 on every benchmark in that table.

You can use it in Claude on the web and desktop for Pro, Max, Team and Enterprise users, through the API as claude-fable-5-1, and on Amazon Web Services, Google Cloud and Microsoft’s cloud. If you are new to the family, our guide to how Claude works is a good starting point. There is also a more permissive sibling, Mythos 5.1, limited to vetted security and life-science organizations.

Benchmarks: Who Is Ahead Where

Here is the part that deserves caution. Each company publishes its own scores, on its own test setups. They don’t always line up, so I’d read the table below as a rough map and not a final ruling.

On those four, Astra leads on three and Fable 5.1 on one, the hardest-reasoning exam. That matches the pattern in the launch coverage: Astra posted a near-perfect ARC-AGI-3 score yet trailed on Humanity’s Last Exam. Computer-use tests are harder to compare, because OpenAI reports an “offline partial” OSWorld 2.0 score of 72.6 while Anthropic’s strict score is 41.7 and its partial score is 77.9. Different settings, different numbers.

Anthropic also says that tasks blocked by its safeguards were scored as zero, which likely pulls its reported results down a little. The honest takeaway is that both models are very close at the top, and your own task will decide the winner better than any chart.

Safety Limits That Affect Everyday Users

Both labs now route some requests away from the flagship. On Claude, questions about cybersecurity or biology that trip the safeguards are sent to older Opus models, and API customers must set up a Fallback API. Anthropic says rerouted requests aren’t billed at Fable prices. On OpenAI’s side, the advanced cyber features sit behind its Trusted Access program rather than in the default product.

For most readers writing emails, studying or building ordinary apps, none of this will matter. If you work in security research, penetration testing or biology, it matters a lot, and you should check which model your account can actually use. Anthropic also notes that output from models released after August 2, 2026 is watermarked under the EU AI Act Code of Practice.

Which One Should You Pick?

Pick GPT-6 Astra if you want a model that can drive a browser and desktop apps for you, if your work leans toward terminal and automation tasks, or if you already live inside OpenAI’s tools. Keep an eye on prompt length, because going past 272,000 tokens changes the bill.

Pick Claude Fable 5.1 if you run long agentic or coding sessions, reuse the same large context again and again, or care about the lower cached-input rate. It also has the stronger reported result on the hardest reasoning exam in the table above.

If you can’t decide, run the same three real tasks on both. Use a prompt you actually send every week, a long document, and one multi-step job. Compare the answers, the time and the cost, then pick the one that wastes less of your day. Budget-minded teams should also keep watching cheaper rivals such as DeepSeek’s V4.1 Flash. And if you are brand new to all of this, start with what ChatGPT is before comparing flagships.

Which one have you tried so far? Tell us in the comments what you used it for.

How to Test Both Models Yourself

Published scores only tell you so much, so a short home test is worth the hour. Pick three jobs from your own week. A good set is a summary of a long PDF you already know well, a small coding or spreadsheet fix, and a research question where you can check the answer. Run the identical prompt on each model, using the same effort level where the app allows it.

Then score the results on three things: whether the answer was correct, how many follow-ups it needed, and what it cost in tokens or subscription time. Write the results down. Most people find that one model suits their style of prompting better, and that gap is rarely visible in a benchmark table. Repeat the test in a month, because both labs keep updating their models.

Frequently Asked Questions

Which is cheaper, GPT-6 Astra or Claude Fable 5.1?

The base prices match at $10 per million input tokens and $50 per million output tokens. Fable 5.1 is cheaper for cached input at $0.25 versus $1, and Astra costs more once a prompt passes 272,000 input tokens.

Which model has the bigger context window?

They are close. Astra is listed at 1,050,000 tokens and Fable 5.1 at 1 million, and both can output up to 128,000 tokens.

Is GPT-6 Astra better than Claude Fable 5.1 at coding?

The reported scores are close. OpenAI lists 57.7 on Terminal-Bench 4.0 and Anthropic lists 55.8 percent, but the companies run their own tests, so try both on your own code.

Can I use these models for free?

The free API tier doesn’t support Astra. Fable 5.1 is available to paid Claude plans (Pro, Max, Team and Enterprise) and through the API.

Why do some security questions get refused?

Both companies limit advanced cybersecurity help in the public versions. Claude reroutes those requests to older models, and OpenAI keeps its advanced cyber features for vetted programs.

Image credits: Photo: TechCrunch, Wikimedia Commons, CC BY 2.0. • Photo: TechCrunch, Wikimedia Commons, CC BY 2.0.