GPT-6 Astra vs Claude Fable 5.1: Benchmarks, Pricing, and Which to Use

Anthropic released Claude Fable 5.1 on September 1, 2026. Two days later, OpenAI answered with GPT-6 Astra. For a short window, the two most capable general-purpose models either company had shipped went head to head within 48 hours of each other, and the industry has spent the days since arguing over which one actually leads.

The honest answer is that neither model wins everything. On Artificial Analysis's Intelligence Index, an aggregate score the site runs independently across both models, Fable 5.1 scores 66 against Astra's 61, and Fable 5.1 is also the slightly cheaper of the two on blended real-world pricing. But on several individual benchmarks OpenAI published in its own launch comparison, Astra leads, in some cases by a wide margin.

This post compares what each model actually is, how they perform on the numbers worth trusting versus the numbers worth a raised eyebrow, pricing and context windows, how each company is handling the safety risk of a more capable, more agentic model, and which one fits which kind of work.

What Are GPT-6 Astra and Claude Fable 5.1?

GPT-6 Astra is OpenAI's newest large language model, released September 3, 2026 as a computer use model built to navigate browsers, spreadsheets, and applications the way a person does, with a 1.05 million token context window. We covered its capabilities, benchmark claims, and safety questions in full in our earlier post.

Claude Fable 5.1 is Anthropic's newest frontier model, released September 1, 2026, built for demanding reasoning and long agentic tasks with a focus on coding, multistep research, document and spreadsheet work, and computer use. It has adaptive thinking that is always on, accepts text and images, returns text, and supports a 1 million token context window with up to 128,000 output tokens.

Anthropic shipped it alongside Claude Mythos 5.1, which Anthropic says is the same underlying model with different safeguards. Fable 5.1 is generally available to everyone. Mythos 5.1 is available only through Anthropic's trusted access programs, with safeguards built specifically to support work in cybersecurity and the life sciences, and Anthropic is adding a Life Sciences Verification Program for biology research. Fable 5.1 and Mythos 5.1 are also the first Claude models to carry Anthropic's text watermarking.

How Do They Actually Compare on Benchmarks?

Start with the number closest to independent: Artificial Analysis, a third party that runs a standardized evaluation suite across models rather than relying only on what each lab reports, puts Fable 5.1 ahead on its Intelligence Index, 66 to Astra's 61. That index blends nine evaluations spanning coding, science, and general knowledge, so it is a reasonable single number if you want one.

OpenAI's own launch comparison tells a more mixed story on individual benchmarks. These figures come from OpenAI's materials, not from Anthropic or an independent lab, and outlets covering them have flagged that comparisons across different agent harnesses are not strictly apples to apples, so treat them as one side's framing rather than settled fact:

• FrontierMath Tier 4 (v2), a hard mathematics benchmark: Astra 97.6 percent versus Fable 5.1 87.8 percent.
• GPQA Diamond, graduate-level science questions: Astra 96.0 percent versus Fable 5.1 93.7 percent.
• AutomationBench, which simulates multistep office tasks: Astra 41.4 percent versus Fable 5.1 31.4 percent.
• Terminal-Bench Science, complex scientific workflows in a terminal: Astra 64.6 percent versus Fable 5.1 52.6 percent.
• DeepSWE v1.1, an agentic coding benchmark: Astra 74.1 percent versus Fable 5.1 67.4 percent.

On the benchmarks Anthropic chose to publish itself, comparing Fable 5.1 to Opus 5 and OpenAI's prior GPT-5.6 Sol rather than Astra directly, Anthropic reported Fable 5.1 scoring 65.0 percent on Humanity's Last Exam with tools, up from earlier Claude models, and leading Opus 5 on every benchmark it published. OpenAI has not put out a directly comparable Astra score against that same exam in its own materials, though third-party trackers list Astra behind Fable 5.1 there. Because Fable 5.1 shipped two days before Astra, Anthropic's own comparison naturally could not include it, and Anthropic has not yet published a head-to-head response.

The pattern that holds up across sources: Astra tends to lead on math-heavy, science, and terminal-based agentic benchmarks in OpenAI's own reporting, while Fable 5.1 wins the independent aggregate index and the exam-style reasoning benchmark Anthropic highlights. Neither company's self-reported numbers should be read as neutral, and no independent lab has yet run a full head-to-head across both models on every benchmark.

Pricing and Context Window

On paper, standard API pricing is close: both models list around $10 per million input tokens and $50 per million output tokens. The real difference shows up in cached tokens, which is where most of the cost sits in long agentic sessions. Fable 5.1's cache reads run about $0.25 per million tokens against roughly $1.00 per million for Astra, and Anthropic says that cut brings typical Fable 5.1 workloads in about 25 percent cheaper than Fable 5, and up to 45 percent cheaper for heavily agentic work. Reflecting that, Artificial Analysis puts blended real-world pricing at $7.17 per million tokens for Fable 5.1 against $7.70 for Astra at the same usage ratio.

Context windows are close to a wash: Astra supports roughly 1.05 million tokens against Fable 5.1's 1 million, and both cap output around 128,000 tokens.

How Are OpenAI and Anthropic Handling the Safety Risk?

Both companies shipped their most capable model yet alongside a more restricted version, which says something about where the industry is right now. As we covered in detail, Astra is the first OpenAI model to cross the Critical threshold in OpenAI's Preparedness Framework for cybersecurity, so its advanced cybersecurity capabilities are limited to vetted testers rather than shipped to everyone.

Anthropic's split works differently but rhymes. Fable 5.1 is the generally available model. Mythos 5.1, the same model with fewer restrictions, is gated behind Anthropic's trusted access programs specifically for cybersecurity and life sciences work. Separately, Anthropic said Claude Code users should see around 60 percent fewer erroneous cybersecurity warnings with Fable 5.1, a claim about the model producing more accurate security analysis rather than about offensive capability.

Those are two different axes worth keeping separate. OpenAI is managing the risk of a model becoming dangerously capable at offensive cybersecurity work. Anthropic is highlighting its model getting more precise at defensive code review. Read together, they say the same underlying thing: both labs now consider frontier-model cybersecurity capability something serious enough to gate, not just a benchmark to publish.

Which One Should You Actually Use?

If your work leans on heavy agentic coding, terminal-based tasks, or math and science reasoning, OpenAI's own numbers put Astra ahead, sometimes by a wide margin, though those are the company's own figures. If you want the closest thing to an independently measured aggregate score plus a lower blended cost for long agentic sessions, Fable 5.1 currently has the edge.

For most content and marketing work, the gap that matters less than the headline benchmark is how well a model follows structure, stays grounded in source material, and handles multistep tasks without drifting, which is closer to what AutomationBench and computer-use evaluations actually test than what a math olympiad benchmark like FrontierMath tells you. Both models are strong enough here that the deciding factor is usually cost, existing tooling, and which ecosystem your team already runs on rather than a two- or three-point gap on any single chart.

Where This Leaves Things

Two frontier models leapfrogging each other within 48 hours, each publishing numbers that make it look ahead, is the clearest sign yet of how compressed AI release cycles have become. Whatever leads this week is likely to be answered within a month, by one of these two labs or another. The more durable question is not which model wins today's chart, but which one fits your actual workload, budget, and risk tolerance, and that answer is worth revisiting every time either company ships again.

If you want help figuring out which model, or mix of models, actually makes sense for your content and marketing workflows, our AI training service is built to cut through exactly this kind of noise. Contact us and we will help you figure out what is worth adopting now.