Claude Fable 5.2 vs GPT-6 Astra: Benchmarks, Pricing, and Key Differences
Anthropic released Claude Fable 5.2 on November 9, 2026, positioning it as a model for demanding technical and long-running agentic tasks. OpenAI's GPT-6 Astra, launched in September, is its closest competitor in this comparison.
Anthropic's published benchmarks show Fable 5.2 ahead of Astra across several areas, including coding, computer use, and long-horizon knowledge work. It is also cheaper per token.
This article compares their capabilities, benchmarks, pricing, and practical use cases.
Fable 5.2 vs GPT-6 Astra
Benchmarks: Fable 5.2 leads Astra on every shared benchmark reported by Anthropic.
Pricing: Fable 5.2 has lower input, output, and cached-token rates.
Long-running tasks: Fable 5.2 performs particularly well on multi-step research and technical work.
Computer use: Fable 5.2 leads on Anthropic's reported OSWorld 2.1 results.
Availability: Astra is a better fit for teams already using OpenAI's ecosystem or requiring eligible Zero Data Retention support.
One important qualification: the head-to-head benchmark figures come from Anthropic's launch materials. Independent comparisons are still needed.
What Is Claude Fable 5.2?
Claude Fable 5.2 is Anthropic's model for demanding technical work, released in November 2026. It is designed for complex reasoning, coding, research, and long-running agent workflows.
It shares its underlying model with Claude Mythos 5.2, a version with different cybersecurity and biology safeguards intended for vetted organizations.
Fable 5.2 supports:
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Anthropic still recommends Claude Opus 5.5 as the default for everyday coding and knowledge work, positioning Fable 5.2 for more demanding tasks.
What Is GPT-6 Astra?
GPT-6 Astra is OpenAI's frontier model, launched in September 2026 as the successor to GPT-5.6 Sol.
Its focus includes professional work, computer use, reasoning, and complex workflows. OpenAI has also highlighted its performance on challenging mathematical and abstract reasoning benchmarks.
Astra supports a 1.05-million-token context window and up to 128K output tokens. It is available through OpenAI's ecosystem, including ChatGPT, the API, Codex, and supported cloud platforms.
Claude Fable 5.2 vs GPT-6 Astra: Head-to-Head Comparison
Feature | Claude Fable 5.2 | GPT-6 Astra |
Developer | Anthropic | OpenAI |
Released | November 2026 | September 2026 |
Context window | 1M tokens | 1.05M tokens |
Maximum output | 128K tokens | 128K tokens |
Effort levels | Low, medium, high, xhigh, max | Low, medium, high, xhigh, max |
Remote Labor Index | 41.3% | 20.8% |
Humanity's Last Exam (with tools) | 74.2% | 57.2% |
FrontierCode 2.0 | 49.1% | 42.8% |
OSWorld 2.1 | 89.3% | 79.5% |
Chartography | 73.4% | 71.0% |
Fable 5.2 leads across the shared results, with particularly large differences in long-horizon knowledge work and computer-use evaluations.
Long-horizon knowledge w
ork and research
Fable 5.2's biggest advantage is its performance on complex, multi-step tasks.
On the Remote Labor Index, which evaluates freelance-style projects, Fable 5.2 scores 41.3%, compared with Astra's 20.8%.
Other reported results include:
Benchmark | Claude Fable 5.2 | GPT-6 Astra |
Remote Labor Index | 41.3% | 20.8% |
AA-Briefcase v1.1 | 66.1% (1,785 Elo) | 52.0% (1,569 Elo) |
Humanity's Last Exam | 74.2% | 57.2% |
Terminal-Bench-Science 0.1 | 74.6% | 68.1% |
AutomationBench | 51.4% | 41.4% |
Anthropic also reports that Fable 5.2 formalized a proof of Fermat's Last Theorem in Lean over 30 hours using approximately 1,000 agents.
These results suggest that Fable 5.2 is particularly capable of handling projects that require sustained reasoning rather than producing a single response.
However, the Astra figures in this comparison were run by Anthropic, and some differ from OpenAI's published results.
Coding and agentic workflows
Fable 5.2 also leads on the coding benchmarks reported by Anthropic.
Benchmark | Claude Fable 5.2 | GPT-6 Astra |
FrontierCode 2.0 | 49.1% | 42.8% |
FrontierSWE v2 | 69.9% | 65.5% |
FrontierCode 2.0 evaluates whether software maintainers would accept a model's code changes. Fable 5.2 scores 49.1% at xhigh effort, while its high-effort score is 45.3%.
The results indicate an advantage for Fable 5.2 in coding tasks that require more than generating code, particularly when the output must be suitable for integration into existing projects.
Computer use, vision, and robotics
Fable 5.2 performs strongly in computer-use and robotics evaluations.
Benchmark | Claude Fable 5.2 | GPT-6 Astra |
OSWorld 2.1 | 89.3% | 79.5% |
Chartography | 73.4% | 71.0% |
RoboDojo Sim | 64.9% | 29.8% |
Chartography is relatively close, but the reported difference on RoboDojo Sim is substantial.
The OSWorld comparison requires some caution: OpenAI previously reported Astra on OSWorld 2.0, while Anthropic's comparison uses OSWorld 2.1. Different benchmark versions can affect results.
Claude Fable 5.2 vs GPT-6 Astra Pricing
Fable 5.2 is cheaper than Astra across the standard token rates provided by both vendors.
Token pricing
Pricing per 1M tokens | Claude Fable 5.2 | GPT-6 Astra |
Input | $8.00 | $10.00 |
Output | $40.00 | $50.00 |
Cached input read | $0.20 | $1.00 |
Cache write | $10 (5-minute), $16 (1-hour) | $12.50 |
Batch discount | 50% | 50% |
Fast mode | $16 / $80 | 2x applicable rates |
Astra also applies a long-context surcharge when input exceeds 272K tokens per request. This increases input and cache rates by 2x and output rates by 1.5x for the entire request.
Fable 5.2 does not list an equivalent surcharge across its 1-million-token context window.
Example workload costs
Workload | Claude Fable 5.2 | GPT-6 Astra |
Balanced assistant | $18.00 | $22.50 |
Retrieval below 272K per request | $120 | $150 |
Retrieval above 272K per request | $120 | $275 |
Cache-heavy loop | $101 | $201 |
The difference becomes more significant for long prompts and repeated cached reads. Astra's cached-read rate is five times higher, and its long-context surcharge can substantially increase costs.
These figures compare estimated costs using the listed token rates. Actual spending depends on token consumption and workload design.
When to Choose Claude Fable 5.2 vs GPT-6 Astra
Fable 5.2 is the stronger choice for many demanding technical workloads based on the reported comparisons. Astra remains attractive for teams already invested in OpenAI's ecosystem and for specific requirements around data retention and long-context retrieval.
Choose Claude Fable 5.2 if:
Your agents handle long-running research and multi-step projects.
You need advanced coding and code changes that can be integrated into existing projects.
You frequently work with cached prompts or large context windows.
Choose GPT-6 Astra if:
Your team already uses ChatGPT Business, Enterprise, Codex, or Azure.
You require Zero Data Retention and are eligible for OpenAI's offering.
Your work depends on long-context retrieval supported by Astra's published results.
You prioritize cost efficiency for workloads where Astra can achieve acceptable results.
Conclusion
Claude Fable 5.2 leads GPT-6 Astra across the shared benchmarks reported by Anthropic, particularly in long-horizon knowledge work, coding, and computer use. It also offers lower token prices and more favorable pricing for cached and long-context workloads.
However, GPT-6 Astra remains competitive for teams using OpenAI's ecosystem, and its published long-context retrieval results provide useful evidence for specific workloads.
For businesses choosing between the two, the best approach is to evaluate both models against their actual workflows, considering benchmark performance, data-retention requirements, token usage, and overall operating costs.
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