ChatGPT Astra and Claude Fable are two of the most capable AI models available in 2026, with both designed for difficult reasoning, coding, research, large documents, and long-running agentic tasks. OpenAI released GPT-6 Astra in September, while Anthropic followed its original Fable 5 with Claude Fable 5.1 on September 1.
The specifications are remarkably close, but the models differ once you look at computer use, caching costs, knowledge cutoff, long-running agents, and availability.
ChatGPT Astra vs Claude Fable 5.1 specifications
| Feature | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Context window | 1.05 million tokens | 1 million tokens |
| Maximum output | 128K tokens | 128K tokens |
| Input price | $10 per 1M tokens | $10 per 1M tokens |
| Output price | $50 per 1M tokens | $50 per 1M tokens |
| Cached input | $1 per 1M tokens | $0.25 per 1M tokens |
| Knowledge cutoff | April 30, 2026 | June 2026 |
| Image input | Yes | Yes |
| Reasoning | Adjustable effort | Adaptive, always on |
Astra technically has the larger context window at 1.05 million tokens, although the difference from Fable’s 1 million tokens will rarely matter in normal use. Both can generate up to 128,000 output tokens, making them suitable for large codebases, research collections, books, technical documentation, and other unusually large projects.
Where ChatGPT Astra performs better
OpenAI built GPT-6 Astra around end-to-end professional work, including research, software engineering, browsing, computer control, document creation, spreadsheets, presentations, and other tasks that require the AI to interact with multiple tools.
Astra supports five reasoning-effort levels ranging from low through max, which gives developers more control over the balance between cost, latency, and reasoning depth.
OpenAI’s published evaluations also give Astra an advantage in several agent-oriented tests:
- 64.6% on Terminal-Bench Science 0.1, compared with 52.6% for Fable 5.1
- 57.9% on Terminal-Bench 4.0, compared with 55.8% for Fable 5.1
- 95.9% on BenchCAD, compared with 84.3% reported for Fable 5.1
- Stronger emphasis on browser and computer control for completing real software workflows
These figures come from OpenAI’s own launch evaluations, so they are best treated as useful indicators rather than completely independent benchmarks.
Astra therefore makes more sense when you want one model handling a complete workflow that involves research, websites, applications, files, coding, and finished business documents.
Where Claude Fable 5.1 performs better
Claude Fable 5.1 focuses heavily on demanding reasoning and long-horizon agentic work, particularly projects where an AI continues coding, researching, reviewing files, and using tools across many steps.
Anthropic specifically positions Fable 5.1 for long-running agentic coding, multistep research, documents, spreadsheets, and presentations. It also uses adaptive reasoning automatically rather than requiring users to select one fixed thinking mode.
Fable also has several practical advantages:
- Its June 2026 knowledge cutoff is newer than Astra’s April 30 cutoff.
- Cache reads cost only $0.25 per million tokens.
- Astra charges $1 per million cached tokens.
- Fable is already generally available across Claude’s API and major cloud platforms.
That cheaper cache pricing becomes important for coding agents and large applications that repeatedly reuse the same codebase, documentation, instructions, or system context.
ChatGPT Astra vs Claude Fable: Which should you choose?
For most demanding users, GPT-6 Astra has the stronger overall capability set in 2026, particularly for computer use, complex tool-driven workflows, coding, research, and tasks that move between several applications.
Claude Fable 5.1 remains a strong choice for long-running coding and research agents, especially when an application repeatedly processes a large cached context. Its newer knowledge cutoff and $0.25 cached-input pricing also give it clear practical advantages.
If your work depends heavily on controlling software and completing entire workflows, Astra is the stronger choice. For large, persistent agentic projects where context reuse matters heavily, Fable 5.1 can cost less while remaining highly capable.
