Differences between GPT-6 Astra and Claude Fable 5.1
Choosing between GPT-6 Astra and Claude Fable 5.1 is a decision about frontier models that many teams are currently facing. The comparison below can help you make a decision easier.
Both GPT-6 Astra & Claude Fable 5.1 models are aimed at highly demanding tasks such as complex programming, research, knowledge work, and even agentic tasks. Both have the same listed price for the API of 10 USD per million input tokens and 50 USD per million output tokens. Both models are now supported on WidelAI and WidelAI Code, allowing you to compare them in the same workspace, and use either model to run your programming workflow locally.
What is GPT-6 Astra?
GPT-6 Astra is OpenAI’s flagship model, succeeding GPT-5.6 Sol, and is built on agentic execution instead of just conversation. OpenAI targets this model for computing, professional work, and software engineering tasks; This is also OpenAI’s first model to pass the “Critical” cybersecurity threshold in the company’s Preparedness Framework. This model operates with a context window of 1,050,000 tokens, a maximum output limit of 128,000 tokens, and a knowledge cutoff until April 30, 2026.
What is Claude Fable 5.1?
Claude Fable 5.1 is Anthropic’s pioneering model that has been widely released, for tasks that require complex reasoning and long-term agentic work. This model has a context window of 1 million tokens, a maximum output limit of 128,000 tokens, an always-enabled adaptive thinking feature, and an updated data date until June 2026. According to Anthropic’s documentation, the latency of this model is higher (slower) than Claude Opus 5 and Claude Sonnet 5 – two models with lower prices than it.
Table comparing the differences between GPT-6 Astra and Claude Fable 5.1
| Features | GPT-6 Astra | Claude Fable 5.1 |
| Release date | September 3, 2026 | September 1, 2026 |
| API model ID |
gpt-6-astra
|
claude-fable-5-1
|
| Context window | 1.05M tokens | 1M tokens |
| Max output | 128K tokens | 128K tokens |
| Knowledge cutoff | April 30, 2026 | June 2026 |
| Listed price per 1 million tokens | $10 in / $50 out | $10 in / $50 out |
| Read stored input/ 1M | $1.00 ($2.00 above 272K) | $0.25 |
| The price applies to the excess of 272,000 input tokens | 2x input and cache, 1.5x output | No additional fees |
| FrontierMath level 4 (v2) | 97.6% | 87.8% |
| ScreenSpot-Pro (no tools) | 92.7% | 87.3% (Fable 5, from Mythos) |
| AutomationBench | 41.4% | 31.4% |
| ExploitBench | 100% | 70% |
| AA Intelligence Index | 61 | 66 |
| AA fee/Intelligence Index quest (maximum) | $1.67 | $3.76 |
| Strongest field | Computer usage, mathematics, cybersecurity, cost per task | Inference depth, agent loops are mainly influenced by cache |
Tasks are appropriate for each model
| Mission | Start with | Reason | Measure |
| Cross-application workflows use source code, browsers, and documents | GPT-6 Astra | Comprehensive emphasis on computer usage and tool workflows | Completed steps, recovery process, tool latency |
| Multi-file functionality in an unfamiliar source code repository | Claude Fable 5.1 | Long range encryption and context reuse | Test pass rate, change size, errors detected through evaluation |
| Debugging from the root cause with noisy logs | Claude Fable 5.1 | Strong position in diagnostics and maintenance agents | Accurate diagnosis before patching |
| Research with refined tools and outputs. | GPT-6 Astra | Focus on research, content review, and document creation | Citation quality, factual errors, editing time |
| The agent maintains a stable context across multiple interactions | Claude Fable 5.1 | Low cache read overhead | Execution overhead, cache usage, interventions |
| The workflow requires direct user calibration | GPT-6 Astra | Adjust steering direction midway through the Feedback procedure | Work is saved after editing |
| Architectural decisions have major consequences | Both | Independent thinking reveals assumptions. | Agreement, evidence, unresolved risks |
| Regular format or simple sample content | A cheaper model | It is not necessary to choose the pioneer model as default. | Cost and latency combined |
In short:
Choose GPT-6 Astra if:
- Your agents operate the actual software.
- You need professional, meticulous products.
- Jobs related to mathematics or physical sciences.
- You do defensive security work.
- You are using Codex.
- The cost per task is more important than the maximum score.
Choose Claude Fable 5.1 if:
- You run long agent loops whose overhead comes mainly from cache reads instead of generating output content.
- Your requests are large.
- You want the best score for independent reasoning and are willing to pay for it.
- You are working in the Claude Code environment.
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