GPT-6 Astra vs Claude Fable 5.1
Compare GPT-6 Astra vs Claude Fable 5.1 API pricing, token costs, context windows, and latency. See which model is cheaper and better for your stack.
GPT-6 Astra charges $10.00/1M input and $50.00/1M output tokens, compared to Claude Fable 5.1 at $10.00/1M input and $50.00/1M output. GPT-6 Astra is 1.0x cheaper on input tokens. In reasoning benchmarks, GPT-6 Astra holds the higher AA Intelligence Index rating (57/100). For latency-critical streaming, GPT-6 Astra delivers 47 t/s (192.0s TTFT) while Claude Fable 5.1 provides 88 t/s (155.0s TTFT).
Full Metric & Specification Comparison
| Metric | GPT-6 Astra | Claude Fable 5.1 | Advantage |
|---|---|---|---|
| Provider | OpenAI | Anthropic | - |
| Input Token Cost (1M) | $10.000 | $10.000 | Tie |
| Output Token Cost (1M) | $50.000 | $50.000 | Tie |
| Context Window | 1,050k tokens | 1,000k tokens | GPT-6 Astra |
| AA Intelligence Index | 57/100 | 49/100 | GPT-6 Astra |
| Streaming Latency | Deliberate | Moderate | - |
Key Architectural Takeaways
- Input Token Cost: GPT-6 Astra is $10.00/1M vs Claude Fable 5.1 at $10.00/1M
- Output Token Cost: GPT-6 Astra is $50.00/1M vs Claude Fable 5.1 at $50.00/1M
- Context Window: GPT-6 Astra supports 1,050k tokens vs Claude Fable 5.1 with 1,000k tokens
- Benchmark Intelligence Rating: GPT-6 Astra scores 57/100 vs Claude Fable 5.1 at 49/100 (AA Index v5)
- Inference Latency: GPT-6 Astra is rated Deliberate vs Claude Fable 5.1 rated Moderate
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Frequently Asked Questions
Is GPT-6 Astra cheaper than Claude Fable 5.1?
GPT-6 Astra is significantly more economical, charging $10.00/1M input compared to $10.00/1M for Claude Fable 5.1.
Which model should I choose for production?
If your primary constraint is API cost and high query throughput, choose GPT-6 Astra. If you require peak reasoning accuracy and complex tool use, GPT-6 Astra is recommended.
How does context window compare between GPT-6 Astra and Claude Fable 5.1?
GPT-6 Astra offers the larger context window (1,050k tokens), allowing you to pass significantly larger codebases or documents without chunking.
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