Claude Fable 5.1 vs GPT-6 Astra
Both are their lab’s most capable model, and both are slow and expensive. Fable 5.1 is the stronger pick for long agent work and hard open-ended reasoning; Astra leads on maths, science and operating software. For most teams the honest answer is neither — Opus 5.5 or GPT-6.1 Sol do most of the same work for much less.
Choose Claude Fable 5.1 if
- You run agents for hours that must plan, use tools and check their own work.
- Your problems are open-ended — Fable leads Humanity’s Last Exam, 65.0% to Astra’s 57.2%.
- You need to send PDFs directly; Astra reads text and images only.
Choose GPT-6 Astra if
- The work is heavy on maths or science — OpenAI reports 97.6% on FrontierMath Tier 4 and 96.0% on GPQA Diamond.
- Your agent operates software through its screen, where Astra posts 92.7% on ScreenSpot-Pro.
- You want fewer tokens per task — OpenAI says Astra uses about 70% fewer than GPT-5.6 Sol.
Side by side
| Claude Fable 5.1 | GPT-6 Astra | |
|---|---|---|
| Made by | Anthropic | OpenAI |
| Released | September 2026 | September 2026 |
| Input price, per 1M tokens | $11.50 | $11.50 |
| Output price, per 1M tokens | $57.50 | $57.50 |
| Cached input, per 1M tokens | $0.2875 | $1.15 |
| Batch (about half price) | Yes | Yes |
| Context window | 1M tokens | 1.05M tokens |
| Longest answer | 128K tokens | 128K tokens |
| Reads | Text, Images, PDFs | Text, Images |
| Thinks before answering | Always on | Always on |
| Open weights | No | No |
| Runs in Sri Lanka | No | No |
Prices are Roar AI’s published rates, live from our price list. Specs are each lab’s own published figures.
What a real job costs
Take 1,000 requests, each about 3,000 tokens in and 1,000 out — a long prompt and a page of answer.
The two cost about the same for this job. Models that think before answering spend extra output tokens doing it, so on hard prompts the real gap can be wider than the rates suggest.
Use both, switch any time
On Roar AI both are on the same key and the same monthly invoice, billed in rupees. Trying the other one is a one-word change:
client.chat.completions.create(model="claude-fable-5-1", …) client.chat.completions.create(model="gpt-6-astra", …)
Send the same prompts to both for a day and compare the answers on your own work — that settles it faster than any benchmark. Read the docs.