GPT-6 Astra vs Claude Fable 5.1 cost.
Two frontier models shipped days apart in September 2026 with the same headline price. The bill they produce is not the same.
- Input gap
- same
- Output gap
- same
- Cached gap
- 4×
- Rates verified
- 2026-10-02
Current-model note: Both models are current. Rates below come from the maintained catalogue and carry the date they were verified.
| Current model | Input / 1M | Cached / 1M | Cache write / 1M | Output / 1M | Context |
|---|---|---|---|---|---|
| GPT-6 AstraOfficial pricing ↗ | $10.00 | $1.00 | $12.50 | $50.00 | 1,050,000 |
| Claude Fable 5.1Official pricing ↗ | $10.00 | $0.25 | $12.50 | $50.00 | 1,000,000 |
Rates verified 2026-10-02. The browser may refresh them from the live catalogue. Provider pricing and tiers remain authoritative.
GPT-6 Astra: exact local BPE tokenization
Claude Fable 5.1: Provider-Calibrated UTF-8 Projection
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GPT-6 Astra: documented formula
Claude Fable 5.1: documented formula
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What five real workloads actually cost
Headline per-token prices rarely decide a bill. Request shape does. These figures are calculated from the verified rates for GPT-6 Astra and Claude Fable 5.1, including any long-context tier that applies once a request crosses its threshold.
| Workload | Input | Output | GPT-6 Astra | Claude Fable 5.1 | Difference |
|---|---|---|---|---|---|
| Short chat turn A typical assistant exchange. | 1,000 | 500 | $0.035 | $0.035 | about equal |
| RAG answer Five retrieved chunks plus a question. | 12,000 | 800 | $0.160 | $0.160 | about equal |
| Code review A medium pull request with surrounding files. | 60,000 | 2,000 | $0.700 | $0.700 | about equal |
| Whole-document analysis A long report or contract read in one call. | 300,000 | 4,000 | $6.30 (tier) | $3.20 | 1.97x cheaper on Claude Fable 5.1 |
| Full-context load Filling most of a one-million-token window. | 900,000 | 4,000 | $18.30 (tier) | $9.20 | 1.99x cheaper on Claude Fable 5.1 |
"(tier)" marks a request that crossed a long-context threshold, so it is billed above the headline rate. Output length is the assumption most worth changing for your own case: paste a real prompt into the calculator above to replace these with your numbers.
The cached-input rate is the number most comparisons miss
Agents, chat threads and RAG pipelines resend the same system prompt and context on every call. Those repeated tokens bill at the cached rate, not the headline rate, so a model can be cheaper on paper and more expensive in production. Here the gap is 4.0x: Claude Fable 5.1 reads cached tokens at $0.250 per million.
| Model | Fresh input / 1M | Cached input / 1M | Discount | Break-even |
|---|---|---|---|---|
| GPT-6 Astra | $10.00 | $1.00 | 90% | 1 read |
| Claude Fable 5.1 | $10.00 | $0.250 | 98% | 1 read |
Break-even assumes a cache write costs about 1.25x the base input rate, which is what OpenAI and Anthropic currently document for a short time-to-live. It answers one question: how many times a cached prefix must be re-read before caching is cheaper than paying full price each call. Above that count, every further read saves the discount shown.
Identical sticker price, different cached rate
Both models list $10 per million input tokens and $50 per million output tokens. On a one-off call there is nothing to choose between them on price.
The difference is the cached-input rate. GPT-6 Astra reads cached tokens at $1 per million; Claude Fable 5.1 reads them at $0.25 per million. That is a four-fold gap on exactly the tokens that repeat most: the system prompt, the tool definitions, and the retrieved context an agent resends on every turn.
For a single question this is irrelevant. For a twenty-turn agent loop carrying a 100,000-token prefix, the cached portion dominates the bill and the four-fold gap becomes the whole decision.
The 272,000-token cliff
GPT-6 Astra and the GPT-5.6 family apply a long-context tier once a request crosses 272,000 input tokens. Above that line OpenAI documents roughly double the input rate and 1.5 times the output rate, and it applies to the whole request, not just the tokens above the threshold.
Anthropic documents its one-million-token window at the standard per-token rate throughout. A 900,000-token request bills at the same rate per token as a 9,000-token one.
This is why the "Whole-document analysis" and "Full-context load" rows in the table below can reverse the ranking that the "Short chat turn" row suggests. If your workload regularly crosses 272,000 tokens, the tier is a larger effect than the headline price.
Benchmarks disagree, and token spend is why
Published comparisons do not agree on which model is stronger. OpenAI's own table puts GPT-6 Astra ahead on nearly every row. The independent evaluator Artificial Analysis first placed Astra well behind Claude Fable 5.1, then revised its index on 5 September after criticism that the test underrated Astra. After the revision Fable 5.1 still leads and Astra is second.
The more useful figure for a budget is cost per completed task, because a model that reasons in fewer tokens is cheaper even at the same rate. Artificial Analysis reports that Astra uses fewer tokens per task than any other frontier model, so at identical rates it tends to finish the same task for less.
Neither number tells you what your workload costs. Output length is the variable that decides it, and it is the one you can measure directly by pasting a real prompt into the calculator on this page.
Model the workload, not the marketing price.
The headline rates are identical, so the decision turns on cached input, long-context behaviour, and how many output tokens each model spends to finish the same task.
Review calculation methodology →Measure against the model you use
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Frequently asked questions
Are GPT-6 Astra and Claude Fable 5.1 the same price?+
The headline rates are identical at $10 per million input tokens and $50 per million output tokens. They differ on cached input, where GPT-6 Astra charges $1 per million and Claude Fable 5.1 charges $0.25 per million, and on long-context billing, where GPT-6 Astra applies a higher tier above 272,000 input tokens.
Which is cheaper for an agent that reuses a long system prompt?+
Claude Fable 5.1, in most cases. Repeated prefix tokens bill at the cached rate, and its cached rate is four times lower. The advantage grows with the number of turns and the size of the reused prefix.
What happens above 272,000 input tokens on GPT-6 Astra?+
OpenAI applies a long-context tier that raises the rate for the entire request, not only the tokens above the threshold. Anthropic bills its full one-million-token window at the standard rate, so the two diverge sharply on very large single calls.
Does this calculator send my prompt to either provider?+
No. Text, document contents, and image pixels are processed in your browser. Only public model metadata may be refreshed from a pricing catalogue.