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`gpt-6-astra`vs`claude-fable-5-1`

Both are available on APICLAN through one OpenAI-compatible endpoint, so switching between them is a one-word change. Here is what each actually costs you, and how much the difference is worth at your volume.

gpt-6-astra

Input / 1M$2.32

Output / 1M$10.31

List price ~~$11.62 / $51.56~~

claude-fable-5-1

Input / 1M$2.00

Output / 1M$10.00

List price ~~$10.00 / $50.00~~

On a like-for-like token mix, claude-fable-5-1 runs about 5% cheaper than gpt-6-astra. Both models are from different families.

What the difference is worth to you

Percentages are easy to shrug off. Put your real monthly volume in and see the number in dollars.

Input tokens / month

Output tokens / month

gpt-6-astra

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claude-fable-5-1

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You keep

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Both $10 / $50 at list — and then cache reads differ by four times

The headline numbers are identical: $10.00 per million input, $50.00 per million output. Anyone comparing on those two figures alone would call it a draw.

The cache rates are not close. A cache read costs $1.00 per million on gpt-6-astra (the usual tenth of the input price) and $0.25 on claude-fable-5-1, which uses a 0.025× multiplier instead of 0.1×. For anything that re-reads a large stable prefix — a document, a codebase, a long system prompt — that is the number that decides the invoice.

An agent loop that reads a 200,000-token cached prefix on each of 50 turns, at list:

gpt-6-astra: 50 × 200,000 × $1.00 per million — $10.00 in cache reads alone
claude-fable-5-1: 50 × 200,000 × $0.25 per million — $2.50

Long prompts push the gap further. Astra reprices above 272,000 input tokens to $20.00 / $75.00 for the whole request; Claude 4.6 and later carry the full context at standard price with no tier. A 300k-token request costs $6.15 on Astra at list and $3.10 on Fable 5.1.

Those are list prices, and the two models reach this platform through different upstream groups, so the rates you actually pay here are not in the same ratio — the table above shows both. Cache behaviour is the part that carries over regardless.

Which one should you actually use?

We do not publish capability benchmarks, and you should be sceptical of anyone who does. Scores on public benchmarks rarely predict how a model behaves on your codebase, your prompts and your edge cases.

What we can tell you is exactly what each one costs. Run both against a real task from your own workload — switching takes one word — and let the cheaper one win unless it visibly fails.

A practical approach that works well in agent loops: route the bulk of calls to the cheaper model, and escalate to the more expensive one only when the first attempt fails a check. Most workloads are dominated by routine calls, so the blended cost lands close to the cheap model rather than the expensive one.

Switching between them

Same endpoint, same key, same request shape. Only the model string changes.

-    model="gpt-6-astra",
+    model="claude-fable-5-1",

If you have not connected yet, the quickstart takes about thirty seconds — you change your base URL and nothing else.

Full pricing for each

gpt-6-astra pricing → claude-fable-5-1 pricing →

Prices verified 2026-10-05 and regenerated automatically from live billing data. List prices change; this page follows them.