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GPT-6 Astra vs Claude Fable 5.1: Same $10 / $50 List Price, Different Bill

A pricing comparison finds that GPT-6 Astra and Claude Fable 5.1 both list at $10 per million input tokens and $50 per million output tokens, so the real cost difference comes from cache billing: Fable 5.1 charges $0.25 per million cached-read tokens versus $1 for GPT-6 Astra, but bills cache writes separately at $12.50 (5-minute) or $20 (1-hour) while OpenAI has not published a cache-write price for GPT-6. The break-even works out to roughly 16.7 reuses of the same prefix, and the analysis notes that protocol lock-in (OpenAI Responses/Chat Completions and Codex CLI versus Anthropic Messages and Claude Code) plus Fable 5.1's published 1M context window and phased GPT-6 rollout often matter more than the price gap.

by read5 min views2 publishedOct 30, 2026

The short answer: GPT-6 Astra and Claude Fable 5.1 list at exactly the same input and output price — $10 and $50 per million tokens. So "which is more expensive" is the wrong question. Three things actually decide your bill. First, the cache pricing model: Fable 5.1 charges $0.25 for a cache hit (2.5% of input price) against GPT-6 Astra's $1 (10%), but Anthropic bills cache writes separately while OpenAI has not published a cache write price for GPT-6 — the break-even lands at roughly seventeen reuses of the same prefix, and the arithmetic is below. Second, protocol and ecosystem: Fable speaks Anthropic Messages and is native to Claude Code, GPT-6 speaks OpenAI Responses and Chat Completions and is native to Codex CLI, and for an existing codebase the migration cost usually exceeds the capability gap. Third, spec transparency and availability: Fable 5.1 has a published 1M context window and 128K max output and is fully rolled out, while OpenAI has not published a context window for GPT-6 Astra and is still releasing it in phases.

Per 1M tokens GPT-6 Astra Claude Fable 5.1
Input $10 $10
Output $50 $50
| Cache read | $1 (10% of input) | **$0.25** (2.5% of input) | 
| Cache write | Not published | $12.50 (5 min) / $20 (1 hour) | 

Both are served at vendor list price; full pricing is on the pricing page. Look at the last two rows: those are two genuinely different philosophies of cache billing, and they are the only source of difference in your bill.

Anthropic's model is "expensive write, very cheap read." OpenAI's is "moderate read, write price unpublished." Comparing only what has been published, let N be the number of times the same prefix is reused:

Setting them equal gives N = 12.50 ÷ (1.00 − 0.25) ≈ 16.7. In other words: Concretely: a 15,000-token prefix of system prompt plus tool definitions, reused twenty times in one session. Fable 5.1 costs $0.1875 to write plus $0.075 to read, or $0.2625. GPT-6 Astra costs $0.300 in reads, and its write price is still unpublished, so the real figure may be higher. The gap is small, but the direction is clear: the longer the prefix and the more it is reused, the more Anthropic's cache model wins.

One caveat worth stating plainly: this is arithmetic on published prices, not a substitute for your own bill. Real hit rates depend on prefix stability, TTL and concurrent routing, and estimates routinely miss. HopBase's usage records break out input, cached-hit and output cost per request, so a week of real traffic beats any formula. For raising hit rates in the first place, see the prompt cache cost guide.

GPT-6 Astra Claude Fable 5.1
Protocol OpenAI Responses / Chat Completions Anthropic Messages ( /v1/messages )
Native CLI Codex CLI Claude Code
SDK OpenAI SDK, unchanged Anthropic SDK, unchanged

If your service is already built on one of these SDKs, moving to the other means reworking request construction, streaming event parsing, tool-call format and error handling. That work is usually worth more than the capability difference between the two models. Price the migration before you argue about the model. Fable 5.1 also carries a few behavioural changes worth knowing when migrating from older Claude versions: forced tool calls are gone (any and tool values of tool_choice return 400), thinking blocks are bound to the model and must be replayed verbatim, and assistant prefill is unsupported. Details are in the Fable 5.1 launch post.

output_config.effort), generally available; check GET /v1/models for your key.

For anything that has to go into a technical proposal with a concrete capacity commitment, that difference is real: one has an official number you can cite, the other currently only supports "whatever the API returns." Both models are served at list price on HopBase and callable from a single key, which makes an A/B on real traffic straightforward. The split-test method is the three-step checklist in our GPT-6 versus GPT-5.6 comparison.

Their input and output list prices are identical at $10 and $50 per million tokens. The difference is entirely in caching: Fable 5.1 charges $0.25 per cache hit and $12.50 (5 min) or $20 (1 hour) per write; GPT-6 Astra charges $1 per hit and has not published a write price. Which total is lower depends on how often you reuse a prefix.

With N reuses of a prefix, Fable 5.1 costs 12.50 + 0.25N per million tokens on the five-minute tier, and GPT-6 Astra costs 1.00N. They are equal at N ≈ 16.7, so beyond about seventeen reuses Fable is cheaper. On the one-hour cache tier the threshold is about twenty-seven.

Yes. Both are served at list price on HopBase and callable with one enterprise key subject to group permissions, which makes splitting real traffic for an A/B straightforward. Note the protocols differ: Fable uses /v1/messages, GPT-6 uses /v1/responses or /v1/chat/completions.

OpenAI has not published an official figure as of this writing, and we do not label specs a vendor has not confirmed. Treat the metadata from GET /v1/models and your actual results as authoritative. Claude Fable 5.1 does have a published 1M context window and 128K maximum output.

Check the model field echoed in the response body and run a fixed probe set as a side-by-side comparison. The full method is in the API fidelity self-test.

For enterprise evaluation and cross-vendor selection testing, talk to an advisor; integration steps are in the documentation.

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