# Claude Fable 5.1 vs GPT-6 Astra: rates, caching and fit

URL: https://infro.io/blog/claude-fable-5-1-vs-gpt-6-astra
Author: INFRO Editorial (https://infro.io/blog/editorial)
Publisher: INFRO
Published: 2026-09-29
Last modified: 2026-09-29
Last reviewed: 2026-09-29 (next review due 2026-10-29)
Topic: Evaluation and models
Format: Comparison

> Claude Fable 5.1 and GPT-6 Astra both list $10 per million input tokens and $50 per million output tokens, both allow 128K output, and both batch at $5 and $25. They differ in cache reads ($0.25 against $1.00 per million), in long prompts (Astra bills 2x input and 1.5x output above 272K input tokens; Fable 5.1 keeps its standard rate across 1M) and in reasoning controls, so compare them on your own traffic, by cost per successful task.

Key takeaways:
- Identical standard rates hide a fourfold difference in cache-read pricing: $0.25 per million on Fable 5.1, $1.00 on Astra.
- Above 272K input tokens Astra bills the whole request at 2x input and 1.5x output; Fable 5.1 bills its full 1M window at the standard rate.
- Reasoning is controlled differently: adaptive thinking with a default effort of high on Fable 5.1, explicit effort levels from low to max on Astra.
- The rate card cannot tell you which model finishes your task in fewer tokens and fewer attempts; only your own evaluation can.
- Putting both behind one route lets a comparison, a migration or a fallback be a configuration change rather than a deploy.

On a price list, Claude Fable 5.1 and GPT-6 Astra look like the same product: $10 per million input tokens and $50 per million output tokens, from both Anthropic and OpenAI[1][2]. They are not the same product, and the bill for the same workload can differ substantially between them. The differences sit in the details a headline rate leaves out: cached input, long prompts, output behaviour and where the request is processed.

This comparison uses only what the two vendors publish, read on 2026-09-29. It does not say which model is better; it shows where the published differences are, and how to measure the rest on your own work.

## What each vendor says the model is for

Anthropic positions Claude Fable 5.1 for demanding reasoning and long-horizon agentic work, and suggests it when evaluations on Claude Opus 5.5 at higher effort still fall short[3]. OpenAI describes GPT-6 Astra as its most capable model, built for the hardest end-to-end work[4]. Both are the top of their current lineups; both vendors also sell smaller models for work that does not need the top tier, which is worth remembering before either becomes the default for everything.

## The rate card, side by side

[Diagram: Standard-tier list rates per million tokens, from each vendor's pricing page on 2026-09-29.]

The two lines that differ are cached input. Anthropic bills cache hits on Fable 5.1 at 0.025x the input rate, $0.25 per million, where most Claude models use 0.1x[1]. OpenAI lists Astra's cached input at $1.00 per million[2]. Cache writes are close: $12.50 per million for Anthropic's 5-minute cache and $20 for its 1-hour cache, against $12.50 for Astra[1][2].

Batch processing is identical on paper. Anthropic's batch rate for Fable 5.1 is $5 input and $25 output[1]; OpenAI's batch and flex rates for Astra are the same $5 and $25[2]. OpenAI also lists a fast mode for Astra at $20 input and $100 output[2].

The catalog entries INFRO estimates against are on the [Claude Fable 5.1](https://infro.io/models/anthropic/claude-fable-5.1) and [GPT-6 Astra](https://infro.io/models/openai/gpt-6-astra) model pages.

## Why cache reads decide agent bills

An agent or a long conversation resends a stable prefix — system prompt, tool definitions, earlier turns — on every call. When most input tokens are cache hits, the cached rate is effectively the input rate.

[Diagram: Illustrative. One request with a 50K-token prompt of which 45K is a cache hit, and 1.5K output tokens, priced at each vendor's published rates. Cache writes are excluded.]

> **Example: The same agent turn on both rate cards**
> Illustrative arithmetic on published rates. The prompt is 50,000 tokens, 45,000 of them read from cache, and the answer is 1,500 tokens.
> 
> Fable 5.1: 5,000 × $10 + 45,000 × $0.25 + 1,500 × $50, per million = $0.05 + $0.01125 + $0.075 = $0.136.
> 
> Astra: 5,000 × $10 + 45,000 × $1.00 + 1,500 × $50, per million = $0.05 + $0.045 + $0.075 = $0.170.
> 
> Over 100,000 such turns that is $13,625 against $17,000 — if both models produce the same output length and succeed at the same rate, which is exactly what you have to measure.

The example also shows how easily the conclusion flips. If one model answers in 1,000 tokens and the other in 2,000, the output line dominates. If one needs a retry on one task in ten, that dominates too. The cache-read gap is real, but it is one term in the equation.

## Long prompts: 272K is a threshold for Astra only

OpenAI bills Astra requests with more than 272K input tokens at 2x input and cache rates and 1.5x output, for the full request[2]. Anthropic states that Claude 4.6 and later models include the full 1M window at standard pricing[1].

> **Example: A 400K-token prompt**
> Illustrative. 400,000 input tokens, no cache, 4,000 output tokens. Fable 5.1: 0.4 × $10 + 0.004 × $50 = $4.20. Astra above the threshold: 0.4 × $20 + 0.004 × $75 = $8.30. Under 272K the two are identical per token.

If your workload has a long tail of very large prompts — whole repositories, long contracts, full ticket histories — measure how often you cross 272K before you compare.

## Limits, reasoning controls and cutoffs

[Diagram: Published limits, controls and options for both models, read 2026-09-29.]

- **Context and output.** Fable 5.1 has a 1M-token window and 128K output[3]. Astra has a 1.05M window, a 922K input maximum and 128K output[5].
- **Reasoning.** Fable 5.1 uses adaptive thinking, always on, with a default effort of `high`[3]. Astra exposes effort levels `low`, `medium`, `high`, `xhigh` and `max`[4]. Effort changes how many tokens a request consumes, so compare at the effort you would actually run.
- **Structured outputs and tools.** Anthropic lists Fable 5.1 among the models with generally available structured outputs[6], and all current Claude models support tool use[3]. OpenAI lists structured outputs, function calling and prompt caching for Astra, on Chat Completions, Responses and Batch[5].
- **Knowledge.** Fable 5.1's reliable knowledge cutoff is June 2026[3]; Astra's is April 30, 2026[5].
- **Lifecycle.** Anthropic commits not to retire Fable 5.1 sooner than September 1, 2027 on its own platforms[3]. OpenAI's model page lists a single `gpt-6-astra` snapshot[5].

## Tokens are not counted the same way

A per-token rate compares cleanly only if both vendors count the same tokens for the same text, and they do not. Each vendor uses its own tokenizer. Anthropic notes that Claude 4.7 and later models use a tokenizer that produces approximately 30% more tokens for the same text than its earlier one, with the exact increase depending on content[1]. That statement compares Anthropic's tokenizers with each other, not with OpenAI's, but it makes the general point: the same prompt is a different number of billable tokens on each side.

So compare measured usage, not converted estimates. Send the same prompts to both models and record `prompt_tokens` and `completion_tokens` from each response. Two identical rate cards applied to different token counts produce different bills, and for a long, stable system prompt the difference repeats on every call.

## Location and cloud options

Anthropic applies a 1.1x multiplier to every token category when you pin inference to the US with `inference_geo: "us"`[1]. OpenAI charges a 10% uplift on regional processing endpoints for models released on or after March 5, 2026[2]. Anthropic also lists Fable 5.1 ids for Amazon Bedrock, Google Cloud and Microsoft Foundry[3]; partner clouds bill at their own rates, and Anthropic notes that regional endpoints there carry a 10% premium over global ones[1]. If a customer contract dictates where prompts are processed, compare the rates you would actually pay under that constraint.

## Deciding on your own traffic

The published facts narrow the question; they do not answer it. Decide in four steps:

1. **Pick the task set.** Real inputs from the workload in question, each with a check: a schema, a test suite, a reference answer, or a reviewer's pass/fail.
2. **Run both models on the same inputs**, at the effort level you would ship, with prompts structured so the stable prefix is cacheable on both.
3. **Record cost and outcome per request**, including retries and cache writes.
4. **Compare cost per successful task**, not cost per token. If both pass at similar rates, the lower cost per success wins that task tier; if only one passes, the rate card is irrelevant.

Revisit the decision when either vendor ships a new snapshot or changes a rate; both happen on a schedule you do not control.

## Comparing Fable 5.1 and Astra through INFRO

Connect your Anthropic and OpenAI accounts as [provider connections](https://infro.io/docs/providers); each provider bills its own account directly, at your rates. Then run the task set against both catalog ids, `anthropic/claude-fable-5.1` and `openai/gpt-6-astra`, through the OpenAI-compatible endpoint, with a `workflow` or `feature` value in `metadata` so the run can be isolated later.

For Anthropic, mark the end of the stable prefix with `cache_control` on a text content part; INFRO keeps it when it translates the request for Anthropic ([prompt caching](https://infro.io/docs/features/prompt-caching)). Each response then reports `usage.prompt_tokens_details.cached_tokens` and `usage.cost`, the estimated provider cost including the cached rate. If you want to see whether caching is working on both sides, those two fields answer it per request.

```bash
curl "https://api.infro.io/v1/usage?group_by=model&from=2026-10-01T00:00:00Z&to=2026-10-03T00:00:00Z" \
  -H "Authorization: Bearer $INFRO_API_KEY"
```

Each bucket carries `requests`, `cost`, `unpriced_requests`, `error_rate`, `p50_ms` and `p95_ms` ([usage analytics](https://infro.io/docs/observability/analytics)). Join the request ids in your harness to your pass/fail results to get cost per successful task. Two cautions: enter any negotiated rate under Prices so the estimate reflects what you pay ([billing](https://infro.io/docs/org/billing)), and remember that the catalog carries one rate per model, so an Astra request above 272K input tokens is estimated at the base rate and reads low.

After the decision, a [route](https://infro.io/docs/routes) such as `research-agent` can list the chosen model first and the other second. If the first fails before any response has started, the request moves to the second, and the trace records both attempts ([failover](https://infro.io/docs/features/fallbacks)). That fallback changes behaviour, not just infrastructure, so only list a model you have evaluated on the task.

## Questions

### Does Claude Fable 5.1 charge more for prompts over 200K tokens?

Anthropic's pricing page says Claude 4.6 and later models include the full 1M-token context window at standard pricing, so a 900K-token request is billed at the same per-token rate as a 9K-token one. Check the page again before relying on it, since pricing pages change.

### Can I run Claude Fable 5.1 on AWS or Google Cloud instead of Anthropic's API?

Anthropic lists Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS model ids for Fable 5.1. Partner clouds set their own prices and lifecycle dates, so read the cloud provider's pricing page for the rate that account will bill.

## Sources

1. Pricing — Anthropic. https://platform.claude.com/docs/en/about-claude/pricing (accessed 2026-09-29)
2. Pricing — OpenAI. https://developers.openai.com/api/docs/pricing (accessed 2026-09-29)
3. Models overview — Anthropic. https://platform.claude.com/docs/en/about-claude/models/overview (accessed 2026-09-29)
4. Models — OpenAI. https://developers.openai.com/api/docs/models (accessed 2026-09-29)
5. GPT-6 Astra — OpenAI. https://developers.openai.com/api/docs/models/gpt-6-astra (accessed 2026-09-29)
6. Structured outputs — Anthropic. https://platform.claude.com/docs/en/build-with-claude/structured-outputs (accessed 2026-09-29)