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

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 OpenAI¹². 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 short³. OpenAI describes GPT-6 Astra as its most capable model, built for the hardest end-to-end work⁴. 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

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 0.1x¹. OpenAI lists Astra's cached input at $1.00 per million². 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 Astra¹².

Batch processing is identical on paper. Anthropic's batch rate for Fable 5.1 is $5 input and $25 output¹; OpenAI's batch and flex rates for Astra are the same $5 and $25². OpenAI also lists a fast mode for Astra at $20 input and $100 output².

The catalog entries INFRO estimates against are on the Claude Fable 5.1 and 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.

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 request². Anthropic states that Claude 4.6 and later models include the full 1M window at standard pricing¹.

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

• Context and output. Fable 5.1 has a 1M-token window and 128K output³. Astra has a 1.05M window, a 922K input maximum and 128K output⁵.
• Reasoning. Fable 5.1 uses adaptive thinking, always on, with a default effort of high³. Astra exposes effort levels low, medium, high, xhigh and max⁴. 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 outputs⁶, and all current Claude models support tool use³. OpenAI lists structured outputs, function calling and prompt caching for Astra, on Chat Completions, Responses and Batch⁵.
• Knowledge. Fable 5.1's reliable knowledge cutoff is June 2026³; Astra's is April 30, 2026⁵.
• Lifecycle. Anthropic commits not to retire Fable 5.1 sooner than September 1, 2027 on its own platforms³. OpenAI's model page lists a single gpt-6-astra snapshot⁵.

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 content¹. 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, 2026². Anthropic also lists Fable 5.1 ids for Amazon Bedrock, Google Cloud and Microsoft Foundry³; partner clouds bill at their own rates, and Anthropic notes that regional endpoints there carry a 10% premium over global ones¹. 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; 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). 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.

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). 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), 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 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). That fallback changes behaviour, not just infrastructure, so only list a model you have evaluated on the task.