Claude Fable 5.1 vs GPT-6 Astra

Claude Fable 5.1 and GPT-6 Astra carry the same sticker price — $10 per million input tokens, $50 per million output. The differences that will actually show up in your bill and your codebase are elsewhere: Fable 5.1 reads from cache at $0.25 per million tokens against Astra's $1.00, Astra re-prices your entire request once input crosses 272,000 tokens, and three API calls that work on Astra return a 400 on Fable 5.1.

Anthropic shipped Fable 5.1 on 1 September 2026. OpenAI began rolling out GPT-6 Astra on 3 September. SWARECO builds and runs engineering teams on both, so this is a comparison of what changes when you build on them — not a benchmark table.

This is the API comparison. If you are choosing a chat assistant rather than an AI model to build on, our assistant comparison covers that question instead, and getting better results out of Claude Fable covers prompting once you have chosen.

GPT-6 Astra vs Claude Fable 5.1: the specifications

	Claude Fable 5.1	GPT-6 Astra
API model ID	claude-fable-5-1	gpt-6-astra
Released	1 September 2026	3 September 2026
Context window	1M tokens (also the default)	~1.05M tokens
Max output tokens	128K	128K
Input / output	$10 / $50 per 1M, flat to 1M	$10 / $50 per 1M, to 272K
Cache reads	$0.25 per 1M	$1.00 per 1M
Long-context tier	None	Above 272K: $20 / $75, cache $2.00
Batch discount	No Priority Tier support	Batch and Flex: 50% off
Forced tool use	Returns 400	Supported via tool_choice
Effort control	output_config.effort, low–max	reasoning_effort
Typical harness	Claude Code	Codex
Knowledge cutoff	Not published for 5.1	April 2026

For context on where Fable 5.1 sits in Anthropic's own lineup: Claude Opus 5 (claude-opus-5) shares the 1M context window but costs $5 input and $25 output — half Fable 5.1's rate. Fable 5.1 is the tier above it, and the price difference is the question worth asking before you reach for the top of the range.

The sticker price is identical. The cache price is not.

Fable 5.1's headline commercial change is a 75% cut to cache reads, from $1.00 to $0.25 per million tokens. Anthropic measures that as roughly 25% lower cost on typical workloads and up to 45% on agentic ones. GPT-6 Astra reads cache at $1.00 per million — the price Anthropic just moved away from.

That gap matters more than it looks, because of where tokens accumulate in agentic coding. A long-horizon loop re-sends its whole history every turn. A 40-turn session with a 100,000-token working context reads that context roughly 40 times. Those are cache reads, not fresh input, so the cache rate — not the input rate — sets the bill. At a 4× difference, the same loop costs materially less on Fable 5.1 even though both models advertise $10 input.

Astra answers this differently: Batch and Flex pricing cut its standard rates by 50%, to $5 input and $25 output. Where your workload tolerates asynchronous processing, that discount is larger than the cache advantage. Where it does not — interactive agents, anything user-facing — it is unavailable to you.

Astra's 272K cliff re-prices the whole request

This is the single most expensive detail to miss. GPT-6 Astra advertises a context window above a million tokens, but crossing 272,000 input tokens re-prices the entire request at 2× the input and cache rates and 1.5× the output rate — not just the tokens above the threshold.

GPT-6 Astra rate	Up to 272K input	Above 272K input
Input	$10 / 1M	$20 / 1M
Cached input	$1.00 / 1M	$2.00 / 1M
Cache writes	$12.50 / 1M	$25.00 / 1M
Output	$50 / 1M	$75 / 1M

A request at 271,000 tokens and one at 273,000 tokens differ in unit price by a factor of two across every input token in the call. Claude Fable 5.1 has no equivalent tier — $10 and $50 hold from the first token to the millionth.

So the crossover between these two models is a function of context length, not of token price. Below 272K, both charge the same for fresh input and Fable 5.1 charges a quarter as much for cached reads. Above 272K, Astra doubles and Fable 5.1 does not. If your real-world workload lives in long contexts — whole-repository reasoning, large document sets, long agent sessions — that threshold is the comparison.

What the independent index says

Third-party benchmarking from Artificial Analysis puts Fable 5.1 ahead on capability and Astra ahead on cost:

Artificial Analysis metric	Claude Fable 5.1	GPT-6 Astra
Intelligence Index	66 (max, with fallback)	61
Coding Agent Index	70 (in Claude Code, leads the index)	67

Fable 5.1 leads both indices. On cost per task for coding, Artificial Analysis reports Astra at "less than half the cost of Claude Fable 5, for the same score" — and that comparison deserves a caveat most write-ups skip. It is measured against Claude Fable 5, not 5.1. Since 5.1 cut cache reads by 75% in the same release, a cost-per-task figure benchmarked on the previous model understates Fable 5.1's position by exactly the amount that changed. Treat it as directional, not current.

On token efficiency, Astra uses about one third of the tokens of GPT-5.6 Sol at max effort — a real gain, but a comparison against its own predecessor rather than against Fable 5.1. No published like-for-like token-efficiency figure between these two models exists yet.

What breaks when you port code between them

These are not preferences; they are 400 errors. Anything that works on Astra and relies on the following will fail on Fable 5.1:

• Forced tool use. tool_choice: {type: "any"} and {type: "tool", name: ...} return a 400 on Fable 5.1 — including on count_tokens and Batches. Astra supports tool_choice normally. The replacement is {type: "auto"} plus an explicit instruction naming the tool, strict: true to keep arguments schema-valid, or structured outputs when the forced call only existed to get JSON back.
• Assistant prefill. Prefilling the last assistant turn returns a 400. Use structured outputs or a system-prompt instruction to control response shape.
• Fixed thinking budgets. thinking: {type: "enabled", budget_tokens: N} returns a 400, and so does {type: "disabled"} — thinking is always on. Depth is controlled with output_config.effort, which accepts low through xhigh and max. Astra exposes reasoning_effort instead.
• Zero data retention. Fable 5.1 is not available under ZDR unless Anthropic expressly authorises it; an org whose retention configuration does not meet the 30-day requirement gets 400 invalid_request_error. This is a procurement conversation, not a code change, and it is the constraint most likely to stop a regulated team outright.
• Priority Tier. Not supported on Fable 5.1 — a Priority Tier request naming it fails validation.

Two more behaviours matter for agent harnesses. Fable 5.1 never returns its raw chain of thought; thinking blocks come back with an empty string by default, and you must set display: "summarized" for a readable summary. And under preserved thinking, editing an earlier turn invalidates thinking blocks — accounts created on or after 31 August 2026 get a 400 on edited history. Harnesses that rewrite conversation history to save tokens need to become append-only.

The coding benchmarks — and the one that is misattributed

The 95% SWE-bench Verified figure circulating alongside Fable 5.1 is Fable 5's score, not 5.1's. Anthropic did not headline a SWE-bench Verified number for 5.1. The software-engineering figure it does report is SWE-bench Pro at 81.2. If you are building a model-selection case on that 95%, you are citing the previous model.

What Anthropic did publish for Fable 5.1 is weighted toward agentic and scientific work:

Benchmark	Claude Fable 5	Claude Fable 5.1
Terminal-Bench-Science 0.1	24.7%	52.6%
Terminal Bench 4.0	42.0%	55.8%
SWE-bench Pro	—	81.2
ProofBench v1.1	—	100%
CursorBench 3.2.0	—	73.4%
Humanity's Last Exam	—	60.9% / 65.0% with tools
OSWorld 2.0 (strict)	—	41.7%

The Terminal-Bench-Science jump — 24.7% to 52.6%, better than double — is the number that best characterises what changed between the two Fable releases. OpenAI has not published a comparable scorecard for Astra on these specific benchmarks, which is why a like-for-like table is not available and any post presenting one should be treated with suspicion.

Claude Mythos 5.1 is the same model behind different safeguards

Anthropic released Claude Mythos 5.1 (claude-mythos-5-1) alongside Fable 5.1, restricted to vetted organisations under Project Glasswing. It shares Fable 5.1's pricing, context window and API surface, and scores higher on Terminal Bench 4.0 — 60.9% against Fable 5.1's 55.8% — a gap Anthropic attributes to the cost of safeguard interventions rather than to a difference in the underlying model.

That figure is worth knowing even if you cannot access Mythos: it puts a number on what safety classifiers cost in agentic throughput. It is also why Fable 5.1 code must handle stop_reason: "refusal", which arrives as an HTTP 200 with a stop_details category. Check stop_reason before reading content, and consider the server-side fallbacks parameter so a refusal routes to another model instead of failing.

How to choose between them

These two models are closely matched on headline capability and identically priced at the sticker, so the decision is made by operational details rather than by a leaderboard. SWARECO applies four tests:

1. How long is your context, typically? If requests routinely exceed 272K input tokens, Astra's long-context tier doubles your input cost and Fable 5.1 does not. This is the largest single cost difference between them.
2. Is the workload agentic or batchable? Agentic coding loops re-read context constantly and favour Fable 5.1's $0.25 cache reads. Work that tolerates asynchronous processing favours Astra's 50% Batch and Flex discount, the bigger lever where it applies.
3. Does your harness force tool calls or edit history? Both are Fable 5.1 incompatibilities needing real refactoring, not a config change.
4. What is your data-retention posture? Zero data retention rules out Fable 5.1 without express authorisation from Anthropic. Establish this before benchmarking anything.

Run the comparison on your own traffic before committing. Both vendors publish list prices; neither publishes your cache-hit rate, and that is the variable deciding which is cheaper for you.

Frequently asked questions

Is GPT-6 Astra cheaper than Claude Fable 5.1?

At the sticker price they are identical — $10 per million input tokens and $50 per million output. Fable 5.1 is cheaper on cache reads ($0.25 versus $1.00 per million) and on any request above 272K input tokens, where Astra re-prices the whole request at 2× input and 1.5× output. Astra is cheaper for batchable work, where Batch and Flex pricing cut its rates by 50%, and Artificial Analysis reports a lower cost per coding task — though measured against Claude Fable 5 rather than 5.1.

What is the context window of each AI model?

Claude Fable 5.1 has a 1M-token context window, which is also its default, and 128K maximum output tokens. GPT-6 Astra has roughly 1.05M tokens of context and the same 128K output cap — but pricing changes above 272K input tokens.

Which scores higher on coding benchmarks?

On the Artificial Analysis Coding Agent Index, Claude Fable 5.1 in Claude Code scores 70 and leads the index, against 67 for GPT-6 Astra. On the Artificial Analysis Intelligence Index it is 66 to 61, also in Fable 5.1's favour.

Did Claude Fable 5.1 score 95% on SWE-bench Verified?

No. That figure belongs to Claude Fable 5. Anthropic did not headline a SWE-bench Verified score for Fable 5.1; the software-engineering benchmark it reports for 5.1 is SWE-bench Pro at 81.2.

Can I move an existing coding agent from GPT-6 Astra to Claude Fable 5.1 without code changes?

Usually not. Forced tool use, assistant prefill, and fixed budget_tokens thinking configurations all return 400 errors on Fable 5.1. Harnesses that edit earlier conversation turns also need to become append-only because of preserved thinking.

What is the difference between Claude Fable 5.1 and Claude Mythos 5.1?

They are the same underlying model behind different safeguard layers. Fable 5.1 is generally available; Mythos 5.1 is restricted to vetted organisations. Mythos 5.1 scores 60.9% on Terminal Bench 4.0 against Fable 5.1's 55.8%, a difference Anthropic attributes to safeguard interventions.

The short version

Both AI models cost the same to start, and Fable 5.1's lead on the independent indices is real but narrow. The decisions are operational: Fable 5.1 if your contexts are long or your loops are agentic, because of flat pricing to 1M and $0.25 cache reads; Astra if your work batches, because a 50% discount beats a 4× cache advantage when you can wait. Check your retention requirements and your tool_choice usage before migrating either way — those are the two things that turn a model swap into a project.