Model guide / Anthropic

Claude Fable 5

current

Claude Fable 5 is Anthropic's most capable widely available model for ambitious coding, professional knowledge work, and long-running agents. Its one-million-token context and high autonomy target multi-hour or multi-day projects, but it is expensive and carries special safeguards and retention requirements.

Released Jun 9, 2026ProprietaryClosed weightsVerified Jul 20, 2026

Context window

1M tokens

Maximum output

128K tokens

Input price

$10 / 1M

Output price

$50 / 1M

Best for

  • Long-horizon coding and large migrations
  • Complex professional analysis and document-heavy work
  • Agents that plan, delegate, test, and self-correct
  • Vision-heavy workflows involving charts, tables, and interface screenshots

Not ideal for

  • Routine work that Sonnet 5 can handle more cheaply
  • Low-latency or high-volume chat
  • Sensitive use cases incompatible with 30-day safety retention
  • Cyber or life-science tasks likely to trigger fallback safeguards

Independent perspective

What experts are saying

this is something of a beast

After roughly five and a half hours of hands-on testing, Willison's defining impression was scale: Fable felt slow and expensive, but unusually hard to exhaust. His examples suggest the premium buys broad knowledge and persistence on multi-step coding work, not merely a small benchmark gain.

Simon Willison

Independent developer and creator of Datasette

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You can ask for anything—explainers, visualizers, dashboards, bespoke single-use apps.

Karpathy framed Fable's significance as demand expansion: when competent software becomes available on tap, developers create more tests, tools, dashboards, and one-off applications instead of merely producing the same software faster.

Andrej Karpathy

AI researcher and founder of Eureka Labs

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Claude Fable 5 is definitely the smartest model available to the general public

Lambert sees Fable 5 as the public capability leader, while warning that its unusual deployment controls make the product behavior as important to evaluate as the raw intelligence. For buyers, that means pairing capability tests with checks for consistency and policy-driven behavior changes.

Nathan Lambert

AI researcher and author of Interconnects

Read the source
PromptHQ evaluation

PromptHQ 100-word challenge

We give every model the same miniature science-fiction mystery. The response is short enough to read in one chat bubble, while the premise tests creative writing, compression, foreshadowing, causal coherence, and instruction following.

Claude Fable 5100 words

Write a science-fiction mystery in exactly 100 words.

A three-person research station on Europa receives a message from itself, timestamped 11 minutes in the future:

"Do not open the greenhouse door."

The door is opened anyway.

Plant two subtle clues. Give the warning a logical explanation without aliens, time travel, hallucinations, or simulations. The final sentence must change what the reader thought the warning meant.

Output only the story. Do not explain it.

Kaur read the message twice. Sender: Station Perimeter-Six. Recipient: the same. Timestamp eleven minutes ahead of the galley clock, though the greenhouse terminal had run fast since the power fault. Do not open the greenhouse door. Osei laughed nervously. Reyes said nothing; his chair had been empty since the pressure alarm. Kaur cranked the manual seal. The air shoved past her into frost and shredded stalks, toward Reyes, kneeling at the far wall, gloved palm pressed over a hissing breach. He hadn't warned them of something waiting inside. He had typed it one-handed, begging them not to break his seal.

Run the same prompt yourself

Open Claude Fable 5 in Multichat with the evaluation prompt prefilled.

Try this prompt

What the response revealed

Exact word countpass

The response contains exactly 100 whitespace-delimited words.

Constraint followingpass

It provides only the story and avoids every prohibited explanation.

Causal coherencepass

The fast greenhouse terminal explains the timestamp, while Reyes's improvised seal explains the warning and the consequence of opening the door.

Foreshadowingpass

The fast terminal, pressure alarm, and Reyes's empty chair all support the resolution.

Endingpass

The warning becomes Reyes's plea to preserve the seal rather than a warning about a threat inside.

Writingpass

The response creates character, tension, and a clear reversal in exactly 100 words.

The model receives the prompt without web access or external tools. Claude Fable 5 is run through Claude CLI at medium reasoning effort. We preserve the response as generated apart from display rendering.

Performance

Claude Fable 5 benchmarks

Benchmark scores are sensitive to reasoning effort, harness, tools, token budget, prompt format, sampling, and evaluation date. Scores here retain their source and should not be treated as directly interchangeable unless the underlying setup matches.

SWE-Marathon

24%

long-horizon software engineering · Anthropic

DeepSWE

70%

long-horizon coding · Anthropic

SWE-Bench Pro

80%

software engineering · Anthropic

Vibe Code Bench v1.1

90.4%

application building · Anthropic

Humanity's Last Exam

53.3%

academic reasoning · Anthropic

Family position

Claude 5 positioning

Fable sits above the Opus class in Anthropic's capability hierarchy. Opus 4.8 remains the strong flagship fallback, while Sonnet 5 offers much lower cost and higher throughput.

Claude Fable 5

This model

Highest general capability

$10 input

$50 output

Claude Opus 4.8

Complex coding and enterprise

$5 input

$25 output

Claude Sonnet 5

Speed and intelligence balance

$2 input

$10 output

API pricing

Per million text tokens

Input

$10

Cached input

$1

Cache write

$12.50

Output

$50

US-only inference costs 1.1x. Safety classifiers may route some cyber, biology, chemistry, or distillation requests to Opus 4.8; Anthropic says users are informed and charged the fallback model's price.

Where Fable 5 stands out

Built for demanding work

Long-horizon coding and large migrations are central to the model's positioning, rather than an incidental capability.

Long-context capacity

The published context window is 1,000,000 tokens, making the model a candidate for large documents, repositories, and sustained agent state.

Reasoning and tools

Reasoning is supported with low, medium, high, xhigh, max provider setting(s), and the model can participate in tool-using workflows through its available API surface.

Limitations to know

Benchmarks are configuration-sensitive

Scores can move substantially with the harness, tool access, effort setting, token budget, and evaluator. Treat the table as evidence, not a universal ranking.

Context size is not guaranteed recall

A large advertised window does not mean every detail is retrieved reliably at maximum length. Validate representative long-context workloads before deployment.

Product access differs from model capability

PromptHQ and gateway limits may expose fewer modalities, tools, or tokens than the provider's first-party API.

Capabilities and specifications

Knowledge cutoff

Jan 1, 2026

Inputs

text, image, PDF

Reasoning

Supported

Default effort

medium

Supported API features

Streaming
Function calling
Structured outputs
Web search
File search
Code interpreter
Computer use
MCP
Tool search
Web and file search low, medium, high effort

Frequently Asked Questions

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Available on PromptHQ Max