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Text · Released Nov 2026

Anthropic Claude Fable 5.2

Anthropic's Fable model for the most demanding reasoning and long-horizon agentic work, improving on Fable 5.1 in coding, reasoning and vision at a lower price, with always-on adaptive thinking and a 1M-token context window.

Claude Fable 5.2 is a cloud text model from Anthropic. It is multimodal: alongside a text prompt it accepts images, then replies with generated text. Its context window handles up to 1M input tokens and up to 128K output tokens, and it supports adjustable reasoning effort for harder problems. Generation can be tuned with system instructions. It runs through Anthropic, OpenRouter, and Replicate using your own API key, from $8.00 per million input tokens.

Modality
Text
Model ID
anthropic/claude-fable-5.2
Specs
Released
Nov 2026
Pricing
$8.00 / 1M in · $40.00 / 1M out
Inputs
Text, Images
Context window
1M in · 128K out
Reasoning
Adjustable effort
Controls
System prompt
Real outputs

Samples

Generated with Claude Fable 5.2 via Replicate. The same three prompts run against every text model in the catalog, shown verbatim, so the only thing that changes between two models’ answers is the model.

Short answer49 / 50 words

Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.

An API is a set of rules that lets one piece of software ask another for information or services. Think of a wall socket: you don't need to understand the power plant, you just plug in. Similarly, a weather app plugs into a weather service's API to fetch forecasts.

Medium answer147 / 150 words

A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.

Use a hosted provider. With two people, every hour spent on password resets, session handling, multi-factor authentication, and breach response is an hour not spent on the product that makes you different. Authentication is a solved problem where mistakes are catastrophic and success is invisible to customers. A provider gives you tested security, social logins, and compliance paperwork on day one, usually free or cheap at your scale. The strongest argument against this is lock-in at the most sensitive layer of your system. Your users' identities and credentials live in someone else's infrastructure, under their pricing and their roadmap. Costs often climb steeply as you grow, and migrating away is painful, since password hashes may not export cleanly, forcing resets that lose users. If the provider has an outage, raises prices, or shuts down, your entire front door is affected, and you have limited ability to respond.

Long answer205 / 190–220 words

Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.

Your AI spending is hard to predict because, unlike traditional software licensed per seat, most AI services charge by usage, and usage is difficult to forecast. Three forces drive this. First, costs are metered in small units of text processed, called tokens. A short question costs little, but an employee who uploads a lengthy contract for analysis can cost a hundred times more, and you cannot know in advance which will happen. Second, adoption spreads unevenly. One team's successful experiment can become a companywide habit within weeks, multiplying volume well beyond what any budget assumed. Third, vendors change prices and products often. Newer, more capable models typically cost more per use, and engineers tend to choose the most powerful option even when a cheaper one would suffice. You can regain control without slowing progress. Ask each team to set monthly spending caps with alerts, so surprises surface early rather than on the invoice. Require that routine tasks run on cheaper models, reserving premium ones for work that truly needs them. Assign costs to the departments that incur them, creating accountability. Finally, review usage quarterly and negotiate volume discounts once patterns stabilize. Treat AI like a utility bill: variable, but entirely manageable with measurement and discipline.

About the creator

Anthropic

Anthropic builds the Claude family of large language models, focused on safety, helpfulness, and honesty.

www.anthropic.com ↗
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