Claude Fable 5

Claude Fable 5 is Anthropic's new top-capability generally available Claude model. It is built for long-running coding, agentic execution, multimodal reasoning, research, and high-stakes professional workflows, with stronger long-horizon performance than earlier Opus models, state-of-the-art vision, and a 1M-token context window. Safety classifiers can route some sensitive requests to Claude Opus 4.8 instead.

Complete technical specification for integration
API Options
Platform-level options for task execution and delivery.
taskType
stringrequiredvalue: textInferenceIdentifier for the type of task being performed
taskUUID
stringrequiredUUID v4UUID v4 identifier for tracking tasks and matching async responses. Must be unique per task.
outputFormat
stringdefault: TEXTSpecifies the file format of the generated output. The available values depend on the task type and the specific model's capabilities.
Allowed values2 values
webhookURL
stringURISpecifies a webhook URL where JSON responses will be sent via HTTP POST when generation tasks complete. For batch requests with multiple results, each completed item triggers a separate webhook call as it becomes available.
Learn more1 resource
- WebhooksPLATFORM
- Webhooks
deliveryMethod
stringdefault: syncDetermines how the API delivers task results.
Allowed values3 values
- Returns complete results directly in the API response.
- Returns an immediate acknowledgment with the task UUID. Poll for results using getResponse.
- Streams results token-by-token as they are generated.
Learn more1 resource
- Task PollingPLATFORM
includeCost
booleandefault: falseInclude task cost in the response.
includeUsage
booleandefault: falseInclude token usage statistics in the response.
numberResults
integermin: 1max: 4default: 1Number of results to generate. Each result uses a different seed, producing variations of the same parameters.
Inputs
Input resources for the task (images, audio, etc). These must be nested inside the inputs object.
inputs object.Core Parameters
Primary parameters that define the task output.
model
stringrequiredvalue: anthropic-claude-fable-5Identifier of the model to use for generation.
Learn more3 resources
jsonSchema
object | stringJSON Schema for structured output. Only honoured when
outputFormatis JSON. Accepts the OpenAI envelope ({name, schema, strict}) or a bare JSON Schema; bare schemas are auto-wrapped withname='response'andstrict=true.
messages
array of objectsrequiredmin items: 1Array of chat messages forming the conversation context. The final message must use the user role.
Settings
Technical parameters to fine-tune the inference process. These must be nested inside the settings object.
settings object.settings»systemPromptsystemPrompt
stringmin: 1max: 200000System-level instruction that guides the model's behavior and output style across the entire generation.
settings»cachecache
objectPrompt caching configuration. Caches designated parts of the request to reduce cost and latency on repeated calls.
Properties2 properties
settings»cache»scopescope
stringdefault: system+historyControls which parts of the request are cached.
Allowed values2 values
- Cache the system prompt only.
- Cache the system prompt and conversation history up to the last user message.
settings»cache»ttlttl
stringdefault: 5mTime-to-live for the cache.
Allowed values2 values
settings»maxTokensmaxTokens
integermin: 1max: 128000Maximum number of tokens to generate in the response.
settings»splitThinkingsplitThinking
booleandefault: trueWhen enabled, the model's internal reasoning is separated from the main response and returned in a dedicated
reasoningContentfield.
settings»stopSequencesstopSequences
array of stringsmin: 1max: 50max items: 5Array of sequences that will cause the model to stop generating further tokens when encountered.
settings»thinkingLevelthinkingLevel
stringdefault: highControls the depth of internal reasoning the model performs before generating a response.
Allowed values6 values
toolChoice
objectControls how the model selects which tool to call. This only takes effect when
toolsare defined.Examples3 examples
Let the model decide (default):
"toolChoice": { "type": "auto" }Force a specific tool call:
"toolChoice": { "type": "tool", "name": "get_weather" }Require any tool call:
"toolChoice": { "type": "any" }Properties2 properties
toolChoice»typetype
stringrequiredStrategy the model uses to decide when and which tools to call.
Allowed values4 values
- The model decides whether to call a tool based on the conversation context. This is the recommended default.
- The model must call at least one tool but chooses which one. Useful when you always need structured output.
- The model must call the specific tool identified by
name. Use this to force a particular function call. - The model will not call any tool, even if tools are defined. Useful for forcing a text-only response.
toolChoice»namename
stringName of the specific tool the model must call. Required when type is
tool.
tools
array of objectsmin items: 1An array of tool definitions that the model may call during generation. The model can invoke one or more tools based on the conversation context, outputting structured calls with arguments instead of (or alongside) free-text.
For
functiontools, each definition requires:type:"function"name: Unique identifier (alphanumeric, hyphens, underscores; max 64 chars).description: What the function does. The model uses this to decide when to call it.schema: JSON Schema object describing the expected input arguments.
The
searchtool is executed server-side by the provider. You don't need to handle the tool result yourself.The
codeInterpretertool is executed server-side by the provider. You don't need to handle the tool result yourself.Examples4 examples
Function tool, weather lookup:Built-in web search:"tools": [ { "type": "function", "name": "get_weather", "description": "Get current weather for a city", "schema": { "type": "object", "properties": { "city": { "type": "string", "description": "City name" } }, "required": ["city"] } } ], "toolChoice": { "type": "auto" }Built-in code interpreter:"tools": [ { "type": "search" } ]Multiple function tools:"tools": [ { "type": "codeInterpreter" } ]"tools": [ { "type": "function", "name": "search_products", "description": "Search the product catalog by query and filters.", "schema": { "type": "object", "properties": { "query": { "type": "string" }, "category": { "type": "string" } }, "required": ["query"] } }, { "type": "function", "name": "add_to_cart", "description": "Add a product to the user's shopping cart.", "schema": { "type": "object", "properties": { "productId": { "type": "string" }, "quantity": { "type": "integer", "minimum": 1 } }, "required": ["productId"] } } ]Properties4 properties
tools»typetype
stringrequiredThe kind of tool to make available to the model. User-defined functions require
nameandschema, while built-in tools (search,codeInterpreter) are executed server-side by the provider.Allowed values1 value
tools»namename
stringmax: 64Unique function name. Required for function tools.
tools»descriptiondescription
stringExplanation of what the function does, used by the model to decide when to call it.
tools»schemaschema
objectJSON Schema object describing the function's input parameters.