Speed
Output tokens / second
Custom pricing for production workloads
Claude Fable 5 is Anthropic's model designed for complex reasoning and agentic coding workflows. Features a 1M token context window, adaptive thinking, and strong performance across multilingual tasks, long-context handling, and vision.
Claude Fable 5 is Anthropic's model designed for complex reasoning and agentic coding workflows.
anthropic/claude-fable-5https://api.aimlapi.com/v1Claude Fable 5 API is Anthropic’s latest large language model interface designed for developers, startups, enterprises, and AI product teams seeking advanced reasoning, long-context understanding, agentic workflows, and reliable production-scale deployments. Built on the newest generation of Claude architecture, Fable 5 expands beyond traditional chatbot interactions by providing a foundation for sophisticated AI applications that can analyze complex information, maintain context across lengthy conversations, execute multi-step tasks, and generate highly structured outputs.
One of the defining strengths of Claude Fable 5 is its enhanced reasoning engine. The model can break down difficult problems into manageable components, evaluate multiple possibilities, identify dependencies, and generate well-supported conclusions.
For businesses, this means the model can assist with:
Strategic AnalysisOrganizations can use Claude Fable 5 to evaluate market trends, analyze competitive landscapes, identify risks, and summarize large collections of research materials.
Technical Decision SupportEngineering teams can leverage the model for architecture planning, design reviews, troubleshooting workflows, and system documentation.
Research ApplicationsResearchers can process extensive documents, identify patterns, extract insights, and generate concise summaries from large information sources.
The result is a model capable of functioning as a highly effective analytical assistant rather than a simple text generator.
Modern enterprises often work with substantial amounts of information. Traditional AI systems frequently struggle when documents become too large or conversations extend over many interactions.
Claude Fable 5 is designed to maintain coherence across extensive contexts, making it ideal for applications involving:
Large contracts, policy libraries, compliance frameworks, and regulatory documentation can be processed more effectively.
Software documentation, system specifications, API references, and engineering reports can remain accessible within a single workflow.
Organizations can integrate internal documentation, training materials, and operational procedures into AI-powered systems.
This expanded context handling significantly reduces the need for aggressive document chunking and complex retrieval strategies.
Software development remains one of the most valuable use cases for modern AI models. Claude Fable 5 includes substantial improvements in code generation, debugging, refactoring, and software design support.
Developers can generate production-ready code across multiple programming languages, frameworks, and environments.
The model can identify logical errors, implementation flaws, and edge cases that might otherwise be overlooked.
Legacy systems can be modernized through intelligent code restructuring while preserving functionality.
Claude Fable 5 can generate technical documentation, inline comments, API descriptions, and onboarding materials.
Many enterprise applications require predictable outputs rather than free-form text. Claude Fable 5 excels at producing structured responses suitable for automation pipelines.
Organizations can deploy intelligent support systems capable of understanding customer issues, retrieving relevant information, and generating accurate responses. Benefits include faster response times, reduced support costs, and improved customer satisfaction.
Developer-focused applications can leverage Claude Fable 5 for code generation, code review, debugging, testing assistance, and technical documentation.
Companies often struggle to make internal information accessible. Claude Fable 5 can transform documentation repositories into intelligent knowledge systems that provide contextual answers and actionable insights.
Financial institutions, consulting firms, research organizations, and analysts can use the API to process large volumes of information and generate comprehensive summaries.
Marketing teams, publishers, and content organizations can automate research, drafting, editing, localization, and content optimization workflows.
Claude Fable 5 is particularly effective for systems that coordinate multiple tasks, tools, and decision points within a larger workflow.
Compared with earlier Claude models, Fable 5 introduces improvements across several critical dimensions.
Better ReasoningMore consistent problem-solving and deeper analytical capabilities.
Improved Long-Context PerformanceEnhanced ability to work with extensive documents and lengthy conversations.
Stronger Agent CapabilitiesBetter support for planning, task management, and tool orchestration.
Enhanced Coding PerformanceMore accurate code generation and debugging assistance.
eater Enterprise ReadinessExpanded governance, security, and deployment controls.
Claude Fable 5 is documented as suited to software engineering and coding, including ambitious coding projects, code review, performance work, and long-running development sessions. It is also suited to knowledge work, especially complex multi-stage work such as deep research, analysis, and enterprise workflows involving financial analysis, spreadsheets, slides, and documents.
It is documented for long-running, autonomous or agentic tasks, including work that can span hours, days, or multi-day projects with planning across stages, delegation to sub-agents, and self-verification. It is also suited to vision-heavy work, such as understanding diagrams, charts, and tables in files and PDFs, and checking outputs against the original design or goal.
See how Claude Fable 5 compares in generation speed. Data retrieved Nov 3, 2026.
Output tokens / second
Compare 8 models available on AI/ML API across 5 independent benchmarks.
Expert-written questions across academic subjects.
Graduate-level science questions written to resist web search.
Agentic tasks completed in a command-line environment.
Reasoning across long documents of about 100k tokens.
Python coding tasks drawn from scientific research.
| Usage | Price | Unit |
|---|---|---|
| Input | $10 | 1M tokens |
| Output | $50 | 1M tokens |
| Cached input | $1 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
| $10 | $50 | |
| $0.1 | $0.5 | |
| $2 | $10 | |
| $4 | $20 | |
| $10 | $50 |
Give your agent the model details, code and documentation.
export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "anthropic/claude-fable-5",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "anthropic/claude-fable-5",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "anthropic/claude-fable-5",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Claude Fable 5 costs $10 input and $50 output per 1M tokens on AI/ML API, billed as you go with no subscription.
Use anthropic/claude-fable-5 as the model parameter. Requests go to the OpenAI-compatible endpoint with your AI/ML API key.
Claude Fable 5 accepts up to 1,000,000 tokens of context and returns up to 128,000 output tokens.