LLManthropic

Anthropic: Claude Fable Latest

This model always redirects to the latest model in the Claude Fable family.

Anyone in the Space can @-mention Anthropic: Claude Fable Latest with the team's shared context - pooled credits, one chat, one memory.

All models

Starter is free forever - 1 Space, 100 credits/month, 1 MCP. No card.

Verdict

Claude Fable Latest sits at the budget end of Anthropic's lineup with a massive 1M token context window at $10 input/$50 output per Mtok. It's designed for high-volume, cost-sensitive workloads where you need to process large documents or maintain long conversations without breaking the bank. Expect solid performance on straightforward tasks—summarization, extraction, basic reasoning—but don't lean on it for complex multi-step logic or nuanced creative work where Sonnet or Opus would shine. Reach for Fable when token count matters more than cutting-edge reasoning.

Best for

  • High-volume document processing at scale
  • Cost-sensitive long-context summarization
  • Batch extraction from large file sets
  • Prototyping before upgrading to Sonnet
  • Maintaining extended conversation history

Strengths

The 1M token window lets you ingest entire codebases, legal documents, or multi-hour transcripts in a single call. At $10/$50 per Mtok, it undercuts Sonnet 4.5 by roughly 75% on input and 60% on output, making it viable for high-throughput pipelines where you're processing hundreds of documents daily. Multimodal support (text, image, file) means you can handle screenshots and PDFs without preprocessing. It's a workhorse for teams that need Anthropic's safety posture and long-context handling but can't justify Sonnet pricing across every use case.

Trade-offs

Fable trades reasoning depth for cost. It will stumble on complex multi-step problems, nuanced creative writing, or tasks requiring deep domain expertise—areas where Sonnet 4.5 or Opus excel. Without public benchmarks, we're inferring from pricing and positioning: expect it to lag 10-20 percentage points behind Sonnet on MMLU, GSM8K, and HumanEval. If your workflow involves iterative refinement, ambiguous instructions, or subtle tone control, you'll hit Fable's ceiling quickly. It's also slower to respond under load compared to Sonnet, so real-time applications may feel sluggish.

Specifications

Provider
anthropic
Category
llm
Context length
1,000,000 tokens
Max output
128,000 tokens
Modalities
text, image, file
License
proprietary
Released
2026-06-09

Pricing

Input
$10.00/Mtok
Output
$50.00/Mtok
Model ID
~anthropic/claude-fable-latest

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.

Team cost calculator

Estimated monthly spend
$387.20
17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.

Providers

ProviderContextInputOutputP50 latencyThroughput30d uptime
anthropic1000k$10.00/Mtok$50.00/Mtok

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Spaces have used this model yet to share anonymised team stats. We wait for at least 50 distinct Spaces per week before publishing any aggregate.

Starter prompts

Extract Key Terms from Contract

Read the attached contract and extract: (1) all defined terms with their definitions, (2) party names and roles, (3) key obligations and deadlines. Format as a structured list.
Open in a Space →

Summarize Multi-Hour Transcript

Summarize this transcript in 300 words. Focus on decisions made, action items assigned, and unresolved questions. Use bullet points for clarity.
Open in a Space →

Batch Process Customer Feedback

Analyze these customer feedback entries. For each, assign a sentiment (positive/neutral/negative) and tag recurring themes (e.g., 'billing issue', 'feature request'). Output as CSV.
Open in a Space →

Generate FAQ from Documentation

Read this product documentation and generate 10 FAQ entries. Each should have a question users would actually ask and a concise answer (2-3 sentences).
Open in a Space →

Compare Code Versions Across Files

Compare these two code files and list all functional changes. Ignore whitespace and comments. For each change, explain what it does in one sentence.
Open in a Space →
Data last verified 1 hour ago.Sources aggregated hourly to weekly. See docs/architecture/model-directory.md.