Claude Fable 5 pricing
$10.00 per million input tokens, $50.00 per million output. 1M token context window. Read from Anthropic’s own documentation on August 3, 2026.
- Input
- $10.00 per 1M tokens
- Cached input
- $1.00 10% of base
- Output
- $50.00 5.0× input
- Context window
- 1M 128K max output
- Token counting
- Estimate o200k_base
- Price verified
- 2026-08-03 2 days ago
Source: Anthropic pricing documentation. Prices change without notice — verify before committing spend.
What Claude Fable 5 costs on real work
Four workload shapes at 100,000 requests a month. The point of showing four is that the ranking between models changes depending on which one describes you.
| Workload | In | Out | Per request | Per month |
|---|---|---|---|---|
| ClassificationShort input, one-word answer. Input-dominated. | 500 | 50 | $0.007500 | $750.00 |
| Chat turnA system prompt plus a few turns of history. | 1,500 | 300 | $0.0300 | $3,000.00 |
| Document summaryA long document in, a paragraph out. | 20,000 | 800 | $0.2400 | $24,000.00 |
| Code generationOutput-heavy — where output pricing dominates. | 2,000 | 1,500 | $0.0950 | $9,500.00 |
Put your own numbers in the cost calculator, or measure a real prompt first in the token counter. If your requests share a stable prefix, the cached rate applies to most of your input — check the structure in the cache checker.
Counting tokens for Claude Fable 5
Anthropic does not publish a tokenizer that runs in a browser, so any pre-flight count for Claude Fable 5 is an estimate rather than a measurement.
Anthropic does not publish a client-side tokenizer. Counted with o200k_base, then scaled: ~1.18x for the historical tiktoken/Claude gap, times ~1.30x for the newer tokenizer introduced with Claude 4.7.
Treat it as accurate to within roughly ten to twenty percent. That is fine for budgeting and wrong for sizing a prompt right at a context window boundary — where precision matters, use Anthropic’s own token counting endpoint from your backend. The methodology page sets out every scaling factor used here.
Other Anthropic models
The tier question: is a cheaper model in the same family enough for your task?
| Model | Input | Output | Context | Chat turn |
|---|---|---|---|---|
| Claude Fable 5 — this page | $10.00 | $50.00 | 1M | $0.0300 |
| Claude Opus 5 | $5.00 | $25.00 | 1M | $0.0150 |
| Claude Opus 4.8 | $5.00 | $25.00 | 1M | $0.0150 |
| Claude Opus 4.6 | $5.00 | $25.00 | 1M | $0.0150 |
| Claude Sonnet 5 | $2.00 | $10.00 | 1M | $0.006000 |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 1M | $0.009000 |
| Claude Sonnet 4.5 | $3.00 | $15.00 | 200K | $0.009000 |
Alternatives from other providers
Models priced nearest to Claude Fable 5, not the cheapest on the market — those are the ones actually worth evaluating against it.
Frequently asked questions
- How much does Claude Fable 5 cost?
- $10.00 per million input tokens and $50.00 per million output tokens, with cached input at $1.00 per million. On a typical chat turn of 1,500 input and 300 output tokens that is $0.0300 per request, or $3,000.00 per month at 100,000 requests. Read from Anthropic's own documentation on August 3, 2026.
- Can I count Claude Fable 5 tokens exactly?
- No. Anthropic does not publish a tokenizer that runs in a browser, so any pre-flight count for Claude Fable 5 is an estimate. Anthropic does not publish a client-side tokenizer. Counted with o200k_base, then scaled: ~1.18x for the historical tiktoken/Claude gap, times ~1.30x for the newer tokenizer introduced with Claude 4.7. Treat it as accurate to within roughly ten to twenty percent and never as the basis for sizing a prompt right at a context window boundary.
- What is the context window of Claude Fable 5?
- 1,000,000 tokens, with a maximum of 128,000 output tokens in a single response. That budget covers everything in the request — system prompt, conversation history, tool definitions, documents — plus the response itself, not just your input.
- Why is output more expensive than input on Claude Fable 5?
- Output costs 5.0 times input here. Input is processed in a single parallel pass, while output is generated one token at a time with a full pass over the model for each. That is why a model that answers concisely can be cheaper in production than one with a lower headline rate.