Claude Opus 4.6 pricing
$5.00 per million input tokens, $25.00 per million output. 1M token context window. Read from Anthropic’s own documentation on August 3, 2026.
- Input
- $5.00 per 1M tokens
- Cached input
- $0.5000 10% of base
- Output
- $25.00 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 Opus 4.6 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 | $0.003750 | $375.00 |
| Chat turnA system prompt plus a few turns of history. | 1,500 | 300 | $0.0150 | $1,500.00 |
| Document summaryA long document in, a paragraph out. | 20,000 | 800 | $0.1200 | $12,000.00 |
| Code generationOutput-heavy — where output pricing dominates. | 2,000 | 1,500 | $0.0475 | $4,750.00 |
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 Opus 4.6
Anthropic does not publish a tokenizer that runs in a browser, so any pre-flight count for Claude Opus 4.6 is an estimate rather than a measurement.
Anthropic does not publish a client-side tokenizer. Counted with o200k_base and scaled ~1.18x, the commonly reported gap between tiktoken and Claude's pre-4.7 tokenizer.
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 Opus 4.6 — this page | $5.00 | $25.00 | 1M | $0.0150 |
| Claude Fable 5 | $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 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 Opus 4.6, not the cheapest on the market — those are the ones actually worth evaluating against it.
Frequently asked questions
- How much does Claude Opus 4.6 cost?
- $5.00 per million input tokens and $25.00 per million output tokens, with cached input at $0.5000 per million. On a typical chat turn of 1,500 input and 300 output tokens that is $0.0150 per request, or $1,500.00 per month at 100,000 requests. Read from Anthropic's own documentation on August 3, 2026.
- Can I count Claude Opus 4.6 tokens exactly?
- No. Anthropic does not publish a tokenizer that runs in a browser, so any pre-flight count for Claude Opus 4.6 is an estimate. Anthropic does not publish a client-side tokenizer. Counted with o200k_base and scaled ~1.18x, the commonly reported gap between tiktoken and Claude's pre-4.7 tokenizer. 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 Opus 4.6?
- 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 Opus 4.6?
- 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.