Claude Sonnet 5.5 API Pricing
Claude Sonnet 5.5 costs $2.00 per 1M input tokens and $10.00 per 1M output tokens.These rates were last verified on .
Published rates and billing units
| Billing dimension | USD list price |
|---|---|
| Input / 1M tokens | $2.00 |
| Cached input / 1M tokens | $0.10 |
| Cache write / 1M tokens | $2.50 |
| Output / 1M tokens | $10.00 |
| Cache write (1 hour) / 1M tokens | $4.00 |
| Batch: Input / 1M tokens | $1.00 |
| Batch: Output / 1M tokens | $5.00 |
| Batch: Cached input / 1M tokens | $0.05 |
| Batch: Cache write / 1M tokens | $1.25 |
| Batch: Cache write (1 hour) / 1M tokens | $2.00 |
Verified 2026-10-22 · Official source · Pricing methodology. Taxes, optional services and account-specific discounts are not included.
Current Anthropic API pricing, Sonnet 5.5 model docs and launch page list $2 input / $0.10 cache read / $10 output per 1M; 5-minute cache write $2.50 and 1-hour cache write $4 per 1M.
Cost examples
These examples use published list prices only. They exclude taxes, enterprise discounts, minimum charges, retries, batch discounts, and provider-specific billing rules.
1M input + 1M output
$12.00
A direct per-million comparison against every other text model.
100K input + 25K output
$0.45
A compact chat, summarization, or analysis workload.
10M input + 2M output
$40.00
A monthly production estimate for heavier RAG or agent traffic.
75% cached input + 250K output
$3.08
Cached input is about 95% cheaper than standard input at list price.
Calculator for Claude Sonnet 5.5
Enter your own input, output, and cached-token assumptions. This calculator is preloaded with only Claude Sonnet 5.5, so the result stays focused on this model.
Input cost
$2.00
Output cost
$10.00
Total
$12.00
Price history
No list-price movement across 19 daily snapshots from 2026-10-04 to 2026-10-22.
When to use Claude Sonnet 5.5
Good fit
- code generation, debugging, and agentic development work where output quality affects engineering time
- reasoning-heavy prompts, planning, analysis, and multi-step tool use
- long-document and repository-scale prompts where context window matters
Be careful with
- blind migration without testing your own prompts, latency, and failure modes