anthropic/claude-opus-5-5 pricing
Claude Opus 5.5 is Anthropic's most capable Opus model. Better at coding, knowledge work, and long-running tasks. It's easier to collaborate with and more cost efficient than ever. anthropic/claude-opus-5-5 from DeepInfra costs $4.00 per 1M input tokens and $20.00 per 1M output tokens. Cached input is $0.200 per 1M. Context window 1,000,000 tokens.
API id anthropic/claude-opus-5-5 · DeepInfra · source · updated 2026-09-25 · general-purpose
$4.00
Input / 1M tokens
$20.00
Output / 1M tokens
$0.200
Cached input / 1M
1M
Context window
anthropic/claude-opus-5-5 specifications
- Provider
- DeepInfra
- API id
anthropic/claude-opus-5-5- Model type
- general-purpose
- Context window
- 1M tokens
- Max output
- not published
- Currency
- USD
- Price source
- Provider API
- Last checked
- 2026-09-25
Tags
- flagship
What anthropic/claude-opus-5-5 costs in practice
| Workload | Input tokens | Output tokens | Estimated cost |
|---|---|---|---|
| Short chat turn | 1,000 | 500 | $0.01 |
| Document summary | 50,000 | 2,000 | $0.24 |
| 1M tokens in, 200K out | 1,000,000 | 200,000 | $8.00 |
Estimates use the standard tier and ignore caching discounts, so real bills with repeated prompt prefixes are typically lower.
Common questions
- How much does anthropic/claude-opus-5-5 cost per 1M tokens?
- anthropic/claude-opus-5-5 costs $4.00 per 1M input tokens and $20.00 per 1M output tokens on the standard tier. Cached input is $0.200 per 1M.
- What is anthropic/claude-opus-5-5's context window?
- anthropic/claude-opus-5-5 accepts up to 1,000,000 tokens in a single request.
- Is there a cheaper DeepInfra model than anthropic/claude-opus-5-5?
- Yes. deepseek-ai/DeepSeek-V4-Flash is $0.090 per 1M input tokens versus $4.00 for anthropic/claude-opus-5-5, with a 1,048,576 token context window.
- Where does this anthropic/claude-opus-5-5 price come from?
- From the OpenRouter catalogue, because DeepInfra does not publish machine-readable pricing. OpenRouter is a reseller, so its rate can differ from DeepInfra's own — verify before committing spend.
Head-to-head comparisons
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