Claude API pricing
Official model token pricing table.
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Why output tokens cost more than input tokens
Learn why output tokens usually cost more than input tokens and how generation cost affects model serving economics, AI agents, and inference spend.
Compute & Pricing LessonsLearning path
One concept connected to AI compute market decisions.
5-8 minutesRead time
A practical introduction designed to be completed in one sitting.
Tokens / Pricing / Serving CostTags
Useful for developers, founders, procurement teams, and analysts tracking model-serving economics.
Plain-English definition
Plain-English definition
Output tokens often carry a higher listed API price than input tokens because generation is performed sequentially during the response, tying up serving resources as each next token is produced; actual pricing is set by each provider.
Why it matters
Why it matters
Output-heavy coding, reasoning, and agent workflows can create much larger serving bills than short responses, so token mix matters for model adoption and AI compute demand.
- Capability changes matter economically only when they affect deployed workloads or buyer choices.
- Token volume, latency, retries, and throughput determine how a useful result becomes serving cost.
- A ComputeTape reader should connect model evidence to inference demand and required AI compute capacity.
Simple example
Simple example
Anthropic lists Claude Opus 4.7 at $5 per million input tokens and $25 per million output tokens. At those listed rates, an illustrative request with 100,000 input tokens and 20,000 output tokens costs $0.50 for input and $0.50 for output: much fewer output tokens contribute the same spend.
- Use the example to compare workload economics, not as a current market quote.
- Record the task type, evaluation or workload conditions, and the cost inputs before comparing results.
- A successful result is valuable only if its latency and cost fit the intended production use.
Example figures are illustrative calculations, not current quoted market prices.
Current example
Current pricing example: Claude Opus 4.7
Anthropic’s official pricing documentation lists Claude Opus 4.7 at $5 per million base input tokens and $25 per million output tokens. Last checked: May 24, 2026.
Pricing is current-source information and should be checked again before making a procurement decision.
Market signal
How to read the market signal
If buyers adopt agents or long-form reasoning workflows that produce more output, model-serving demand and spend can grow even when request count or input volume appears stable.
- Look for adoption, routing, usage-volume, or capacity signals rather than a headline score alone.
- Compare input tokens, output tokens, latency, tool rounds, retries, and completion quality together.
- Keep sourced capability facts separate from interpretation about future AI compute demand.
Market read: this metric becomes an AI compute signal only when it changes serving volume, effective workload cost, or the capacity buyers require.
Common mistake
Common mistake
Do not estimate a workload from input tokens alone, and do not assume every provider or model has the same input-to-output pricing relationship.
Practical takeaway
What you can do with this
Track input and output token volumes separately, use current official pricing, and model how answer length, reasoning, and agent steps change cost per completed task.
- Buyers: test the metric on tasks close to the workload you will pay to serve.
- Builders: measure tokens, latency, retries, completion rate, and model price on each test run.
- Analysts: require a source and an adoption mechanism before treating a model result as demand evidence.
Decision check: identify the capability measured, the serving cost driver it affects, and the buyer behavior that would make capacity demand change.
Helpful memory trick
Helpful memory trick
Input loads the workbench; output keeps the generator working token by token.
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