Opus 4.7 API Pricing
Opus 4.7 costs $6.25 per 1M input tokens and $25.00 per 1M output tokens on Anthropic's API. Cached input is billed at $0.5 per 1M tokens. Prices are provider list prices in USD, refreshed as sources update; see the Opus 4.7 model profile for capability scores.
Price List
| Rate | Price (USD) | Notes |
|---|---|---|
| Input | $6.25 | per 1M tokens |
| Output | $25.00 | per 1M tokens |
| Cached input (read) | $0.5 | per 1M tokens |
| Cache write | $7.81 | per 1M tokens |
| Cache write (1-hour) | $12.50 | per 1M tokens |
| Blended input + output | $31.25 | 1M in + 1M out at list price |
| Context window | 1M tokens |
What a Workload Costs
Computed straight from the list prices above — token counts are illustrative workload sizes, not measurements of Opus 4.7.
| Workload | Cost | Tokens |
|---|---|---|
| Short chat turn | $0.019 | 1K in / 500 out |
| Summarize a long document | $0.675 | 100K in / 2K out |
| Agentic coding session | $5.63 | 500K in / 100K out |
| 1M input + 1M output tokens | $31.25 | 1M in / 1M out |
Effective Cost
In AI IQ's scoring, Opus 4.7's list price is adjusted by a token-usage multiplier of 1.547 (measured from real benchmark runs), giving an effective cost of $48.34 per 1M input + output tokens. That places it #114 of 117 models on the effective-cost ranking (lower is cheaper). Some models spend far more tokens than others on the same task, so effective cost compares what a unit of work really costs. Method details are on the methodology page; see all models on the cost charts and the falling cost of intelligence over time.
Cheaper Alternatives at Similar Capability
Models with a lower effective cost that score within a few IQ points of Opus 4.7 (IQ 127), or better.
| Model | Provider | IQ | Effective Cost / 1M |
|---|---|---|---|
| gpt-5.6-sol | OpenAI | 136 | $40.81 |
| opus-5 | Anthropic | 135 | $37.56 |
| gpt-5.5 | OpenAI | 133 | $31.38 |
| gpt-5.6-terra | OpenAI | 132 | $20.25 |
| opus-4.8 | Anthropic | 130 | $39.12 |
| gpt-5.6-luna | OpenAI | 129 | $9.29 |