GPT 6.1 Sol API Pricing
GPT 6.1 Sol has tracked API rates of $2.00 per 1M input tokens and $10.00 per 1M output tokens. Cached input is billed at $0.1 per 1M tokens. Rates are in USD from the AI IQ dataset; see the GPT 6.1 Sol model profile for capability scores.
Price List
| Rate | Price (USD) | Notes |
|---|---|---|
| Input | $2.00 | per 1M tokens |
| Output | $10.00 | per 1M tokens |
| Cached input (read) | $0.1 | per 1M tokens |
| Cache write | $2.50 | per 1M tokens |
| Blended input + output | $12.00 | 1M in + 1M out at list price |
| Context window | 1.05M tokens |
Pricing References and Billing Caveats
These are the tracked rates, not a live quote. Hosted-provider choice, region, long-context tiers, caching, batch discounts, and temporary promotions can change the actual bill. Open-weight availability does not mean hosted inference is free. Workload examples assume the listed standard input and output rates, with no cache hits or discounts.
OpenAI pricing catalog — check current offerings and rate tiers. This catalog may not list historical models or third-party hosted endpoints.
Publisher-reported pricing notes
- Standard API prices are $2 per million input tokens and $10 per million output tokens; cached input is $0.10 per million.
- Prompts above 272K input tokens incur higher rates.
Official release and model references
These references document the model and its release; not every reference is a pricing table. Confirm the applicable endpoint and billing tier with your inference provider before purchase.
What a Workload Costs
Computed straight from the list prices above — token counts are illustrative workload sizes, not measurements of GPT 6.1 Sol.
| Workload | Cost | Tokens |
|---|---|---|
| Short chat turn | $0.00700 | 1K in / 500 out |
| Summarize a long document | $0.220 | 100K in / 2K out |
| Agentic coding session | $2.00 | 500K in / 100K out |
| 1M input + 1M output tokens | $12.00 | 1M in / 1M out |
Effective Cost
In AI IQ's scoring, GPT 6.1 Sol's list price is adjusted by a token-usage multiplier of 0.528 (measured from real benchmark runs), giving an effective cost of $6.34 per 1M input + output tokens. That places it #99 of 160 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.