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GPT-6 Sol API pricing

GPT-6 Sol costs $2 per million input tokens and $10 per million output tokens at the standard rate. Here are its cached and batch prices, and what it costs for your usage.

OpenAI Prices last checked: how

Price per million tokens

Token typeStandardBatch (50% off)
Input$2$1
Cached input$0.20$0.10
Output$10$5

GPT-6 Sol: Long prompts are billed at higher long-context rates.

Standard rates in USD, before tax OpenAI API pricing
Your usage
GPT-6 Sol / month $156.00 $0.0052 per request
  • Input $60.00
  • Cached input $6.00
  • Output $90.00

Monthly cost for typical workloads

WorkloadStandardBatch
Support chatbot
2,000 in / 300 out tokens, 1,000 requests a day, 50% cached
$156.00$78.00
Document summaries
10,000 in / 800 out tokens, 200 requests a day
$168.00$84.00
Tagging / classifying
400 in / 20 out tokens, 20,000 requests a day
$600.00$300.00
USD, before tax. A month is 30 days. Compare every model in the calculator

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FAQ

Frequently asked questions

How much does GPT-6 Sol cost per million tokens?

GPT-6 Sol costs $2 per million input tokens and $10 per million output tokens at the standard rate. Prices are in USD, before tax, from OpenAI API pricing, last checked on 29 Sept 2026. Long prompts are billed at higher long-context rates.

Does GPT-6 Sol have a Batch API discount?

Yes. Through the Batch API, GPT-6 Sol costs $1 per million input tokens and $5 per million output tokens, 50% less than the standard rate. Batch requests are processed asynchronously rather than straight away.

How much does prompt caching save on GPT-6 Sol?

Input read from the prompt cache costs $0.20 per million tokens instead of $2, 90% less. Writing to or storing the cache can cost extra; those charges aren’t included here.

Is GPT-6 Sol one of the cheaper API models?

For a support chatbot sending 1,000 requests a day (2,000 input and 300 output tokens each, 50% of the input cached), GPT-6 Sol is the 13th cheapest of the 18 OpenAI, Anthropic and Google models we track, at $156.00 a month. Price alone doesn’t tell you how well a model will handle your task, so test the candidates on your own prompts.

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