GPT-6 Sol Pro pricing.
Prices checked
GPT-6 Sol Pro costs $1 in and $5 out per million tokens from OpenAI; the cheapest price is $1.55 at NanoGPT, checked .
OpenAI's own price
OpenAI
$1 in · $5 out
127% above the typical price of the 392 text models we track.
Cheapest price
NanoGPT
$2 in · $0.20 out
22% less than OpenAI, and the provider says why.
Web search from this model costs $10 per 1,000 searches, plus its tokens; dedicated search APIs from $1
Every provider selling GPT-6 Sol Pro
What it costs for your work
A month at OpenAI's own price
A month at NanoGPT, the cheapest
What you pay more
A page is about 600 tokens in and 60 out, a support reply 200 in and 300 out, an email 150 in and 250 out. Nothing else is counted: no cache discount, no batch rate, no free tier.
How we count
Prices are per million tokens. The big number on each row is blended 3 in : 1 out, the way most work uses a model. A price below OpenAI's own is listed as cheapest only when the provider says why.
- Costs more
- Sold by someone else for more than the maker charges.
- Maker's price
- What the lab that trained the model charges on its own API. Every other row is measured against it.
Similar models
Models from other labs that do what GPT-6 Sol Pro does, cheapest first. Each percentage is against GPT-6 Sol Pro's cheapest price, $1.55 per M tokens.
Frequently asked
- Who sells GPT-6 Sol Pro cheapest?
- No provider sells GPT-6 Sol Pro below OpenAI's own price for a reason it publishes. 7 providers sell it at that price or above, and every one of them is on this page.
- Is GPT-6 Sol Pro open weights?
- No. OpenAI keeps the weights, so every price on this page is OpenAI's own or a resale of it. A price below OpenAI's here needs a published reason, or it is listed and not ranked.
- How much context does GPT-6 Sol Pro hold?
- 1,050,000 tokens in one call, which is about 1.1M, and it can call your own functions.
- How does GPT-6 Sol Pro compare with the rest of the market?
- At $2 per million tokens, OpenAI's own price is 127% above the typical $0.88 across the 392 text models we track.
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