OpenAI · Everyday work and programming
GPT-6 Sol
A versatile choice for working with text, code and images.
- Costs
- $2 input / $10 output per million tokens. Above 272 thousand input tokens: 2× input and 1.5× output.
- Speed
- We have not independently measured speed on a comparable basis.
- Availability
- OpenAI API
- Input length
- 1,050,000 tokens in total; output capped at 128 thousand tokens.
- Inputs
- Text and images → text
Data checked . Published September 22, 2026. Specifications and prices are provided by the vendor; usage recommendations come from the editorial team. We do not yet have our own comparative test of this version.
Ideal use
When to choose it
- Writing and fixing code
- Processing documents and multi-step tasks
Usage boundaries
When to choose another model
- For simple classification, consider the cheaper GPT-6 Luna.
Traceable supporting sources
Data and measurement sources
Distinguish between the manufacturer's documentation and the results of a specific test. Measurements also depend on the settings and the task set used.
Vendor documentation
Specifications, availability, and termsManufacturer data verified as of the review date. Recommended use is an editorial interpretation.
Open original source ↗Efficiency in practice
With what GPT-6 Sol combine
Many tasks without unnecessary costs
A low-cost model handles routine work, and the more expensive one gets only the truly difficult cases.
Use your own data to verify when a stronger model should take over the work. A model cannot reliably assess its own answer.Quick code edits with a second check
One system makes the change, another looks for blind spots, and tests decide.
A second model will not help if both receive the same incorrect assumption without evidence.Documents → analysis → verification
A fast model prepares the material, and a stronger model focuses on analyzing it.
Agreement between two models is not proof; the cited inputs are what matters.Trends over time
Related events from AI Radar
OpenAI fixed a bug in image encoding in the GPT-6 Sol and GPT-6 Luna models
Developers who used image inputs with the GPT-6 Sol or GPT-6 Luna models may have encountered worse results due to a bug in image encoding; after the fix, it is advisable to retest your tasks involving visual content.
Companies running pipelines with image inputs via the GPT-6 Sol or GPT-6 Luna models (API, Codex, computer use) may have received worse results on visual tasks due to a bug; it is now advisable to re-verify output quality and, if needed, recompute the affected workflows.
Next step
Compare the model using your actual task.
A benchmark narrows the selection. A short trial on your data determines the choice.