Model selection
For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending
.mdto the page URL.
Meet the models
Availability, tools, reasoning settings, and usage limits differ by product and model version. Check the models available in ChatGPT or the API model catalog.
Find the right model for your workflow
Choose your work and task and get a recommendation.
When to consider GPT-6.1 Sol
Consider GPT-6.1 Sol for complex projects where cost matters, such as creating a board presentation from financial results or building a website from a product brief. Compare it with Astra on the same task to assess the tradeoff between quality and cost.
See the API model page for specifications and API pricing, or Codex and ChatGPT Work availability for access through your ChatGPT plan.
How to think about models and reasoning effort
Luna is the most cost-efficient model, while Astra is our state-of-the-art, most powerful model. If cost and latency aren't a concern, you can default to Astra. To reduce costs or latency, use the guidance below to choose a model and reasoning effort for your needs.
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Luna · Low
Fine-grained edits, well-scoped problem-solving, and simple data extraction.
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Luna · Extra high
Finding current context across multiple apps, prioritizing work, and solving problems with clear constraints.
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GPT-6.1 Sol · Medium
Complex technical work and coordinated deliverables you expect to revise.
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GPT-6.1 Sol · Extra high
Polished deliverables, connected visual systems, and decisions built from conflicting evidence.
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Astra · Low
Concise writing and content adaptation that preserve facts and nuance.
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Astra · Medium
Ambitious projects that need broad context, reliable interactions, and complete results.
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Astra · Extra high
Demanding analysis and complex deliverables with exacting requirements.
Experiment
Treat the guidance on this page as a starting point. The best way to find the right model for your workflow is to experiment with different models and reasoning settings to see what works.
Start by considering:
- How often does your workflow run? A frequent automation makes usage and cost add up faster than an occasional project.
- How quickly do you need the result? A task you're waiting on may need a faster setting than one that runs overnight.
- How will you use the output? A draft for your review may need less polish than something you'll share externally.
- How important is the quality of the result? Depending on your use case or industry, you might want to use a stronger model to put an emphasis on quality.
If you can, experiment using the same inputs to compare results and keep the lightest setting that meets your quality bar.