Meet the models
Astra
State-of-the-art intelligence
Ambiguous problems, deep analysis, and ambitious deliverables.
Sol
Great everyday driver
Writing, coding, and work that needs judgment.
Luna
Smart and efficient
Scoped tasks, triage, and frequent automations
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.
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.
Luna is our most efficient model, while Astra is our state-of-the-art, most powerful model. If you don’t need to think about usage or how long it takes to complete a task, you can default to Astra. To optimize usage, use the guidance below to choose a model and reasoning effort for your needs.
Model and reasoning effort
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Luna · Low
Fine-grained edits, well-scoped problem-solving, and simple data extraction.
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Luna · Medium
Creating from clear briefs and making coordinated updates to existing work.
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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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Sol · Low
Focused writing and editing, fact-checking, and straightforward work in apps.
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Sol · Medium
Everyday coding, research, and workflows that need judgment and completeness.
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Sol · Extra high
Deeper analysis, thorough verification, and careful review of documents, data, and code.
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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.