Short answer
There is no single GPT model that is best for every production API workload. OpenAI recommends GPT-6 Astra for complex reasoning and coding, GPT-6.1 Sol when balancing capability and cost, and GPT-6 Luna for cost-sensitive, high-volume work. It positions GPT-6.1 Terra for visual reasoning, precise computer use, and clear writing. Treat these as starting points, not proof that a model will be best on your specific tasks. (developers.openai.com)
| Workload | First model to evaluate | Why consider it |
|---|---|---|
| Complex reasoning, coding, or demanding professional work | GPT-6 Astra (gpt-6-astra) |
OpenAI’s flagship recommendation for demanding tasks. (developers.openai.com) |
| Complex work where cost matters | GPT-6.1 Sol (gpt-6.1-sol) |
Positioned as a balance of capability and cost for complex work. (developers.openai.com) |
| Visual reasoning, computer use, or natural writing | GPT-6.1 Terra (gpt-6.1-terra) |
Specifically positioned for these tasks. (developers.openai.com) |
| Focused, frequent, high-volume tasks | GPT-6 Luna (gpt-6-luna) |
OpenAI describes it as an efficient option for focused, high-volume work. (developers.openai.com) |
Compare API prices and shared capabilities
The table shows OpenAI’s listed Standard API prices per 1 million tokens. Input and output are billed separately; cached input is shown for workloads that use it. These listed rates apply to the Standard pricing tier and are not a full estimate of your bill. (developers.openai.com)
| Model | Input / 1M tokens | Cached input / 1M tokens | Output / 1M tokens | Listed use |
|---|---|---|---|---|
| GPT-6 Astra | $10.00 | $1.00 | $50.00 | Complex reasoning and coding |
| GPT-6.1 Sol | $2.00 | $0.10 | $10.00 | Complex work with cost in mind |
| GPT-6.1 Terra | $1.00 | $0.05 | $5.00 | Visual reasoning and computer use |
| GPT-6 Luna | $0.10 | $0.01 | $0.50 | Focused, high-volume work |
The four model entries list a 1.05-million-token context window, 128,000 maximum output tokens, and support for Functions, Web search, File search, and Computer use. The models are available through the Responses API and OpenAI Client SDKs. Check the model and API documentation for the exact features and limits that apply to your integration. (developers.openai.com)
Long-context pricing and processing modes can change the applicable rates, and tool-specific calls may incur additional fees. For example, the Astra model page says prompts above 272,000 input tokens are repriced for the full request. Review the current pricing details for your model and usage pattern before budgeting. (developers.openai.com)
A simple cost illustration
Suppose an application makes 100,000 requests, each with 10,000 input tokens and 1,000 output tokens. Using the listed Standard input and output rates, the token charges would be approximately:
- Astra: $15,000
- Sol: $3,000
- Terra: $1,500
- Luna: $150
This is a hypothetical calculation, not a usage forecast: it excludes cached-input pricing, tools, long-context adjustments, retries, and any review or correction work. A lower token bill does not necessarily mean a lower cost per successful result.
How to choose before production
Use a small, controlled evaluation rather than selecting by model name alone:
- Define success first. Write down what a correct, usable result must contain and which errors make a response fail.
- Use representative inputs. Include ordinary cases, edge cases, and examples that reflect the actual production workload.
- Compare candidates consistently. Keep the input, instructions, tools, and relevant settings the same. OpenAI’s model-selection guidance recommends comparing candidate models on the same task to assess the quality-and-cost trade-off. (
developers.openai.com) - Record the outcomes that matter. Track pass rate, human correction or review time, end-to-end latency, and API cost—including retries and tool usage where applicable.
- Choose the least expensive candidate that clears your acceptance bar. If a cheaper model fails too often or needs more correction, compare total cost per accepted result, not token rates alone.
This is a proposed evaluation method, not a test result for any of the four models. OpenAI’s model-selection guidance also recommends checking whether the output fits the task—for example, judging writing for audience fit and useful detail, and verifying computer-use tasks by checking the resulting saved state. (developers.openai.com)
Check fit before integrating
OpenAI’s model catalog says its latest models support text and image input and text output, and lists the Responses API and Client SDKs as access routes. The catalog’s flagship entries also list the tools shown above. Those descriptions help screen candidates, but they do not establish that a model is available to a particular account or region, or that a specific tool workflow will behave as your application requires. (developers.openai.com)
Bottom line: start with Astra for demanding reasoning and coding, Sol when complex capability must be balanced against cost, Terra for visual and computer-use tasks, or Luna for focused volume. Then test the best-fit candidates on your own acceptance criteria and workload before choosing a production default.