Short answer: There is no best model for every production workload. The OpenAI model catalog recommends GPT-6 Astra for complex reasoning and coding, GPT-6.1 Sol to balance intelligence and cost, and GPT-6 Luna for cost-sensitive, high-volume work. It describes GPT-6.1 Terra as suited to visual reasoning, precise computer use, and clear writing. These are catalog descriptions, not head-to-head benchmark results or guarantees for your application. Test candidates on your own workload before routing production traffic. (OpenAI API model catalog)
This comparison focuses on OpenAI API models for production workloads. It does not compare ChatGPT app access, subscription plans, or app-specific limits; API model IDs and rates here should not be used to infer those details.
Quick comparison
The catalog lists these four models with a 1.05M context window and 128K maximum output. The listed input and output rates are per 1 million tokens, as specified in OpenAI’s API pricing documentation. The use cases below reflect catalog positioning, not independent performance measurements. (OpenAI API model catalog, OpenAI API pricing)
| API model | Model ID | Catalog’s starting guidance | Input / output price per 1M tokens |
|---|---|---|---|
| GPT-6 Astra | gpt-6-astra |
Complex reasoning and coding; demanding work | $10 / $50 |
| GPT-6.1 Sol | gpt-6.1-sol |
Balance intelligence and cost for complex work | $2 / $10 |
| GPT-6.1 Terra | gpt-6.1-terra |
Visual reasoning, precise computer use, and clear writing | $1 / $5 |
| GPT-6 Luna | gpt-6-luna |
Cost-sensitive, high-volume and focused tasks | $0.10 / $0.50 |
The catalog also lists Functions, Web search, File search, and Computer use for each of the four models. It says models are available through the Responses API and Client SDKs. Confirm the exact model and tool compatibility in the relevant documentation for your implementation. (OpenAI API model catalog)
Which model fits your workload?
Consider Luna for focused, cost-sensitive volume
OpenAI describes GPT-6 Luna as an efficient model for focused, high-volume tasks and recommends it for cost-sensitive workloads. Its listed input and output rates are the lowest among the four models compared here. Treat it as a candidate, not an automatic choice: verify that it meets your quality and reliability requirements on representative tasks. Its model ID is gpt-6-luna. (OpenAI API model catalog)
Consider Terra for visual reasoning, computer use, and writing
The catalog describes GPT-6.1 Terra as fast and efficient for visual reasoning, precise computer use, and clear, natural writing. Its model ID is gpt-6.1-terra. These descriptions can help you shortlist it for relevant work, but they do not establish that it will outperform another model on your particular data or workflow. (OpenAI API model catalog)
Consider Sol to balance capability and cost for complex work
OpenAI describes GPT-6.1 Sol as offering near-Astra performance for complex work at a lower cost, and recommends it to balance intelligence and cost. Its model ID is gpt-6.1-sol. The catalog does not provide a benchmark methodology or workload-specific results for that comparison, so validate the trade-off on your own tasks. (OpenAI API model catalog)
Consider Astra for complex reasoning and coding
The catalog recommends GPT-6 Astra as a starting point for complex reasoning and coding, and describes it as OpenAI’s most capable model for demanding work. Its listed input and output rates are higher than the other three models here, while its listed context and maximum-output limits are the same. Consider it when your evaluation shows that its results justify the higher listed rates. (OpenAI API model catalog)
API prices and limits
The listed token-rate estimate is:
Estimated base cost = (input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate).
For example, at Terra’s listed rates, 1 million input tokens and 100,000 output tokens would come to $1.50: $1.00 for input plus $0.50 for output. This is a base token-rate estimate, not an all-in task-cost quote; it excludes any applicable additional charges or pricing adjustments. (OpenAI API pricing, GPT-6.1 Terra model page)
The model catalog lists a 1.05M context window and 128K maximum output for all four models. For budgeting, use the input and output usage expected for your workload, then measure actual API usage in a trial. Confirm applicable charges in the current pricing documentation. (OpenAI API model catalog)
API models are not ChatGPT subscription tiers
This article compares API model IDs, listed API rates, and catalog specifications. It does not establish which models or limits are available in the ChatGPT app, or what any ChatGPT plan costs. Consult current ChatGPT product information for those separate questions. (OpenAI API model catalog)
How to validate a model before production
The catalog offers starting-point guidance, but it does not establish a universal head-to-head winner for every production workload. Run a controlled comparison before committing:
- Choose representative tasks. Include common cases, difficult examples, and cases that tend to fail.
- Keep the test consistent. Use the same inputs, instructions, tools, and acceptance criteria for each candidate.
- Measure accepted outcomes. Track task success, errors, human correction, and retries against explicit criteria.
- Record latency and cost. Measure end-to-end latency and actual API usage for the tested tasks; include retries and any applicable tool or processing charges.
- Set a decision rule. Choose the least expensive candidate that meets your required quality and latency thresholds. Escalate only where your evaluation shows a need.
This is a proposed test method, not a report of results for these models. Keep conclusions bounded to the tasks and conditions you evaluated.
Bottom line
For an API shortlist, start with Luna for cost-sensitive, focused volume; Terra for visual reasoning, computer use, or writing; Sol when balancing intelligence and cost for complex work; and Astra for complex reasoning and coding. Treat these as candidates, not verdicts. Confirm current specifications and rates in OpenAI’s documentation, then choose based on measured results from your own production-relevant evaluation.