Short answer: There isn’t one best model for every task or account. In ChatGPT, the available model depends on your plan and workspace: Free and Go use GPT-5.6 Luna for Instant, while GPT-6 is rolling out to eligible paid plans. For API use, OpenAI positions Luna for focused, high-volume work, Sol for complex work at lower per-token cost than Astra, and Astra for its most demanding tasks. Treat those as starting points, then test candidates on your own workload. (help.openai.com)
First decide: ChatGPT or the API?
ChatGPT model choices and API model IDs are separate contexts. In ChatGPT, Free and Go use GPT-5.6 Luna for Instant. GPT-6 is rolling out to eligible Pro, Plus, Enterprise, and Business accounts, and options can depend on plan and workspace settings. GPT-6 Pro, powered by GPT-6 Astra, is available on eligible plans and accounts; check your model menu to confirm availability. (help.openai.com)
For API use, the model catalog lists gpt-6-luna, gpt-6.1-sol, gpt-6.1-terra, and gpt-6-astra. The Luna and Terra model pages list text and image input. For Sol and Astra, check the specific model page for the inputs your application requires. ChatGPT subscriptions and API billing are separate. (developers.openai.com)
Choose by task
| Your task | ChatGPT starting point | API candidate | What to check |
|---|---|---|---|
| Everyday questions and routine work | Use Instant if available; Free and Go use GPT-5.6 Luna for Instant | gpt-6-luna |
OpenAI positions Luna for focused, high-volume tasks. Test it against your quality bar. (help.openai.com) |
| Difficult analysis, coding, or multi-step professional work | Try a higher GPT-6 thinking level if available; consider GPT-6 Pro if your plan includes it | gpt-6.1-sol; compare with gpt-6-astra for the most demanding work |
Sol is positioned for complex work at lower per-token cost than Astra; Astra is positioned for the most demanding work. Compare task success, latency, and total cost. (help.openai.com) |
| Visual reasoning or computer-use workflows | Check which models and tools are available in your ChatGPT workspace | gpt-6.1-terra |
OpenAI lists computer use for Terra through the Responses API. Confirm the required tool is enabled; tool-specific models may also incur a per-call fee. (developers.openai.com) |
| Cost-sensitive, high-volume API requests | Use an available efficient option and check your plan’s limits | gpt-6-luna |
Compare completed-task cost and quality, not just the input-token rate. (developers.openai.com) |
These are candidates based on OpenAI’s descriptions, not head-to-head results for your application. Performance depends on the task, prompt, tools, and quality criteria. If a model’s failure would be costly, test it against a stronger candidate before selecting it.
What API prices mean in practice
OpenAI lists standard API rates in U.S. dollars per million tokens for these models. Standard rates apply to prompts under 272,000 input tokens; other processing modes, long prompts, caching, and tools can change the bill. Reasoning tokens are billed as output tokens, so count them within the output total rather than adding them again. Check the current rate card before budgeting. (developers.openai.com)
| API model | Standard input | Standard output |
|---|---|---|
GPT-6 Luna (gpt-6-luna) |
$0.10 / 1M tokens | $0.50 / 1M tokens |
GPT-6.1 Terra (gpt-6.1-terra) |
$1.00 / 1M tokens | $5.00 / 1M tokens |
GPT-6.1 Sol (gpt-6.1-sol) |
$2.00 / 1M tokens | $10.00 / 1M tokens |
GPT-6 Astra (gpt-6-astra) |
$10.00 / 1M tokens | $50.00 / 1M tokens |
Illustrative estimate, not a model test: If API usage totals 100,000 input tokens and 20,000 output tokens—including any billed reasoning tokens—estimated standard costs are $0.02 on Luna, $0.20 on Terra, $0.40 on Sol, and $2.00 on Astra. This assumes prompts under 272,000 input tokens and no tool or other charges. Actual costs depend on token usage and applicable pricing conditions. (developers.openai.com)
How to choose a production API model
Don’t route production traffic to a model solely because it is described as most capable or cost-effective. Run a small, repeatable comparison first:
- Use representative inputs. Include routine requests and important edge cases from your application—for example, a report summary, code fix, chart question, or structured extraction if those reflect your real workload.
- Set acceptance criteria before testing. Define what counts as a successful answer, such as required facts being present, a valid output schema, or code passing specified tests.
- Compare candidates consistently. Keep prompts, tools, and settings the same; record failures as well as successful outputs.
- Measure operational trade-offs. Track quality against your criteria, latency, token use, total task cost, and error rate.
- Choose a candidate that meets your requirements. If a lower-cost model misses important cases, use a stronger candidate for those requests or as a fallback. Retest when your prompts, tools, or requirements change.
This is a test plan, not a claim that these models were evaluated for this article. OpenAI’s migration guidance likewise recommends establishing a baseline with representative requests and recording quality, latency, and cost before a production migration. (help.openai.com)
Bottom line: In ChatGPT, start with the option your account actually offers: Free and Go use GPT-5.6 Luna for Instant; eligible accounts may have GPT-6 options, and eligible plans may offer GPT-6 Pro. For the API, consider Luna for focused, high-volume work, Sol for complex work at lower per-token cost than Astra, Terra for documented visual reasoning and computer-use support, and Astra for demanding tasks. Verify required inputs and tools, estimate cost from current rates, and make the final choice with a workload-specific test—not a universal ranking. (help.openai.com)
Sources
GPT-6 and other models in ChatGPT
GPT-6.1 Terra Model | OpenAI API
GPT-6.1 Sol Model | OpenAI API