Source: https://aiprimetech.io/blog/gpt-6-models-for-codex/

# GPT-6 models for Codex: Astra, Sol or Luna?

The most useful model comparison starts with the work you need completed. A small edit with a clear acceptance test, an ambiguous production bug and a large refactor place different demands on a coding agent.

This is a selection workflow, not a benchmark claiming one model wins every task. Use the same repository state, instructions and validation checks for each model, then compare the result and actual usage.

## Pick an initial candidate

| Your task | Candidate to test first | What to measure |
| --- | --- | --- |
| A bounded edit or extraction | GPT-6 Luna | Correctness against a clear output contract |
| Feature work and routine code review | GPT-6.1 Sol | Complete implementation and validation effort |
| Difficult debugging or interacting constraints | GPT-6 Astra | Whether it resolves the underlying cause |
| An existing GPT-6 Sol integration | GPT-6 Sol, then GPT-6.1 Sol | Regression behavior when changing the model ID |

These starting points follow the roles described in OpenAI's [model-selection guidance](https://developers.openai.com/api/docs/guides/model-selection). Your own workload decides whether a different choice is better.

## Use the exact model IDs

- [GPT-6 Astra](/models/gpt-6-astra/): `gpt-6-astra`.
- [GPT-6.1 Sol](/models/gpt-6-1-sol/): `gpt-6.1-sol`.
- [GPT-6 Luna](/models/gpt-6-luna/): `gpt-6-luna`.
- [GPT-6 Sol](/models/gpt-6-sol/): `gpt-6-sol`.

GPT-6 Sol and GPT-6.1 Sol are separate identifiers. Do not silently replace one with the other inside a comparison. Keep a record of the exact request model, provider, reasoning setting and any service-tier option.

AI Prime Tech has recent completed Responses requests for these four models in its prepaid Codex group. Check the model list for your own key before testing. That evidence establishes recent gateway use; it does not guarantee that every upstream tool or future request is supported.

## Build a small evaluation set

Choose several tasks drawn from your work. Include a straightforward change, a bug with a reproducible failure and a task with an unclear requirement that needs careful interpretation. Give every model the same starting code and a written finish line.

For example, ask each model to fix an input-validation bug without changing the public API. Require the existing checks to pass and include a targeted regression example. Record whether the model found the cause, whether the patch introduced unrelated changes and how much human correction was needed.

Keep the evaluations separate so one model does not inherit another model's solution. Run repository checks from the final result. A persuasive explanation without a working change should not count as success.

## Compare cost per completed task

Per-token pricing is only one input. Record the actual bill for the run, including retries and follow-up prompts. A cheaper request can require more attempts; a more expensive model may still be excessive for a task that a smaller model completes reliably.

The model detail pages link to official standard pricing and explain its limits. For requests through AI Prime Tech, use the dashboard charge for the selected group and plan. Keep cached and uncached work, long context and service options consistent when comparing runs.

For a timed plan, also consider how much useful work you can complete within its active period and rate limits. Do not turn a plan's name into an assumption about throughput or every model's availability.

## Treat context as a budget

Give the agent the relevant code, the failing behavior and the constraints. Adding an entire repository to every request can increase cost and obscure the parts that matter. Ask for evidence from the files it inspected and retain the tests that establish completion.

If you genuinely need a long request, verify the effective limit for the model, client and gateway group. The upstream model's advertised context is a specification, not proof that your full integration will accept a request of that size.

## Configure Codex once, change the model deliberately

Use the custom provider from our [Codex CLI guide](/docs/guides/codex-cli/). Change the `model` value to an exact ID from the list above, then confirm the active model and provider with `/status`.

For an application, the [OpenAI-compatible API guide](/openai-api/) shows a streaming Responses request. Keep the endpoint and credential consistent while changing the model. Test any tools separately before asking the agent to perform a long unattended job.

## A useful decision rule

Choose the lightest model that completes your representative tasks at the quality you need. Keep an escalation path for cases where it fails or the problem becomes more complex. Re-run your examples when changing model versions, prompts or gateway settings.

If you are still choosing between a subscription and API access, read [Codex API key vs ChatGPT subscription](/blog/codex-api-key-vs-chatgpt-subscription/). To start with this gateway, use the [Codex API access page](/codex-api/).