GPT 6 models compared: GPT 6.1 Sol, Astra, Sol, Luna, and GPT 5.6

The current GPT model map

The live catalog checked on October 4, 2026 returned gpt-6.1-sol, gpt-6-astra, gpt-6-sol, gpt-6-luna, gpt-5.6-sol, gpt-5.6-terra, and gpt-5.6-luna. This article does not turn those IDs into invented prices or benchmark claims. Supplier availability and commercial terms can change, so check the models page before you lock a configuration.

Comparison table

The table is a test plan. It is not a ranking. A model that wins on a public coding example can lose on your support policy or JSON schema.

How to choose between GPT 6 and GPT 5.6

Start by writing down what failure costs. A typo in a draft title is cheap. A wrong migration plan, account action, or code review finding is not. Put the strictest checks on the expensive workflows, then test the least capable route against the same fixtures.

Keep old model IDs in a separate compatibility route while you migrate. This gives you a clear rollback target. Do not change the prompt, parser, and model in the same deploy. If the result changes, you will not know which variable caused it.

Build a small model harness

Use a fixture file with real anonymized requests. Each case should include the expected shape, a pass rule, and a note about what the model must refuse. Run the harness for every candidate with identical generation settings.

const candidates = [
  "gpt-6.1-sol",
  "gpt-6-astra",
  "gpt-6-sol",
  "gpt-6-luna",
  "gpt-5.6-sol",
  "gpt-5.6-terra",
  "gpt-5.6-luna",
];

for (const model of candidates) {
  const result = await openai.chat.completions.create({
    model,
    messages: [{ role: "user", content: fixture.prompt }],
    response_format: { type: "json_object" },
  });
  await saveResult({ model, result, fixtureId: fixture.id });
}

The exact response format depends on the SDK and endpoint route. Keep credentials in environment variables and never put a live key in the fixture repository.

Routing patterns that hold up

For a mixed application, send quick labels and simple transforms to gpt-6-luna or gpt-5.6-luna. Send general requests to gpt-6-sol or gpt-6.1-sol. Reserve gpt-6-astra for a queue with a clear reason to spend more time on a difficult case.

For code, route by risk. A testable one-file change can use a Sol or Luna model. A cross-service change should go through a quality-first route, then a human checks the diff and test output. The model does not replace the build.

A migration checklist

1. Pin the current model ID in configuration.
2. Save a fixture set from real, anonymized requests.
3. Run the old and new models with the same prompt and parser.
4. Compare pass rate, parse failures, latency, and token usage.
5. Move a small traffic slice before changing the default.

The most useful dashboard separates model errors from application errors. A timeout, a schema failure, and an incorrect answer need different fixes.

FAQ

Which GPT model should I try first?

Start with gpt-6.1-sol for a mixed general workload. Test gpt-6-sol and gpt-6-astra on the cases where correctness matters most, then test Luna on the simple queue.

Is GPT 6.1 Sol always better than GPT 5.6 Sol?

The model ID does not prove that. Run the same fixtures and keep the result with the better task score for your use case.

Can one API key call all GPT IDs?

The current Claudexia catalog exposes these IDs through the same service. Your account permissions and the live catalog remain the source of truth before a deploy.

Should I use Luna for code review?

Use it only after a review fixture set shows acceptable findings and misses. For risky merges, pair the model with tests and a human approval step.

Where can I check current availability?

See the models documentation and the API response for the current catalog. Do not hard-code a model that is absent from that response.