Decision guide · API models

GPT-6 Luna vs Sol vs Astra: which model should you choose?

The choice is less about the largest context window and more about task difficulty, acceptable failure rate and the true cost of your workload. Compare these three documented API models, then test the same real prompts before committing.

This is an API decision guide, not a comparison of ChatGPT subscription limits. Pricing shown below comes from iilib's individually reviewed model records. Their verification dates are displayed per model.

High-volume, bounded tasks

GPT-6 Luna

Start here for classification, extraction, transformations and cost-sensitive workloads.

Lowest listed standard token price of these three; evaluate output quality on your real data.

Model data verified Oct 6, 2026

Most complex production tasks

GPT-6.1 Sol

Start here for coding, multi-step tools and documents where quality and API cost both matter.

A practical middle tier; test against Astra on difficult cases rather than assuming equivalent results.

Model data verified Oct 3, 2026

Demanding reasoning and research

GPT-6 Astra

Escalate tasks that fail your Sol acceptance tests or need deeper reasoning and review.

The premium tier is a quality candidate, not automatically the lowest total cost for every request.

Model data verified Oct 3, 2026

Specifications and standard API token prices

USD per million text tokens, from the current reviewed entity records. These are base/short-context rates, not an invoice quote: long-context requests, cache writes, processing modes, tool calls and regional routing can change the bill.

Model	Input / 1M	Cached input / 1M	Output / 1M	Context	Max output
GPT-6 Luna	$0.10	$0.01	$0.50	1,050,000	128,000
GPT-6.1 Sol	$2	$0.10	$10	1,050,000	128,000
GPT-6 Astra	$10	$1	$50	1,050,000	128,000

A large context limit is a capacity specification, not proof that the model will use an entire long document equally well.

Worked example

1M input + 200K output tokens

Illustrative total across many requests, none exceeding the provider's short-context threshold. No cache hits, no cache writes, no tool fees; standard text rates only.

Replace assumptions with your real workload in the API Cost Calculator →

Decision rule

Route tasks instead of choosing one universal model

Build a small test set of easy, typical and failure-prone requests; define measurable success criteria.
Run Luna first on bounded, high-volume tasks; measure quality and response time.
Escalate harder cases to GPT-6.1 Sol; compare cost per successful result, not just cost per token.
Test Astra on the remaining difficult cases. Keep it only where its quality advantage justifies the extra cost.

Important pricing and feature caveats

Long-context pricing

Some GPT-6 requests above 272K input tokens use different rates for the entire request. Do not multiply the table's short-context rate by a million-token prompt without checking the official pricing rules.

Reasoning and tool compatibility

Luna, Sol and Astra do not expose identical reasoning settings or tool support through every endpoint. For tool-heavy workflows, verify the specific model's Responses API documentation.

Quality is not a fixed ranking

Provider positioning is a starting point. No universal performance score is asserted here; evaluate pass rates, retries, latency and human review cost for your use case.

Old Sol vs current Sol

The model GPT-6 Sol is distinct from GPT-6.1 Sol. This guide evaluates the newer Sol version.

Primary sources and next steps

Check the official specifications when deploying, and use the model links above for iilib's source records and verification dates.

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