GPT API Models: GPT-6 Astra and GPT-5.6 Tiers

How to choose a GPT API model

Start with the smallest model likely to meet the workload. Compare it with one higher-capability candidate on the same tasks, then choose by accepted-result quality, latency, reliability, and total cost.

• Choose Astra for the hardest end-to-end work across reasoning, software engineering, research, browsers, and professional software.
• Choose Sol for complex professional work when Astra's higher per-token rate is not justified.
• Choose Terra for a quality-cost balance, and Luna for evaluated high-volume work.

GPT API capabilities

• GPT-6 Astra and the GPT-5.6 family support text and image input with text output.
• All four list a 1.05M context window and up to 128K output.
• Astra introduces async tool calling, mid-turn steering, and configuration updates; it also supports the Responses API tools listed on its model page.
• Astra uses low through max reasoning; GPT-5.6 also allows none.

GPT pricing and context questions

Do not compare only the headline input rate. Include output, cache reads and writes, long-context tiers, reasoning tokens, tool calls, retries, and the amount of history resent on every turn.

• Start with medium reasoning, then increase it only when the evaluation set shows a quality gain.
• Trim repeated conversation history even when the context window is large.
• Record latency, input, output, tool calls and retries per completed task—not per isolated request.

Which workloads fit GPT?

• Repository-scale coding and code review.
• Long-document synthesis with citations verified by your application.
• Tool-using agents that need structured calls and recovery logic.
• High-volume transformation and extraction on Terra or Luna.

GPT API limits and migration risks

• LLMFly AI fully supports GPT-6 Astra API use. Configure each GPT model with the endpoint and tools documented for that exact route.
• Large context can raise latency and cost; it is not a substitute for retrieval and context selection.
• Keep model IDs in configuration and test a fallback before production rollout.

Move from model research to a usable model ID

Open a model page above, note its provider model ID, and then use Model Plaza to confirm the ID available to your API key. Store that ID in configuration so it can be reviewed and changed without rewriting the application.