GPT-5.6 API Models: Sol vs Terra vs Luna

How to choose a GPT-5.6 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 Sol for the hardest coding, research and long-running professional workflows.
• Choose Terra when quality still matters but the workload needs a lower unit cost.
• Choose Luna for classification, extraction, routing and other high-volume work after task-specific evaluation.

GPT-5.6 API capabilities

• Text and image input with text output across the current GPT-5.6 family.
• A 1.05M context window and up to 128K output support large repositories and document sets.
• Function calling, web search, file search and computer use are published tool options.
• Reasoning can be adjusted from none to max so latency and depth can be tuned per task.

GPT-5.6 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-5.6?

• 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-5.6 API limits and migration risks

• A published capability does not guarantee that every LLMFly AI route exposes the same endpoint or tool.
• 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.