OpenAI GPT models

Choose the right model in the family

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.

Inputs, outputs, context and tools

• 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.

Where this family fits

These are strong starting points—not universal recommendations. Test them with your own prompts, tools and acceptance criteria.

• 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.

Reasoning, context and cost controls

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.

Send a minimal request

This example uses the first provider model ID in the table. If Model Plaza shows a different ID for your API key, copy the Model Plaza value exactly.

curl https://app.llmfly.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMFLY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6-astra",
    "messages": [{"role": "user", "content": "Explain this request in three bullets."}],
    "stream": false
  }'

Limits and compatibility notes

• 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.

Production evaluation checklist

• Run 20–50 representative tasks and define pass criteria before testing.
• Measure time to first token, total latency, input and output tokens, tool retries, and cost per successful task.
• Test the endpoint, streaming mode, tool schemas, and structured output used by the application.
• Verify 400, 401, 404, 429, and temporary 5xx handling with bounded retries.
• Keep the model ID in configuration and validate a fallback before moving production requests.

What to confirm in Model Plaza

Model Plaza shows which model IDs your API key can use, their billing multipliers, availability, supported endpoints, and compatibility notes. Confirm these details with a small request before deployment.

• GPT-6 Astra and the GPT-5.6 models accept text and image input and return text.
• Astra supports five reasoning levels from low through max; the GPT-5.6 models also support none.
• OpenAI publishes function calling, structured outputs, web and file search, computer use, and additional Responses API tools for Astra.

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.