GPT-6 Astra guides · 2026-09-05
How to Use GPT-6 Astra API Through LLMTR
Send your first GPT-6 Astra request through LLMTR with Responses API, choose a supported reasoning level and inspect output usage before scaling up.
Prepare the model and credentials
This guide uses the paid LLMTR Astra identifier openai/gpt-6-astra. Use an LLMTR API key, a funded account and the LLMTR service origin as LLMTR_BASE_URL. Store the key in LLMTR_API_KEY in the process environment; do not paste it into source code or browser JavaScript.
Confirm the current model listing before the first request. A provider announcement is not proof that every account or gateway supports every advertised tool. This guide uses the text request documented by LLMTR, with no assumption about browser or computer automation.
Send a Responses API request
Run this Bash example after setting both environment variables. Start with one small, non-sensitive task. The explicit high reasoning setting makes your intended configuration visible; it is a starting choice for evaluation, not a promise of optimal cost or quality.
LLMTR Responses API — environment variables required
curl --fail-with-body "$LLMTR_BASE_URL/v1/responses" \
-H "Authorization: Bearer $LLMTR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"openai/gpt-6-astra","input":"Explain three ways to evaluate a document summary.","reasoning_effort":"high","max_output_tokens":4096}'Choose a supported reasoning level
Astra supports low, medium, high, xhigh and max. It does not accept none or minimal. In LLMTR, the :max suffix requests real max reasoning for this model. Do not copy unsupported levels from a different model’s configuration.
Choose one way to express the setting. If multiple forms are present, LLMTR’s documented precedence is reasoning_effort, then reasoning.effort, then the model suffix. An old explicit setting can therefore override a suffix that you just changed.
Inspect output and usage before retrying
Check the HTTP status, response status, returned text and usage. Do not assume a successful HTTP response means the requested answer is complete. A tight output budget can be consumed by reasoning, leaving little or no visible text; inspect the response before deciding to raise the budget.
For an authentication failure, check the key and service origin. For insufficient credit, check the account balance. For invalid parameters, correct the request rather than repeating it unchanged. Retries can create additional billable work, so set a bounded retry policy in your application.
Move an existing client carefully
LLMTR also documents Chat Completions compatibility for Astra. If your client uses that interface, keep its existing response parser and follow the linked documentation’s request format. Do not pass a Responses payload unchanged to a Chat Completions endpoint.
The model does not support temperature and top_p tuning. LLMTR documents removing these unsupported fields and reporting the adjustment. Review client defaults rather than relying on parameters that will not affect the result.
Before increasing traffic, compare a representative set of outputs with your acceptance criteria and review actual token usage using the pricing guide. Keep the model identifier and reasoning setting explicit so the configuration can be reproduced.
Frequently asked questions
Can I disable Astra reasoning with none?
No. Choose low, medium, high, xhigh or max. A low setting is still reasoning; it is not an off switch.
Is the example calling OpenAI directly?
No. It calls the LLMTR origin you supply, uses an LLMTR key and names the model openai/gpt-6-astra.