Integration guides ยท 2026-06-14

GPT-5.5 API migration guide: integrate via an OpenAI-compatible gateway

When GPT-5.5 API ships, learn how to migrate with a base URL change, choose models through an OpenAI-compatible gateway, track pricing, and control cost with LLMTR examples.

Technical visual of a GPT-5.5 API migration showing an OpenAI-compatible gateway, base URL change, model catalog, pricing tracking, and cost control layers.

What actually changes with a new model?

When a new model like GPT-5.5 ships, most teams really want to know how much of their code must change. With an OpenAI-compatible client, the change is usually just the model identifier and the request target.

On the LLMTR side, the goal is to keep your OpenAI Chat Completions surface and point the base URL at the gateway, selecting the model from the catalog. Trying a new model becomes configuration, not rewriting.

  • The client library stays the same.
  • Only the base URL and model ID change.
  • The new model can be tested without breaking existing flows.
  • Rollback is a one-line configuration change.

Base URL and model identifier for GPT-5.5 API

To try the GPT-5.5 API you can keep your existing OpenAI SDK, set the base URL to the LLMTR gateway, and put the canonical GPT-5.5 identifier from the catalog into the model field.

This makes it easy to test GPT-5.5 next to a previous version in the same codebase, because the only changing field is the model identifier.

  • Verify the canonical model ID from the catalog.
  • Test streaming and tool calling.
  • Check token limits for long-context flows.
  • Confirm the response matches the OpenAI schema exactly.

Separate pricing from cost control

Top-tier models like GPT-5.5 price input and output tokens separately; current unit prices appear on the model catalog and pricing pages, and LLMTR does not change those prices.

The platform margin applies only at credit top-up, not to the model unit price. Keeping this separation clear prevents budgeting mistakes.

  • Model unit price is read from the catalog.
  • Platform margin applies only at credit top-up.
  • Track input and output token cost separately.
  • Reduce output cost with caching or concise-response policies.

Model routing and gradual migration

Instead of moving all traffic to GPT-5.5 at once, gradual migration by task type is safer. Simple tasks can stay on an economical model while complex tasks route to GPT-5.5.

LLMTR usage tracking makes it measurable whether a model choice actually wins on quality and cost, so the decision relies on data rather than guesswork.

  • Map models to task types.
  • Run a quality/cost test with the same prompt set.
  • Define the fallback model in advance.
  • Track per-model cost in the usage screen.

Frequently asked questions

Do I need to rewrite my code to use the GPT-5.5 API?

No. With an OpenAI-compatible client you usually only change the base URL and model identifier; your business logic stays the same.

Is GPT-5.5 pricing different on LLMTR?

Model unit prices are not changed; the catalog prices apply. The platform margin is applied only at credit top-up.

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