Call Several AI Providers Through One Endpoint

Trying another AI provider should be a small experiment, not a weekend spent rebuilding your application. For a personal project or coursework assignment, start with a narrower goal: send one useful request, repeat it with another model, and compare the results before spending more.

This walkthrough keeps the connection work separate from the model choice. You will establish one working connection, run a controlled comparison, and decide when a second provider actually helps.

Choose a small task with a clear finish

First, pick something you can judge without another model grading the answer. You might ask for an explanation of a short function, a summary of your own lecture notes, or three test cases for a simple calculation. Avoid sending an entire repository when a small excerpt will answer your question.

Write down what success means before making the request. For a function explanation, that could mean identifying the inputs, describing the output, and catching one edge case. Give both models the same material and the same requested answer length. This makes the comparison easier to interpret.

Establish one key and a modest budget

Create a MyRelay account, choose a small testing budget, and generate an API key in your dashboard. MyRelay provides one key and a shared budget, while the dashboard lets you create or retire keys. Keep your first experiment deliberately small rather than funding a month of hypothetical usage. ([myapirelay.com](https://myapirelay.com/))

Treat the key as a password. Keep it out of public repositories, screenshots, shared notebooks, and submitted coursework. If you accidentally expose it, retire it and replace it before continuing. Make the budget a deliberate decision, not something you revisit only after a long testing session.

Connect your client before changing models

Next, choose one client and get a single request working. MyRelay supports OpenAI-style clients through its common API address, using your relay key. Follow the connection instructions for your chosen client rather than borrowing settings from a different tool. ([myapirelay.com](https://myapirelay.com/how-to-connect?hl=en))

There is an important distinction here: one relay address does not mean every provider uses an identical request format. MyRelay documents separate connection requirements for OpenAI-style, Anthropic-style, and native Gemini requests. Establish the correct interface first; do not assume that changing a model name makes incompatible request formats interchangeable. ([myapirelay.com](https://myapirelay.com/how-to-connect?hl=en))

For the first check, send only your small test prompt. Confirm that an answer arrives and that you can identify the model requested. Leave streaming, attachments, and tool use for later so that connection problems stay easy to isolate.

Repeat the request with another provider

Once the first call works, select a second model supported by the same interface. A comparison might use GPT-6.1 Sol and DeepSeek V4 Flash. Check the current menu and connection guidance for the exact model identifiers rather than guessing them from display names.

Keep the relay credentials and compatible connection settings unchanged. Change only the model selection, then resend the original prompt. Compare both answers against your success criteria. Look for correctness and useful detail, not merely confidence or length.

Do not send every future request to both models by default. Instead, use this first comparison to decide whether either model deserves a particular role in your project.

Inspect the bill alongside the answer

After each test, review the recorded usage. MyRelay charges requests according to the tokens actually used, including applicable cache activity, and provides a record of where tokens went. Use that record to connect each experiment with its cost. ([myapirelay.com](https://myapirelay.com/how-to-connect?hl=en))

Record whether the answer succeeded, whether you needed a retry, and what the completed task cost. A cheap response that requires repeated corrections may not be your cheapest usable result. Before expanding the experiment, shorten unnecessary context and ask whether the requested answer needs to be as long.

Turn the comparison into a repeatable habit

Finish by choosing a default model for your tested task and a specific reason to try the alternative. Keep a short comparison note and repeat the test when your requirements change. One endpoint is most useful when it reduces connection work while leaving model choice deliberate, measurable, and within your budget.