One API for GPT, Claude, Gemini, and DeepSeek: A Hands-On Guide for Developers

If you’re building an AI app, you’ve probably run into this: you want GPT for one kind of task, Claude for another, and you’d like to try Gemini. When cost matters, you also want to compare DeepSeek.

Different models really are good at different tasks.

Finding these models isn’t hard. The real question is how to connect all of them to your app.

You may need to manage different API keys, accounts, and billing dashboards, and sometimes use different SDKs too.

At RequestBeam, we’ve been working to make this process a little simpler. We provide one platform where developers can access multiple AI models through a shared API and balance.

But what does that look like in code? Can a unified API really make development easier?

In this article, we’ll start with the actual setup, show how to switch models, measure costs and response times, and cover what to check before going live.

1. A real-world setup: multiple models, multiple integrations

Let’s say we’re building a small AI app.

We plan to use:

• GPT: for general tasks and structured responses.
• Claude: for writing, coding, and longer tasks.
• Gemini: for multimodal tasks.
• DeepSeek: as another option to test for cost and output quality.

If we connect to each provider’s official API separately, we have four sets of API credentials, pricing pages, and integration methods to deal with.

Here’s a simple comparison:

That last row matters.

One API doesn’t mean every AI model behaves exactly the same way.

Models may differ in the parameters, context lengths, tool calls, response formats, and input types they support. A unified API can make integration simpler, but we still need to know what each model actually supports.

2. Get started with RequestBeam

We only need three things:

1. A RequestBeam account.
2. A RequestBeam API key.
3. A model ID that’s currently available.

You can check supported models and current prices on the website:

• RequestBeam model list
• RequestBeam pricing page
• RequestBeam API docs

For integrations compatible with the OpenAI SDK, RequestBeam provides this base URL:

https://api.requestbeam.com/openai/v1

Note that this isn’t the same address as our general HTTP chat endpoint:

POST https://api.requestbeam.com/v1/chat-completions

In the SDK examples below, we’ll use the OpenAI-compatible endpoint.

Step 1: Install the SDK

Python:

pip install --upgrade openai

Node.js:

npm install openai

Step 2: Set your API key

We recommend putting your API key in an environment variable rather than hardcoding it.

export REQUESTBEAM_API_KEY="your-api-key"
export REQUESTBEAM_MODEL="your-supported-model-id"

Replace your-supported-model-id with the actual ID from the current model list.

We suggest copying the model ID directly instead of guessing from the model’s marketing name. The API identifier may not exactly match the name you see on the model’s official website.

3. Make your first API call with Python

Here’s a basic Python example using the OpenAI SDK.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["REQUESTBEAM_API_KEY"],
    base_url="https://api.requestbeam.com/openai/v1",
)

response = client.chat.completions.create(
    model=os.environ["REQUESTBEAM_MODEL"],
    messages=[
        {
            "role": "user",
            "content": "Explain what an API is in one sentence."
        }
    ],
)

print(response.choices[0].message.content)

That’s the basic setup.

We only need to set the API key and base URL once, then send chat requests using a supported model.

What should come back?

If the request succeeds, we’ll get a response containing the model’s generated content.

For example, the model might explain: “An API is a way for two software applications to communicate with each other.”

The exact wording will vary by model and request.

If the request fails, start by checking:

• Whether the API key is valid.
• Whether the selected model ID is available.
• Whether the endpoint supports the operation you’re requesting.
• Whether the account has enough balance.
• Whether any request parameters are unsupported.

We don’t recommend blindly retrying every error. Authentication failures, incorrect model names, and temporary rate limits need different approaches.

4. Switch between GPT, Claude, Gemini, and DeepSeek

Now for the part we find most useful: testing different models while changing as little app code as possible.

Instead of writing four separate API integrations, we can start with a function that takes a model ID.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["REQUESTBEAM_API_KEY"],
    base_url="https://api.requestbeam.com/openai/v1",
)

def ask_model(model_id, question):
    response = client.chat.completions.create(
        model=model_id,
        messages=[
            {
                "role": "user",
                "content": question
            }
        ],
    )

    return response.choices[0].message.content

Now we can call the same function with different model IDs.

question = "Explain REST APIs to a beginner."

model_ids = {
    "GPT": os.environ["GPT_MODEL_ID"],
    "Claude": os.environ["CLAUDE_MODEL_ID"],
    "Gemini": os.environ["GEMINI_MODEL_ID"],
    "DeepSeek": os.environ["DEEPSEEK_MODEL_ID"],
}

for name, model_id in model_ids.items():
    print(f"\n--- {name} ---")

    try:
        answer = ask_model(model_id, question)
        print(answer)
    except Exception as error:
        print(f"Request failed: {error}")

Before running this, find four valid, available model IDs in the latest model list and set the matching environment variables:

export GPT_MODEL_ID="supported-gpt-model-id"
export CLAUDE_MODEL_ID="supported-claude-model-id"
export GEMINI_MODEL_ID="supported-gemini-model-id"
export DEEPSEEK_MODEL_ID="supported-deepseek-model-id"

The goal is to reuse the same request function instead of writing a separate SDK integration for each model family.

One thing to keep in mind: The models you choose must support the OpenAI-compatible chat endpoint and the specific parameters you send. Some advanced features may need other endpoints.

5. Let the test data speak

Of course, one successful API request only tells us that the endpoint works. It doesn’t prove the service is fast enough, reliable enough, or affordable enough for production.

So let’s run a small test for our own app.

Send the same prompt to all four models and record:

• Whether the request succeeds.
• How long each request takes.
• The number of input tokens.
• The number of output tokens.
• What the model actually returns.

For the first round, we use this prompt:

Explain how HTTPS protects data between a browser and a server. Use fewer than 100 words.

It’s a fairly simple question, but it’s a good starting point to check whether the same request format works across different models.

Python test script

import os
import time
import json
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["REQUESTBEAM_API_KEY"],
    base_url="https://api.requestbeam.com/openai/v1",
    timeout=60.0,
    max_retries=0,
)

models = {
    "GPT": os.environ["GPT_MODEL_ID"],
    "Claude": os.environ["CLAUDE_MODEL_ID"],
    "Gemini": os.environ["GEMINI_MODEL_ID"],
    "DeepSeek": os.environ["DEEPSEEK_MODEL_ID"],
}

prompt = (
    "Explain how HTTPS protects data between "
    "a browser and a server. Use fewer than 100 words."
)

results = []

for provider, model_id in models.items():
    for run in range(5):
        start = time.perf_counter()

        try:
            response = client.chat.completions.create(
                model=model_id,
                messages=[
                    {"role": "user", "content": prompt}
                ],
                temperature=0,
            )

            elapsed = time.perf_counter() - start
            usage = response.usage

            results.append({
                "provider": provider,
                "model_id": model_id,
                "run": run + 1,
                "success": True,
                "latency_seconds": round(elapsed, 3),
                "input_tokens": (
                    usage.prompt_tokens if usage else None
                ),
                "output_tokens": (
                    usage.completion_tokens if usage else None
                ),
                "answer": (
                    response.choices[0].message.content
                ),
            })

        except Exception as error:
            elapsed = time.perf_counter() - start

            results.append({
                "provider": provider,
                "model_id": model_id,
                "run": run + 1,
                "success": False,
                "latency_seconds": round(elapsed, 3),
                "error": str(error),
            })

with open("requestbeam-test-results.json", "w") as f:
    json.dump(results, f, indent=2)

print(json.dumps(results, indent=2))

We set max_retries=0 because we want to see whether each individual request fails, rather than letting the SDK hide errors with automatic retries. This is for measurement; it doesn’t necessarily mean you should use the same setting in production.

We also set temperature=0 to reduce variation in the output where the model supports it. If a model doesn’t accept this parameter, remove it for that model and record the difference.

Here are the test results:

6. Compare API costs using actual token usage

One big reason AI API pricing can be confusing is that input and output tokens usually have different prices.

Some models also offer cached input pricing, which can further affect the final cost.

For text APIs, we usually start with this formula:

Estimated cost = (input tokens x input price + output tokens x output price) / 1,000,000

This assumes both prices are per million tokens and all usage is charged at the standard, non-cached rate.

Try a simple cost calculation

Let’s say our app makes 100,000 requests a month.

We estimate that each request uses an average of:

• 1,000 input tokens.
• 300 output tokens.

That gives us this monthly usage:

Next, let’s compare two sets of hypothetical prices.

In this example, B is 50% cheaper than A.

With the same token usage, that’s a difference of $220 a month.

Before choosing a provider, we’d put the model’s latest prices into the formula and check platform fees, caching rules, minimum charges, and any other billing terms.

You can find RequestBeam’s current model prices on the pricing page.

Why does actual token data matter?

Different models may generate different numbers of output tokens, even when answering the same question.

This matters because output tokens often cost more than input tokens.

We suggest testing models with your own real prompts and using the API’s returned usage data to check consumption whenever possible.

There’s another easy-to-miss detail: different model families may use different tokenizers, so their reported token counts aren’t always directly comparable.

That’s why, rather than simply asking “Which model uses fewer tokens?”, we care more about: How much were we actually charged to complete the same fixed set of tasks?

7. What else do we check before going live?

For a demo, getting a response from the model is usually enough to complete the first step.

But putting an app in front of real users is different.

We pay more attention to failed requests, budget overruns, security issues, and whether the system behaves consistently and predictably.

Here are a few things we suggest checking before sending real business traffic.

Error handling

We distinguish between:

• Authentication problems, such as an invalid API key.
• Rate limits or temporary capacity shortages.
• Unsupported models or request parameters.
• Timeouts and network errors.
• Server-side failures.

We only retry when another attempt has a chance of succeeding, with a limited number of retries and reasonable backoff delays.

Model compatibility

We don’t assume every model supports every feature.

Before using tool calling, structured JSON output, image input, long context, or streaming responses, we check what the specific model and endpoint support.

Cost control

We recommend tracking the cost of each main business task.

For example, if we’re building a content generation tool, we should focus on “How much does it cost on average to generate a complete document?”, not just the listed price per million tokens.

A cheap model that needs three extra retries may not save money in the end.

Security and data handling

We keep API keys on the server to reduce the risk of exposure and rotate them promptly when needed.

Before sending customer data to any third-party API platform, including RequestBeam, we also read its data handling and retention policies.

Compatible APIs don’t mean identical privacy terms and service commitments.

Reliability

For critical workloads, we suggest testing your backup plan for failures and checking whether the fallback model can handle the same inputs.

Failover is only useful when the backup model is actually compatible with the task.

8. When does a unified API make sense?

We think a unified API is worth considering when:

• You’re building a small AI SaaS and want to compare models.
• You’re testing GPT, Claude, Gemini, and DeepSeek.
• You don’t want to maintain several separate billing accounts.
• Your product is changing quickly, and you want to simplify integration first.
• You want to choose different models for different tasks based on actual test results.

But a unified API isn’t always the best choice.

Using the official API directly may be a better fit if you need a new feature the model provider just released that the gateway doesn’t support yet, need a direct contract with the model provider, rely on the provider’s dedicated support, or need advanced controls that the compatible API doesn’t expose.

Likewise, if your team already runs a mature in-house gateway that meets your needs for reliability, monitoring, and model integration, switching platforms may not offer much benefit.

We’d rather developers compare options based on their real needs, not just marketing claims.

10. Frequently asked questions

Can I use one API key to call GPT, Claude, Gemini, and DeepSeek?

RequestBeam provides a shared API key and platform balance for supported models. You still need the exact model ID from the current model list and the appropriate supported endpoint for each call.

Do I need to change my existing OpenAI SDK code to use RequestBeam?

For supported OpenAI-compatible chat requests, you can usually keep using the OpenAI SDK and only change the base URL, API key, and model ID.

But if you use advanced features specific to a provider, you may still need to change your code.

Is a unified API always cheaper than connecting directly to the official API?

That depends on the model, token usage, cached pricing, platform fees, and actual workload.

We suggest comparing real bills for the same set of tasks rather than assuming all gateways are cheaper.

But RequestBeam is cheaper than the official APIs and offers better quality than other AI APIs.

Can I switch models dynamically while my app is running?

Yes. Your app can choose a different supported model ID for each request.

But you still need to handle differences in model parameters, responses, and features.

Does RequestBeam only support text models?

No.

RequestBeam also offers image, video, and some data API services.

These services may use different endpoints and request formats, so you can’t call them the same way as a text chat API.

A final thought

Using multiple AI models shouldn’t mean maintaining four completely separate integration systems.

At RequestBeam, our goal is to make the shared parts simpler: one platform, one balance to manage, and a unified way to access supported models.

But we also believe measuring real results matters most.

Before choosing a model or service provider, we suggest answering three questions:

1. Does it work? Test the API features your app actually needs.
2. What does it cost? Calculate costs from actual usage, not just advertised prices.
3. How does it perform? Use your own prompts to compare latency, failures, and output quality.

We’ve provided code and a simple testing workflow above, so you can check for yourself with your own account.

To get started, take a look at our model list, check the latest prices, and follow the API docs.

We’d love to see what you build with it.

The RequestBeam Team