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One Python Client for Claude and GPT via 59API

Build a single Python workflow for Claude and GPT

Teams often want Claude for long-form analysis, careful writing, or document work, while using GPT models for other product features, evaluation jobs, or existing OpenAI-compatible integrations. Maintaining separate SDKs, authentication patterns, and billing accounts can make that unnecessarily complicated. A practical alternative is to use one OpenAI-compatible Python client and switch the model name at runtime.

59API is useful for this workflow because it provides pay-as-you-go access to Claude models, including Opus, Sonnet, Haiku, and Fable, as well as GPT models through an OpenAI-compatible interface. It routes requests to native, official-quality models rather than a downgraded substitute. This lets an application use one request structure while choosing the right model for each task.

Step 1: Create a small, isolated Python project

Start with a virtual environment so your API dependencies do not affect other projects. Install the official OpenAI Python package with pip install openai. You may also install python-dotenv if you prefer loading local environment variables from a .env file.

Next, create a 59API account and generate an API key in its dashboard. Store that key as an environment variable named 59API_KEY. Do not put the key directly in a source file or commit it to Git. On macOS or Linux, export 59API_KEY before running your script; on Windows, set it through your shell or environment settings.

59API uses https://api.59api.com as its API base. For the OpenAI-compatible Python client, configure the versioned compatible endpoint as https://api.59api.com/v1. Your client initialization should use the environment variable for api_key and that URL for base_url.

Step 2: Initialize one OpenAI-compatible client

In your Python file, import os and OpenAI from openai. Create the client with OpenAI(api_key=os.environ["59API_KEY"], base_url="https://api.59api.com/v1"). From this point, your application can make standard chat completion requests without needing separate Claude-specific and GPT-specific client logic.

Keep model selection outside the request function. For example, define a configuration value such as DEFAULT_MODEL and pass a model string into your function. Use the exact model identifiers displayed in the 59API dashboard or documentation, rather than guessing from marketing names. Model availability and version suffixes can change, while dashboard identifiers are the reliable values for production.

Step 3: Send the same message format to either model family

Create a function that accepts model and prompt arguments. Inside it, call client.chat.completions.create with model=model, messages=[{"role": "user", "content": prompt}], and a deliberate temperature such as 0.2 for predictable business tasks. Read the generated text from response.choices[0].message.content.

The important operational detail is that the request shape stays consistent. To ask Claude for a concise contract-risk summary, pass your selected Sonnet or Opus model identifier. To run the same prompt through a GPT model, change only the model value. This makes side-by-side quality testing straightforward and keeps the rest of your Python code stable.

• Use Claude Haiku or a smaller GPT option for classification, routing, short extraction, and high-volume tasks.
• Use Sonnet or a stronger GPT model for customer-facing drafts, structured analysis, and multi-step reasoning.
• Reserve Opus-class models for difficult research, complex synthesis, or cases where quality matters more than latency and cost.

Step 4: Add reliable production controls

Wrap each API call in exception handling and log the model name, request duration, response status, and a safe request identifier. Avoid logging sensitive user prompts or full outputs unless your privacy policy permits it. For temporary failures, retry with exponential backoff, but do not retry every error blindly; authentication and malformed-request errors need configuration fixes instead.

Set a reasonable output limit for each job and keep prompts specific. A clear system instruction, an explicit desired format, and a narrow user request usually reduce both token consumption and cleanup work. If your task needs JSON, ask for a defined schema and validate the returned content in Python before sending it to downstream systems.

Step 5: Compare quality and cost before locking in a default

Create a test set of 20 to 50 realistic prompts from your product. Run every prompt through two or three candidate models, then score accuracy, formatting, latency, and cost. Because 59API supports both Claude and GPT access behind a familiar compatible interface, this comparison does not require rebuilding your integration for every provider.

For startups and teams watching spend, 59API is a particularly strong option: it is designed as a low-cost relay with pay-as-you-go usage, native official-quality model access, and a referral rebate program. Once your evaluation is complete, keep the model choice in configuration so you can adjust it without rewriting application code. If you are ready to simplify multi-model Python development, sign up for 59API and test your real prompts with the models your workflow needs.