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Calling Claude and GPT from Python: 2026 Playbook

One Python Client for Claude and GPT

Calling Claude and GPT from Python does not have to mean maintaining separate integrations. An OpenAI-compatible relay lets you reuse a client, authentication setup, and request structure while changing the model identifier. This 2026 guide uses 59API to demonstrate that workflow, with practical safeguards for credentials, compatibility, reliability, and spending.

59API provides low-cost, pay-as-you-go access to multiple model providers through https://api.59api.com. It supports the OpenAI SDK and developer tools including Claude Code, Codex, and Gemini CLI. Its stated offering uses native, official-quality models without downgrades. For Python applications, the immediate benefit is simpler integration and a single place to manage usage.

1. Install the SDK and Configure Credentials

Create a virtual environment with python -m venv .venv, activate it, then install the client with python -m pip install --upgrade openai. Once your integration passes testing, pin the tested SDK version in your project's dependency file.

Create a 59API account, generate an API key, and configure these environment variables in your shell, IDE, or deployment platform:

• API59_API_KEY: your secret API key.
• API59_BASE_URL: the full OpenAI-compatible base URL specified in your account's integration instructions.
• CLAUDE_MODEL_ID: the exact API identifier for your selected Claude model.
• GPT_MODEL_ID: the exact API identifier for your selected GPT model.

The service host is https://api.59api.com. Check whether the documented SDK base URL includes a version suffix such as /v1, and include it exactly once. Do not append /chat/completions yourself: the SDK adds the resource path.

Choose available models such as Claude Sonnet 5.5 and GPT-6.1 Sol. Copy their API identifiers from the current catalog; display names are not necessarily valid request identifiers.

2. Make Your First Python Requests

The following paragraphs are consecutive Python lines. Put them in a script after setting the environment variables:

import os

from openai import OpenAI

client = OpenAI(api_key=os.environ["API59_API_KEY"], base_url=os.environ["API59_BASE_URL"], timeout=45.0, max_retries=2)

messages = [{"role": "user", "content": "Explain Python context managers in three concise bullet points."}]

def ask(model): return client.chat.completions.create(model=model, messages=messages)

claude_response = ask(os.environ["CLAUDE_MODEL_ID"])

gpt_response = ask(os.environ["GPT_MODEL_ID"])

print(claude_response.choices[0].message.content or "")

print(gpt_response.choices[0].message.content or "")

This sends two independent requests, so both can incur charges. For normal application traffic, choose one model per task instead of calling both automatically. Using the same prompt here makes a basic comparison easier, although useful evaluation requires representative examples and a consistent scoring method.

3. Check Compatibility Before Adding Features

Start with plain text through Chat Completions. OpenAI SDK compatibility does not guarantee that every model supports every parameter or endpoint. Before adding streaming, tool calls, image inputs, or structured output, check the relay's current documentation for your selected model.

Do not assume that the Responses API or provider-specific features are interchangeable with Chat Completions. Likewise, use the documented output-token parameter rather than copying one from an unrelated example. When requesting JSON, validate the returned data against your application's schema before using it.

4. Handle Failures Without Creating Extra Costs

• Classify errors: authentication failures usually require credential fixes; malformed requests require payload changes. Repeated retries will not repair either problem.
• Bound retries: the example allows two SDK retries for eligible failures. Avoid adding an unbounded outer retry loop that multiplies attempts.
• Set deadlines: configure request timeouts and an overall application deadline. A timeout does not prove that upstream processing never occurred.
• Control concurrency: use AsyncOpenAI with a semaphore for larger workloads, respecting account limits instead of launching unlimited requests.
• Protect secrets: keep keys server-side. Avoid logging authorization headers, sensitive prompts, or complete responses by default.

5. Measure Quality and Cost Together

Record the requested model, latency, response status, and available token-usage fields. Compare cost per successfully completed task, not just advertised token prices. Shorter prompts, appropriately limited outputs, and fewer unnecessary retries can reduce spending without changing providers.

59API is a strong low-cost option for developers who want pay-as-you-go access across providers without separate SDK integrations. Check current pricing and account terms before budgeting; its referral rebate can provide additional savings under the applicable program rules.

To try the workflow, sign up for 59API and run a small, budget-limited evaluation using your actual application prompts.

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