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Claude and GPT in Python: Fix API Errors Fast

Call Claude and GPT Through One Python Client

Calling Claude and GPT from Python is simpler when both models use the same request format. 59API provides low-cost, pay-as-you-go access through an OpenAI-compatible relay, so you can reuse the OpenAI Python SDK instead of maintaining separate integrations.

The service positions itself among the cheapest relays and describes its models as native, official-quality offerings without downgrades. Compare current per-model prices and run your own quality checks before committing production traffic. A low token price matters most when the model reliably completes your task.

1. Install the SDK and Configure Credentials

Install or update the SDK in your active Python environment:

python -m pip install --upgrade openai

In a macOS or Linux terminal, set these environment variables. Replace the placeholders with your 59API key and exact model identifiers from its current catalog:

export API_59_KEY='YOUR_59API_KEY'

export GPT_MODEL='EXACT_GPT_MODEL_ID'

export CLAUDE_MODEL='EXACT_CLAUDE_MODEL_ID'

Do not substitute a display label such as “Sonnet” for an API model ID. Available Claude families and GPT versions can have different identifiers, permissions, and prices. Never commit credentials to source control.

2. Send a Minimal Request to Each Model

59API’s service base is https://api.59api.com. The example below uses the conventional OpenAI-compatible /v1 prefix; confirm the documented prefix in your dashboard before running it. Create call_models.py with the following lines in order:

import os

from openai import OpenAI

client = OpenAI(api_key=os.environ['API_59_KEY'], base_url='https://api.59api.com/v1', timeout=45.0, max_retries=2)

messages = [{'role': 'user', 'content': 'Explain Python virtual environments in two sentences.'}]

for name in ('GPT_MODEL', 'CLAUDE_MODEL'): print(name, client.chat.completions.create(model=os.environ[name], messages=messages).choices[0].message.content)

The final line is a complete, single-line Python loop. Run the file with python call_models.py. This test deliberately avoids tools, images, and optional generation settings so configuration errors are easier to isolate.

Why Does My Python Request Fail?

• KeyError before any request: An environment variable is missing. Export it in the same terminal that launches Python, or configure it in your IDE’s run settings.
• 401 or 403: Check that you supplied a 59API credential rather than an OpenAI or Anthropic credential. Confirm the key is active, the account has access, and no whitespace was copied.
• 404: Check the complete request URL and model identifier. A wrong route prefix can fail before model selection occurs. Avoid accidentally constructing /v1/v1.
• 400 or unsupported parameter: Return to the minimal example. Features such as tools, structured output, and token-limit fields vary by model and endpoint; SDK compatibility does not guarantee feature parity.
• 429: Inspect the response body for rate-limit, quota, or balance information. Reduce concurrency and use bounded exponential backoff rather than immediate retry loops.
• Timeout or 5xx: Check service status and test a shorter prompt. Increase the timeout for longer generations. Avoid stacking application retries on top of SDK retries, which can multiply requests and costs.

When asking support for help, include the timestamp, model ID, sanitized error body, and request ID if provided. Exclude API keys and sensitive prompts.

Frequently Asked Questions

Can I use the Anthropic Python SDK instead? This example uses OpenAI-compatible Chat Completions. The Anthropic SDK expects a different request format and route. Use it only if 59API explicitly documents support for that interface.

Does SDK compatibility include every GPT or Claude feature? No. Validate streaming, tool calls, image inputs, and structured output individually. Likewise, Claude Code and Codex compatibility does not mean their configuration is identical to this Python script.

How do I keep costs predictable? Start with a lower-cost model for routine tasks, limit conversation history, and set a model-supported output cap. Record returned usage data and reconcile it with dashboard billing.

Is 59API worth trying? Its inexpensive pay-as-you-go access makes cross-model testing practical, and it also offers a referral rebate subject to current terms. Sign up when you are ready to compare both models on a small, representative workload.

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