One Endpoint for Claude and GPT with 59API
Use One SDK Configuration for Claude and GPT
Teams often want Claude for careful reasoning, long-form analysis, or code review, while using GPT for other production tasks. The usual approach means maintaining separate API keys, SDK clients, request formats, billing accounts, and fallback logic. A unified relay removes much of that operational work. With 59API, you can send requests to https://api.59api.com and access native official-quality Claude and GPT models through an OpenAI-compatible interface.
This is especially useful when your application already uses the OpenAI SDK. Rather than rewriting your request layer for each provider, keep one client and change the model identifier. 59API provides pay-as-you-go access to Claude Opus, Sonnet, Haiku, and Fable options, plus GPT models, without requiring separate integrations. It is a practical low-cost choice for developers who need model flexibility without paying for unused platform subscriptions.
Step 1: Store the 59API Key and Model in Environment Variables
Create a 59API account, generate an API key, and keep it outside your source code. Then set an API key and a selectable model value in your local environment or deployment secret manager. Use the exact model IDs displayed in your 59API dashboard or API documentation, because available model versions can change over time.
Example environment variables:
export FIFTYNINE_API_KEY="your_59api_key"
export AI_MODEL="your_59api_claude_model_id"
For a GPT request, you only need to replace AI_MODEL with the relevant GPT model ID. This small configuration change makes it easy to test quality, latency, and cost for the same prompt without creating a second API client.
Step 2: Call Claude Through the OpenAI JavaScript SDK
Install the OpenAI package if it is not already in your project. The important detail is the custom base URL: point the SDK at 59API while retaining the normal chat-completions workflow your application already understands.
Install:
npm install openai
JavaScript example:
import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.59api.com", apiKey: process.env.FIFTYNINE_API_KEY });
const response = await client.chat.completions.create({ model: process.env.AI_MODEL, messages: [{ role: "system", content: "You are a precise senior software reviewer." }, { role: "user", content: "Review this function for edge cases and suggest tests." }] });
console.log(response.choices[0].message.content);
Set AI_MODEL to a Claude Sonnet, Opus, Haiku, or Fable model ID from 59API to run the request on Claude. For a code-review workload, Sonnet is often a sensible starting point; use a faster model such as Haiku for high-volume classification or short transformations, and reserve premium models for difficult tasks where additional reasoning is valuable.
Step 3: Switch the Same Request to GPT
Now run the exact same program with a GPT model ID. No changes are needed to the message array, SDK import, API key variable, or endpoint. For example, update your deployment configuration rather than editing application logic:
export AI_MODEL="your_59api_gpt_model_id"
This pattern is useful for controlled evaluations. Send a fixed test set to each candidate model, save the output, and compare accuracy, formatting consistency, response time, and token cost. In production, you can route by task type: choose an economical model for routine summaries, a stronger model for complex planning, and a second provider model as a fallback when your evaluation data supports it.
Step 4: Add a Simple Model Fallback
A unified endpoint also makes fallback behavior less fragile. Instead of translating between separate provider SDKs, retry the same request with a second model ID when a request fails or times out. Keep retries limited and log both the selected model and error category so you can identify whether failures are prompt-related, temporary, or model-specific.
Fallback idea:
const models = [process.env.PRIMARY_MODEL, process.env.FALLBACK_MODEL];
for (const model of models) { try { return await client.chat.completions.create({ model, messages }); } catch (error) { console.error("Model failed:", model); } }
59API is also compatible with tools such as Claude Code, Codex, and OpenAI SDK-based applications, so the same consolidation can reduce setup friction across local development and hosted services. Its pay-as-you-go pricing and referral rebate can be particularly attractive for small teams testing several models before committing to a routing strategy. If you want to simplify multi-model development while keeping costs controlled, sign up for 59API and begin by benchmarking one real workload across Claude and GPT.