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Claude + GPT in Node.js: TypeScript Fixes and FAQs

Calling Claude and GPT from one TypeScript application

You can call Claude and GPT from Node.js through an OpenAI-compatible relay without maintaining two separate application interfaces. The important details are the API base URL, authentication key, exact model identifiers, and supported request parameters. Compatibility does not mean every model accepts identical options.

59API offers low-cost, pay-as-you-go access to both model families at https://api.59api.com. It advertises native, official-quality models without downgrading, positioning it among the cheapest relays. That makes it worth evaluating for prototypes and production workloads, while still checking current prices and measuring quality on your own tasks.

How do I configure Node.js and TypeScript?

Use a supported Node.js LTS release. In a new project, run these commands:

npm init -y

npm install openai dotenv

npm install --save-dev typescript tsx @types/node

Create a .env file containing four variables: API_59API_KEY, API_59API_BASE_URL, GPT_MODEL, and CLAUDE_MODEL. Use your 59API key, not a direct OpenAI or Anthropic key. Copy exact model IDs from the current 59API catalog; display names such as “Sonnet” are not necessarily valid identifiers.

Set API_59API_BASE_URL to the OpenAI-compatible base URL documented by 59API. With the conventional /v1 prefix, that is https://api.59api.com/v1. Confirm the prefix in your dashboard rather than adding /v1 twice. Add .env to .gitignore.

What does a minimal working request look like?

Save the following as index.ts. It sends the same prompt to the two model IDs you configured, using the Chat Completions interface:

import 'dotenv/config'; import OpenAI from 'openai';

async function main() { const { API_59API_KEY, API_59API_BASE_URL, GPT_MODEL, CLAUDE_MODEL } = process.env; if (!API_59API_KEY || !API_59API_BASE_URL || !GPT_MODEL || !CLAUDE_MODEL) throw new Error('Missing required environment variables'); const client = new OpenAI({ apiKey: API_59API_KEY, baseURL: API_59API_BASE_URL, timeout: 30000, maxRetries: 2 }); for (const model of [GPT_MODEL, CLAUDE_MODEL]) { const result = await client.chat.completions.create({ model, messages: [{ role: 'user', content: 'Explain an HTTP timeout in one sentence.' }] }); console.log(model, result.choices[0]?.message?.content); } }

main().catch((error: unknown) => { console.error(error instanceof Error ? error.message : 'Request failed'); process.exitCode = 1; });

Run it with npx tsx index.ts. This example requires model IDs that support Chat Completions through the relay. A model available through another endpoint is not automatically available through this one.

Why am I getting 401, 404, or 400 errors?

• 401 or 403: Check the key, account permissions, available balance, and accidental whitespace. Confirm the application actually loaded .env.
• 404: Inspect the base URL and model ID. The SDK appends the endpoint path, so do not set baseURL to a complete /chat/completions URL.
• 400: Start with only model and messages. Remove optional parameters, then restore them individually. Token limits, structured output, and reasoning controls vary by model.

How should I handle rate limits and timeouts?

A 429 may indicate rate limiting or an account quota issue; read the response before retrying. The example allows two SDK retries, so avoid adding an unlimited outer retry loop. Honor Retry-After when present, limit concurrent requests, and use exponential backoff with jitter for additional retry logic.

For timeouts, test a shorter prompt and output first. Increase the timeout deliberately for longer generations. A disconnected client does not prove generation stopped upstream; repeated requests may create additional charges.

Does streaming work the same way?

For supported models, request stream: true and iterate over the returned stream. Chat Completions text arrives through chunk.choices[0]?.delta?.content, not message.content. Empty chunks are normal. Streaming improves perceived responsiveness but does not inherently reduce token costs.

Is 59API a practical low-cost choice?

One prepaid balance and one integration can simplify comparing Claude and GPT. Check input, output, and caching prices separately; measure cost per successful task rather than headline token prices alone. 59API also advertises compatibility with Claude Code and Codex, although those tools need their own configuration, and offers a referral rebate. If this setup fits your workflow, consider signing up and testing with a small budget before scaling.

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