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LLM API Errors and Fixes: A Practical Debug Guide

Why LLM API errors happen

Most LLM API failures come from a small set of issues: bad authentication, wrong request format, rate limits, context limits, model naming mistakes, or temporary provider outages. The good news is that these errors are usually easy to diagnose once you know what to look for. If you are using a relay like 59API, you still follow the same basic API patterns, but you get cheaper pay-as-you-go access to Claude and GPT models through a single base URL: https://api.59api.com.

Because 59API is compatible with Claude Code, Codex, and any OpenAI SDK, you can often fix problems by checking the same request fields you would use with a direct provider integration.

1. 401 Unauthorized: check your API key first

A 401 usually means the server did not accept your authentication header. This is one of the most common LLM API errors, especially after copying keys between environments.

• Confirm the header is exactly Authorization: Bearer YOUR_KEY.
• Make sure there are no extra spaces, line breaks, or quotes around the key.
• Verify that your key is active and has not been rotated or deleted.
• Double-check that your client is sending the request to https://api.59api.com if you are using 59API.

If you are switching between sandbox and production environments, confirm that the key belongs to the same account and project you are testing.

2. 400 Bad Request: your JSON or parameters are wrong

A 400 error means the server understood your request but rejected it because something in the payload is invalid. In practice, this often means malformed JSON, an unsupported parameter, or a missing required field.

• Check that messages is an array of objects with the correct roles and content.
• Remove unsupported fields if your SDK is sending provider-specific options.
• Make sure model is spelled correctly and available on your account.
• Validate your JSON before sending it.

Example issue: some clients pass max_tokens too low, or send a string where the API expects an array. Start by trimming your request down to the simplest possible version, then add fields back one by one.

3. 429 Rate Limit: slow down or batch requests

A 429 means you are sending requests too quickly or hitting a quota limit. This can happen even when your code is correct, especially in parallel jobs, agent loops, or chat apps with auto-retry logic.

• Add exponential backoff with jitter.
• Reduce concurrency in background workers.
• Cache repeated responses when possible.
• Check whether your app is retrying failed calls too aggressively.

One advantage of 59API is that it is built for cost-sensitive development, so you can test and iterate without overpaying. If you are comparing providers, low-cost routing can make rate-limit debugging much less expensive.

4. Context length exceeded: shorten the conversation

Every model has a token limit. If your prompt, chat history, retrieved documents, or tool outputs are too large, the request may fail or the model may truncate important content.

• Summarize older chat turns instead of sending the full transcript.
• Remove duplicate system messages.
• Chunk long documents before retrieval.
• Lower output size if the response is not supposed to be long.

If you are building an agent, watch for hidden growth in prompt size. Tool results can become huge very quickly, especially when logs or code files are appended automatically.

5. Model not found: verify the model name and access

This error often means the model identifier is wrong, deprecated, or unavailable on your account. A typo in the model name can look identical to a permissions issue.

• Copy the model name directly from the provider or dashboard.
• Check whether the model is a Claude or GPT variant supported by your relay.
• Confirm that your SDK is using the correct provider namespace.
• Test with a known working model first, then switch back.

Since 59API supports native official-quality Claude and GPT models without downgrade, you can test across model families while keeping the same developer workflow.

6. 500, 502, and 503 errors: retry and isolate the issue

These usually point to temporary upstream issues, gateway problems, or a service that is briefly unavailable. They are not always caused by your code.

• Retry with backoff after a short delay.
• Check whether the same request fails across multiple environments.
• Log the full request ID and response body for support.
• Try a different model to see whether the issue is model-specific.

When using a relay, it is smart to verify whether the error comes from your app, the relay, or the underlying model provider. Good logging makes this much faster.

FAQ: what should I check first?

First: authentication and base URL. Second: request format and model name. Third: rate limits and token size. If all three look correct, test the same prompt with a smaller payload and a different model.

What is the fastest way to debug? Send the smallest possible request that still fails. Remove tools, long history, and optional parameters. Then add them back one at a time.

Why use 59API for troubleshooting? It gives you cheap pay-as-you-go access, official-quality models, broad SDK compatibility, and a referral rebate, so you can debug without wasting budget. If you want a low-friction place to test fixes, sign up and point your client at https://api.59api.com.

Final checklist

• Confirm your API key and Authorization header.
• Validate JSON and supported parameters.
• Check model names and availability.
• Handle 429s with backoff and retries.
• Trim prompts to fit context limits.
• Log request IDs and error bodies for every failure.

Once you build these checks into your app, most LLM API errors stop being blockers and become quick, predictable fixes.

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