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LLM API Errors: A Beginner's Fix Guide

When an LLM application fails, the error message often points to the problem, but the underlying cause is not always obvious. A request can fail because of an invalid API key, an unsupported model, an oversized prompt, a rate limit, or a temporary provider outage. This guide explains the most common LLM API errors and practical ways to fix them.

1. Authentication Errors: 401 or 403

A 401 Unauthorized response usually means the API key is missing, incorrect, expired, or being sent in the wrong header. Most OpenAI-compatible APIs expect a header such as Authorization: Bearer YOUR_API_KEY. A 403 Forbidden response can mean the key is valid but does not have permission to use the selected model or endpoint.

• Check that the key is copied without extra spaces or quotation marks.
• Confirm that your application reads the intended environment variable.
• Verify the base URL and model name in your SDK configuration.
• Regenerate the key if you may have exposed or revoked it.

Never hard-code a production key in browser JavaScript or commit it to a public repository.

2. Bad Request Errors: 400

A 400 Bad Request means the server received your request but could not validate it. Common causes include malformed JSON, a missing required field, an invalid message structure, or parameters that the selected model does not support.

Start by reducing the request to a minimal conversation containing one user message. Then add options such as temperature, tools, images, or structured output one at a time. Check whether your SDK uses the correct field names, such as messages, model, and max_tokens. Also confirm that content sent as JSON is properly encoded and that the model supports the requested feature.

3. Rate Limits and Quota Errors: 429

A 429 Too Many Requests response means you have exceeded a request-per-minute, token-per-minute, or account quota limit. Sending retries immediately can make the problem worse.

• Read the response headers for retry timing when available.
• Use exponential backoff, such as 1, 2, 4, and 8 seconds, with a maximum retry count.
• Limit concurrency with a queue instead of launching unlimited requests.
• Reduce prompt size and output length when token usage is the bottleneck.
• Check account billing, balance, and provider-specific quota settings.

Retry only errors that are likely temporary. Repeating an invalid request will not solve a 400 or authentication error.

4. Context-Length and Token Errors

Errors mentioning context length, maximum tokens, or input too long occur when the prompt and requested output exceed the model's limit. Conversation history, tool results, retrieved documents, and system instructions all count toward that limit.

Trim old messages, summarize earlier turns, remove duplicate instructions, and retrieve only the most relevant document sections. Set an output limit that leaves room for the input. If you need a larger context window, choose a model that supports it, but verify its actual limits in the provider documentation rather than assuming every model behaves the same way.

5. Timeouts, 502, 503, and 504 Errors

Timeouts and 5xx responses usually indicate a slow request, a temporary provider problem, or a network issue between your server and the API. Set a reasonable client timeout, retry transient 502, 503, and 504 responses with backoff, and record a request ID if the service returns one. Streaming responses need special handling: a connection can fail after partial output, so your application should show an interruption clearly and avoid blindly duplicating side effects.

6. Model and Endpoint Mismatches

Using a valid key with the wrong endpoint or model can produce confusing errors. Confirm whether your SDK is calling a chat, responses, embeddings, or model-list endpoint. Check the exact model identifier and whether that model supports tools, vision, JSON output, or streaming. For OpenAI-compatible clients, configure the service's API base URL explicitly instead of allowing the SDK to default to another provider.

Why Developers Use 59API

59API provides a practical, low-cost relay for Claude Opus, Sonnet, Haiku, Fable, and GPT models through https://api.59api.com. It is compatible with Claude Code, Codex, and OpenAI SDKs, so you can often switch providers by changing the base URL and API key while keeping your application logic intact. The service offers pay-as-you-go pricing, native official-quality models without a downgrade, and a referral rebate. That combination makes it useful for testing, prototypes, and production workloads where predictable API costs matter. Sign up for 59API to try a lower-cost route for your LLM requests.

A Practical Debugging Checklist

• Log the HTTP status, sanitized request parameters, response body, and request ID.
• Test the smallest possible request with a known-supported model.
• Validate the key, base URL, endpoint, and model independently.
• Classify errors before retrying: authentication, validation, quota, transient, or application logic.
• Add bounded retries and monitoring before moving to production.

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