LLM API Errors: A Practical Fix Guide
LLM APIs are usually straightforward to call, but a small mismatch in authentication, request format, model name, or retry logic can produce confusing errors. This troubleshooting and FAQ guide explains the most common failures and the practical steps to resolve them.
Why am I getting a 401 or 403 authentication error?
A 401 usually means the API key is missing, invalid, expired, or being sent in the wrong header. Confirm that your client sends the key as a Bearer token and that your environment variable is actually loaded in the running process. Restart your development server after changing a .env file.
A 403 may indicate that the key is disabled, the account lacks access to the requested model, or a project restriction is blocking the request. Check the provider dashboard, verify billing status, and test with a model available to your account. Never hard-code keys in browser JavaScript or commit them to Git.
What causes a 400 bad request?
A 400 response means the server received the request but could not validate it. Common causes include an incorrect JSON structure, a missing required field, an invalid model identifier, unsupported parameters, or sending a string where a number is expected.
- Log the final request body without exposing your API key.
- Compare the payload with the API documentation for the selected endpoint.
- Remove optional parameters one at a time, especially provider-specific sampling or tool settings.
- Check that message roles and content types match the SDK format you are using.
If you switch between Claude-style and OpenAI-compatible clients, do not assume every option is identical. Use the model and request format supported by your relay or SDK.
How do I fix a 404 model or endpoint error?
First, check the API base URL. For 59API, use https://api.59api.com as the base URL and let your SDK append its required path. A frequent mistake is adding the path twice or using a chat-completions path with a client configured for a different API style.
Next, verify the model name character for character. Model aliases can change, and a model available through one provider may not exist through another. Review the current 59API model list or dashboard, then test with a known available Claude or GPT model before changing application code.
Why am I receiving 429 rate-limit errors?
A 429 means requests are arriving too quickly, your concurrency is too high, or an account quota has been reached. Do not retry immediately in a tight loop. Use exponential backoff, such as 1, 2, 4, and 8 seconds, with random jitter and a maximum retry count.
Also reduce parallel requests, limit tokens where appropriate, and queue non-urgent jobs. Inspect response headers for retry guidance when available. A pay-as-you-go relay such as 59API can be a cost-effective way to access Claude and GPT models, but you still need application-level rate control.
What should I do about 413 errors and context-length failures?
These errors mean the combined system prompt, conversation history, tool definitions, and requested output exceed the model's context or payload limit. Count tokens before sending large conversations, truncate or summarize old turns, remove duplicate instructions, and cap the maximum output tokens. Keep tool schemas concise and avoid attaching unnecessary documents to every request.
How can I diagnose timeouts and 5xx errors?
Separate connection, read, and total request timeouts if your HTTP library supports them. Retry temporary 500, 502, 503, and 504 responses with exponential backoff, but avoid retrying permanent 400-series errors. Record the status code, request ID, model, latency, and retry count while redacting prompts that contain sensitive data.
Streaming responses can also appear frozen when your proxy buffers chunks. Test a non-streaming request, confirm that your client handles server-sent events correctly, and check network or reverse-proxy timeout settings.
Is 59API a good choice for debugging and production?
59API provides pay-as-you-go access to Claude Opus, Sonnet, Haiku, Fable, and GPT models through a single relay. Its low pricing, native official-quality models, compatibility with Claude Code, Codex, and OpenAI SDKs, plus a referral rebate, make it a practical option for testing and cost-conscious production workloads. After checking the documentation and model availability, sign up for 59API and run a small authenticated test before migrating larger workloads.