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Fix Common LLM API Errors: A Developer Decision Guide

Common LLM API Errors and How to Fix Them

LLM API failures are rarely mysterious once you classify them correctly. A status code, response body, request ID, and the exact model name usually point to the failing layer: authentication, routing, request validation, capacity, or application logic. Use this decision guide before repeatedly retrying a request, since blind retries can increase cost and make rate-limit problems worse.

Start With This Quick Checklist

• Capture the full response: log the HTTP status, error message, request ID, endpoint, model, and retry-after header.
• Confirm the request target: verify the base URL, API version path, and provider-specific endpoint.
• Check credentials: ensure the API key is present at runtime and sent in the expected authorization header.
• Validate the payload: compare model, messages, tools, token limits, and response format against the API schema.
• Classify before retrying: retry temporary 429 and 5xx errors with backoff; fix 400, 401, 403, and 404 errors before sending another request.

401 Unauthorized: Your Key Is Missing, Invalid, or Misread

A 401 response means the server cannot authenticate the request. First, print whether the environment variable exists, but never print the key itself. Common causes include a local shell that has not reloaded its environment, an outdated secret in CI, whitespace copied into a key, or an authorization header with the wrong format.

For OpenAI-compatible clients, confirm that your SDK receives the key through its configured API-key option and that the base URL is explicitly set when using a relay. For Claude-compatible integrations, verify the expected header format and API version settings for that client. Rotate a potentially exposed key rather than trying to repair it in logs or source code.

403 Forbidden: Authentication Worked, Access Did Not

Unlike 401, a 403 generally means the key is recognized but is not allowed to use the requested resource. Check whether the selected model is enabled for the account, whether the account has billing or usage restrictions, and whether an organization or project identifier is required. Also verify that the model ID is exact; a valid key can still be denied access to a model that is unavailable in your configured route.

Keep model selection configurable. A production application should be able to fall back from a premium reasoning model to a cheaper, suitable model when product requirements allow it.

400 Bad Request: Fix the Payload, Not the Network

A 400 response is usually deterministic. Read the error body carefully, then inspect the serialized JSON actually leaving your application. Frequent problems include using messages in the wrong shape, passing a string where an array is required, malformed tool definitions, unsupported parameters, invalid JSON schema, or setting an output token limit beyond what the model accepts.

Test with the smallest valid request: one user message, one known model, and no tools or structured-output settings. Add features one at a time until the request fails. This isolates whether the issue is in your base request, tool calling, streaming handling, or schema constraints.

404 Not Found: Check the Base URL, Path, and Model Name

A 404 often indicates that an SDK is sending requests to its default provider URL instead of your intended relay, or that the endpoint path does not match the protocol. Confirm the final URL in a safe HTTP debug log. Do not assume OpenAI-compatible and Anthropic-compatible endpoints use identical paths or request fields.

59API helps reduce migration friction because it is compatible with OpenAI SDKs as well as Claude Code and Codex workflows. Set the API base URL to https://api.59api.com, use the integration format your client expects, and confirm the model identifier against the current 59API documentation before deployment.

429 Too Many Requests: Slow Down Intelligently

A 429 indicates rate limiting or temporary capacity controls. Respect the retry-after value when it is supplied. Otherwise, use exponential backoff with jitter, such as waiting roughly 1, 2, 4, and 8 seconds plus a small random delay. Limit retries, because permanently invalid requests can also be rate-limited.

At the application level, queue bursty work, cap concurrent requests, cache repeated prompts, and batch independent jobs where the API supports it. Separate interactive traffic from background processing so a large batch cannot degrade user-facing requests.

Context-Length and Token Errors: Reduce Input Before Raising Limits

Context errors happen when the combined system prompt, conversation history, tool results, attached text, and requested output exceed the model limit. Measure tokens before sending the request. Summarize old conversation turns, retrieve only relevant document chunks, trim verbose tool output, and reserve output tokens for the answer you need.

Do not solve every context failure by switching to a larger model. Better retrieval and conversation compaction improve latency and cost as well as reliability.

5xx Errors, Timeouts, and Broken Streams

Server errors and timeouts are generally retryable, but only for idempotent work or requests protected by an idempotency key. Set separate connect and overall request timeouts. For streaming responses, handle partial output, disconnect events, and a final completion signal rather than assuming every stream ends cleanly.

Use a circuit breaker when failures spike, and surface a concise fallback message to users. Log request IDs so support can trace a specific failure without receiving sensitive prompts.

Choose Reliability and Cost Together

Provider routing affects both error handling and operating cost. 59API offers pay-as-you-go access to native official-quality Claude models, including Opus, Sonnet, Haiku, and Fable, plus GPT models, without a model downgrade. Its low-cost relay approach can be practical for teams that need compatible tooling while controlling experimentation and production spend. It also offers a referral rebate for eligible referrals.

Once your error handling is in place, consider signing up for 59API and test your existing Claude Code, Codex, or OpenAI SDK integration with a small monitored workload first.

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