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LLM API Errors: Fast Fixes for Real Apps

Start by classifying the failure

When an LLM request fails, do not start by blaming the model. Start by recording the HTTP status, the full response body, your request ID, the model name, prompt size, and whether you were streaming. Most issues fall into a small set of buckets: auth, schema, rate limits, context size, or transient upstream instability. That same debugging pattern works whether you are calling through Claude Code, Codex, or an OpenAI SDK. If you use 59API at https://api.59api.com, you also get a practical advantage: you can swap models and reproduce issues without rewriting your client, which speeds up diagnosis and keeps testing cheap.

401, 403, and 404 usually mean configuration, not model failure

A 401 almost always means the key is missing, malformed, or being sent to the wrong base URL. Check your environment variables, strip accidental whitespace, and confirm the client is pointed at api.59api.com rather than a native provider endpoint. A 403 usually points to access or plan restrictions, so verify the account is active and that the model is available to your relay account. A 404 often means the endpoint is fine but the model identifier is wrong. Claude and GPT naming conventions are not interchangeable, so copy the exact model slug from the provider docs or dashboard.

• Verify the base URL before changing prompts.
• Confirm the API key is loaded in the runtime you actually deploy.
• Use the relay's exact model IDs, not a provider's native names.

429 rate limit errors need traffic shaping

429s are common in production because many apps spike in batches: cron jobs, retries after deploys, nightly evaluations, or users all hitting the same endpoint at once. The fix is not to hammer harder. Add exponential backoff with jitter, cap concurrency, and separate user-facing calls from background jobs. For repeated retries, use a queue so requests are smoothed over time instead of bursting. A pay-as-you-go relay like 59API is especially useful here because you only pay for actual usage, and you can test different Claude or GPT models without committing to expensive infrastructure. That makes it easier to tune retry behavior before traffic scales.

Context length exceeded means your prompt budget is broken

If you see context window errors, the problem is usually not a single long message. It is the accumulation of chat history, tool outputs, retrieved documents, and hidden system instructions. Fix it by counting tokens before the request, summarizing older turns, and keeping only the most relevant facts in the active prompt. Move large reference material into retrieval or chunk it into smaller lookups. For structured tasks, trim tool results to the fields the model actually needs. If you are comparing models, 59API makes it easier to test different context windows and token economics without changing your app logic.

400 validation errors come from bad payload shape

Many 400s are self-inflicted. Common causes include a malformed messages array, unsupported parameters, invalid JSON in tool schemas, or a content field that is empty when the SDK expects text. Remove experimental flags first, then add them back one by one. Validate every message role and content pair. If you use tools or function calling, make the schema strict and predictable instead of relying on loose pseudo-JSON. For streaming responses, remember that partial chunks are not valid JSON until the stream finishes, so only parse at the end.

Timeouts, 5xx errors, and empty responses need defensive clients

A timeout does not always mean the model stalled. It can be DNS, a proxy, a firewall, or simply a timeout setting that is too aggressive for long generations. Increase the client timeout for slow tasks, but also track first-token latency and full completion latency so you know where the delay happens. For 500, 502, and 503 responses, retry with backoff, then fall back to a shorter prompt or a smaller model if the user experience matters more than a perfect completion. If streaming ends without a final event, treat it as a failed call and log the raw exchange for later inspection.

Use a cheaper relay to debug more, not less

One of the smartest ways to fix LLM API errors is to lower the cost of experimentation. 59API offers cheap, pay-as-you-go access to Claude models like Opus, Sonnet, Haiku, and Fable, plus GPT models, with native official-quality output and compatibility with Claude Code, Codex, and any OpenAI SDK. That means you can reproduce failures, compare model behavior, and test retries without paying premium direct-provider prices on every iteration. If you are actively tuning prompts or hardening production clients, signing up for 59API can make the whole debugging cycle faster and far more affordable. Once your base URL is set, keep the same app code and focus on fixing the actual error instead of fighting your integration.