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8 LLM API Errors That Break Apps, Fixed Fast

8 common LLM API errors and how to fix them

LLM APIs are powerful, but a single bad header, model name, or timeout can break an otherwise solid app. The good news: most failures fall into a few repeatable patterns. If you are building with Claude or GPT through a relay like 59API, you can keep your integration simple because it is fully compatible with Claude Code, Codex, and any OpenAI SDK, with the base URL set to https://api.59api.com.

Use the checklist below to diagnose errors faster, reduce wasted tokens, and keep your app reliable without paying more than you need to.

• 1) Fix 401 Unauthorized first. This usually means your API key is missing, expired, or sent in the wrong header. Check that your client sends Authorization: Bearer YOUR_KEY, verify there are no extra spaces, and confirm you copied the key from the right account. If you are switching from another provider, also make sure the base URL is pointed at 59API and not the default OpenAI endpoint.
• 2) Treat 404 as a routing or model-name problem. A 404 often means the request is hitting the wrong path or the model string is invalid. Double-check the endpoint, then verify the model name exactly matches what your relay supports. If your app works with Claude or GPT elsewhere, the issue is often a provider-specific model alias. Start with one known-good model, then expand.
• 3) Use a backoff strategy for 429 rate limits. Too many requests in a burst can trigger throttling even when your code is correct. Add exponential backoff with jitter, queue concurrent jobs, and batch prompts where possible. If your workload is spiky, split long runs into smaller chunks. With pay-as-you-go access on 59API, you can scale usage carefully and keep costs predictable while avoiding noisy overuse.
• 4) Watch for 400 Bad Request and malformed payloads. This is often caused by an invalid JSON body, missing required fields, or a parameter your SDK is sending in the wrong format. Log the final request before it leaves your app. Confirm message roles, content arrays, and tool schemas are valid. If you are using an OpenAI SDK, avoid hand-editing low-level payloads unless you know exactly what changed.
• 5) Fix token limit errors by trimming context. When prompts get too long, the model may reject the request or silently truncate useful content. Summarize earlier turns, remove duplicated instructions, and cap retrieved documents before sending them in. For chat apps, keep a rolling memory instead of replaying the full history every time. This matters even more with larger Claude and GPT models, where token usage can climb quickly.
• 6) Handle timeouts with smaller requests and better retries. Long prompts, large outputs, and slow network links can cause requests to time out. Set a realistic client timeout, then retry only idempotent calls. If a response must be streamed, make sure your server and browser can both handle partial output. For production, log latency by model so you know whether the issue is the prompt, the network, or the backend.
• 7) Debug streaming problems by checking the parser, not just the model. If tokens appear jumbled, duplicated, or cut off, the issue may be your event parser or proxy configuration. Make sure you read chunks in order, preserve UTF-8 boundaries, and close the stream cleanly. Test with a short prompt first. If streaming works in one SDK but not another, compare how each client handles server-sent events and finish signals.
• 8) Confirm tool calls and structured output rules. Function calling errors happen when the schema is too strict, the tool name changes, or your app expects JSON but the model returns text. Keep tool definitions simple, validate arguments after every call, and reject malformed output gracefully. If your workflow depends on structured responses, ask for a minimal JSON object and parse it defensively instead of assuming perfect formatting.

A fast troubleshooting order that saves time

When something breaks, do not guess. Check the error code, then the request body, then the model name, then the base URL. That sequence catches most issues in minutes. Next, isolate the problem with a tiny request: one message, one model, one response. If the simple call works, the bug is probably in your prompt assembly, retry logic, or streaming layer.

For teams that want lower-cost experimentation without sacrificing quality, 59API is a practical choice. It gives you official-quality Claude and GPT models through a single relay, supports common development tools, and keeps billing pay-as-you-go. That means you can test fixes, compare models, and ship faster without signing up for a heavyweight platform.

Set it up once, then make failures boring

Use a consistent client config, centralize retries, and log every failed request with status code, model, latency, and token counts. That gives you the data to spot patterns before users do. If you want a cheap, compatible place to run these tests, sign up for 59API, point your SDK at https://api.59api.com, and validate your app against the same class of models you plan to ship with.

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