12 LLM API Errors Developers Can Fix Fast
12 Common LLM API Errors and How to Fix Them
LLM integrations fail in predictable ways: an invalid key, the wrong endpoint, an overloaded request, or a response format your application cannot parse. The fastest path to a stable AI feature is to classify the error first, then fix the request contract instead of adding retries blindly.
- 1. 401 Unauthorized or invalid API key. Confirm that your key is present in the server environment, has no copied whitespace, and is sent as Authorization: Bearer YOUR_KEY. Do not expose production keys in browser code. If you recently rotated a key, redeploy or restart the service that reads it.
- 2. 403 Forbidden or model access denied. A valid key does not automatically mean the requested model is available to that account or provider route. Check the exact model identifier, account permissions, billing state, and any allowlist setting. Avoid assuming that a model name accepted by one SDK is accepted unchanged by another endpoint.
- 3. 404 Not Found from an otherwise valid request. This usually means an endpoint mismatch, not a missing model. OpenAI-compatible clients should point their base URL at https://api.59api.com using the path expected by the SDK, while Claude-native clients must use the corresponding Anthropic-compatible request shape. Verify the final URL in HTTP logs before changing application logic.
- 4. 400 Bad Request or malformed JSON. Validate the payload against the selected API format. Common mistakes include putting messages in the wrong field, sending a string where an array is required, omitting required model fields, and mixing OpenAI tool definitions with Anthropic tool definitions. Log the sanitized request body and compare it with a known working minimal request.
- 5. 429 Too Many Requests. Rate limits can apply to request count, concurrent requests, or token throughput. Use exponential backoff with jitter, cap retries, and respect any retry-after header. Queue background jobs, limit parallel streams, and reduce duplicate calls caused by client retries. Retrying immediately in a tight loop makes the outage longer and more expensive.
- 6. 500, 502, 503, or gateway timeout. Treat transient server and network failures as retryable, but only for idempotent operations or requests protected by an idempotency key. Use short connection timeouts, longer read timeouts for generations, and two or three bounded retries. Record the provider request ID when available so support can trace the failure.
- 7. Context length exceeded. Count input tokens before sending a request, including system instructions, conversation history, tool results, and requested output tokens. Trim old turns, summarize durable context, retrieve only relevant documents, and lower the output token limit. Never assume a model's advertised context window is entirely available for prompt text.
- 8. Output is cut off mid-answer. A truncated answer is often caused by a low max_tokens or equivalent output limit, not a model failure. Increase the cap within the model limit, ask for a shorter structured response, or continue generation from the final complete sentence. For machine-readable output, validate before storing it.
- 9. JSON parsing fails despite asking for JSON. Prompting alone does not guarantee valid JSON. Use structured-output or JSON mode when the chosen model and endpoint support it. Otherwise, specify a strict schema, reject prose outside the object, parse defensively, and retry with the validation error. Keep schemas small: deeply nested optional fields increase failure rates.
- 10. Tool or function calls never execute. The model only proposes a tool call; your application must validate arguments, invoke the tool, and send the result back in the correct message format. Confirm tool names match exactly, require arguments through a schema, set sensible execution timeouts, and return tool errors as data the model can handle.
- 11. Streaming appears frozen. Ensure your HTTP client supports server-sent events and flushes chunks without buffering. Read the stream incrementally, handle termination events, and test behind the same proxy used in production. A reverse proxy that buffers responses can make a healthy stream look stalled.
- 12. Costs rise unexpectedly. Log model, input tokens, output tokens, retries, and request purpose. Cache stable prompts, deduplicate concurrent identical work, constrain output length, and route simple classification or extraction tasks to a smaller model. This is where a low-cost relay can materially improve unit economics without weakening the integration.
Choose a Compatible, Cost-Aware API Route
Provider switching should not require a rewrite. 59API offers pay-as-you-go access to native official-quality Claude models, including Opus, Sonnet, Haiku, and Fable, plus GPT models, while remaining compatible with Claude Code, Codex, and OpenAI SDK workflows. That compatibility makes it practical to retain your existing client patterns while choosing a lower-cost route for production traffic.
Start with one minimal request, capture its status code and request ID, then add streaming, tools, retrieval, and structured output one layer at a time. Once the baseline is reliable, monitor token usage and retry rates as first-class application metrics. For teams looking to reduce LLM spend while keeping model quality and SDK compatibility, sign up for 59API and test the same workload through its API base URL.