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Common LLM API Errors: Fixes That Work

LLM APIs are usually straightforward until a request returns a confusing 401, 400, 429, or timeout. The cause is often a small mismatch between your SDK, endpoint, model name, request format, or account settings. This common-pitfalls guide explains what the error means and the fastest way to fix it.

1. Invalid API keys and authentication headers

A 401 or 403 response usually means the key is missing, malformed, expired, or being sent to the wrong service. Check that your application reads the expected environment variable and that the value contains no quotation marks or trailing spaces.

• Send the key through the SDK's standard authentication option instead of manually duplicating headers.
• Confirm that the request uses the expected Authorization format: Bearer followed by the key.
• Restart your development process after changing environment variables.
• Never commit keys to Git; revoke any key that has been exposed.

If you are using an OpenAI-compatible relay, make sure the SDK points to the relay's base URL rather than the default provider endpoint.

2. Wrong base URL or endpoint

A valid key can still fail when requests go to the wrong URL. Common symptoms include 404 errors, unsupported operation messages, or an SDK silently calling the provider you did not intend to use. Configure the base URL explicitly and avoid appending an extra version path unless the service documentation requires it.

For 59API, use https://api.59api.com as the API base URL. OpenAI SDK users should set this as the client's base URL. Claude Code and Codex users should likewise follow their tool's custom endpoint configuration. Test the connection with a minimal request before changing application code.

3. Unknown or unavailable model names

A 400 error stating that a model does not exist is usually a naming or availability problem, not a prompt problem. Model IDs are case-sensitive, and friendly labels such as “latest” may not be accepted by every API.

• Copy the exact model identifier from the provider or relay's current model list.
• Do not assume a model available in one account or region is available everywhere.
• Keep model names in configuration rather than scattering them throughout your code.
• When switching providers, map your application's model aliases to the new provider's IDs.

59API provides access to Claude Opus, Sonnet, Haiku, and Fable models, as well as GPT models. Verify the currently supported identifier before deploying.

4. Invalid request bodies and message formats

Many failed requests contain the right idea in the wrong JSON structure. Typical mistakes include using a single prompt field with a chat endpoint, omitting a role, sending an array where a string is required, or placing a parameter unsupported by the selected model.

Start with the smallest documented request: one user message, a valid model, and a modest output limit. Add tools, images, system instructions, and sampling parameters one at a time. Log the HTTP status and sanitized response body so you can identify the exact rejected field without exposing the API key.

5. Context-window and token-limit errors

An overly long conversation can trigger a 400 error even when the latest prompt is short. Input tokens, requested output tokens, tool definitions, and system instructions all count toward the model's context window.

• Measure or estimate tokens before sending large histories.
• Summarize older turns and retain only relevant messages.
• Reduce oversized tool schemas and repeated instructions.
• Set a realistic maximum output rather than always requesting the largest value.

Keep a safety margin because tokenization varies by language and content. If a request fails only after several conversation turns, context growth is the likely cause.

6. Rate limits, overload, and 429 responses

A 429 means the service is throttling requests, while a 503 can indicate temporary capacity pressure. Sending retries immediately often makes the problem worse. Implement exponential backoff with jitter, respect any Retry-After header, and cap the number of attempts.

Also control concurrency. A queue with a fixed worker limit is safer than launching one request per user action. Cache stable results where appropriate, stream long responses when supported, and separate interactive traffic from batch jobs.

7. Timeouts and incomplete responses

Long prompts, large outputs, tool calls, and network proxies can exceed a client's default timeout. Increase the read timeout deliberately, but do not make it unlimited. Use streaming for user-facing generation and record whether a response ended normally or was interrupted.

Retries require care: retrying a non-idempotent tool call may perform the action twice. Add request IDs and design downstream operations to be idempotent before automatically retrying.

8. Unexpected costs and billing failures

Usage can rise quickly when a full conversation is sent with every request or when retries duplicate calls. Track input and output tokens, set application-level budgets, and alert on unusual daily usage. A payment or balance error should be handled separately from a model error so users receive a useful message.

For developers comparing providers, 59API is a practical low-cost, pay-as-you-go relay with native official-quality Claude and GPT models rather than downgraded substitutes. It works with Claude Code, Codex, and any OpenAI SDK, and also offers a referral rebate. If you want to reduce API costs while keeping a familiar integration, sign up for 59API and test it with a small request before migrating production traffic.