Fix LLM API Errors Without Burning Your Token Budget
Start with the cost of a failed workflow
LLM API errors become expensive when your application retries blindly, resends oversized prompts, or repeats completed work. The goal is not simply fewer errors: it is fewer paid tokens per successful task.
Consider illustrative pricing of $2 per million input tokens and $8 per million output tokens, not a quoted 59API rate. A request using 2,000 input tokens and 500 output tokens costs $0.008. At 10,000 requests, that is $80. If 5% of requests are accidentally processed twice, another 500 completions add $4. Failed attempts are not automatically billable; check the provider's billing rules and usage records.
401 and 403: fix credentials before retrying
401 usually means authentication failed; 403 usually means access is denied. Retrying the same credentials will not repair either problem.
- Check that the API key belongs to the service receiving the request. A direct-provider key is not interchangeable with a relay key.
- Inspect the runtime environment, not just your local configuration. Containers and CI jobs frequently have missing or stale secrets.
- Use the authentication mechanism required by the selected endpoint. OpenAI-compatible and Claude-native interfaces can require different headers.
- For 403, inspect model permissions, account restrictions, and the response message. Rotate keys only when necessary, and never log full secrets.
With 59API, configure the service's API base URL, https://api.59api.com, and follow its documented SDK-specific path. Check the final request URL to catch duplicated path segments.
400, 404, 413, and 422: repair the request
These errors commonly indicate invalid parameters, an incorrect endpoint or model identifier, or an oversized payload. Providers differ, so read the error body rather than relying on the status alone.
- 400 or 422: remove unsupported parameters and verify the message structure, tool schema, and output-token field for that API.
- 404: confirm the endpoint and copy an available model identifier from the service's current model list. Marketing names are not necessarily API identifiers.
- 413 or context-limit errors: reduce attachments, retrieved passages, and conversation history. HTTP payload limits and model context limits are separate constraints.
Reserve context space for the answer: input tokens plus the permitted output must fit the model's applicable limits. Use a compatible tokenizer where available; character counts are only rough estimates.
In the earlier example, reducing input to 1,200 tokens and output to 300 lowers each request to $0.0048. Across 10,000 requests, that is $48 instead of $80: a 40% reduction, assuming unchanged pricing and adequate answer quality.
429: distinguish throttling from exhausted quota
A 429 can indicate request-rate limits, token-rate limits, or exhausted quota. Read the response details. Quota exhaustion needs a billing or account fix, not an endless retry loop.
For temporary throttling, honor Retry-After when supplied. Otherwise, use exponential backoff with jitter. A reasonable starting policy is three total attempts, with waits around one and two seconds plus randomness. Queue jobs and lower worker concurrency; repeated rejected requests can also count against rate limits on some services.
5xx errors and timeouts: avoid duplicate work
500, 502, 503, and 504 responses often justify bounded retries. Network timeouts are trickier: the server may have completed generation even though your client received nothing.
- Set connection and response timeouts separately where your client supports them. Long responses need a realistic response deadline.
- Inspect SDK retry defaults. Three SDK attempts inside three application attempts can produce nine attempts.
- Use an idempotency key only if the endpoint documents support. Track task state yourself to prevent duplicate application actions.
- Do not execute streamed tool instructions until the complete call is validated. A disconnected stream may contain only partial JSON.
Choose lower prices, then measure successful-task cost
59API offers inexpensive, pay-as-you-go access to native Claude and GPT models without model downgrades, with compatibility for Claude Code, Codex, and OpenAI SDKs. That makes it a practical option for lowering baseline token spending without rebuilding your workflow. Its referral rebate can provide additional savings; check current rates and rebate terms before budgeting.
Track model, status, latency, token usage, retry count, and cost per successful task without exposing sensitive prompts. Start with a small test batch before scaling. If those results fit your workload, consider signing up for 59API and comparing your measured cost against your current setup.