Inference API Common Mistakes: Troubleshooting Guide
An inference API often fails due to misconfigured token limits, incorrect streaming headers, or unexpected content filters. This guide resolves the most common integration errors for uncensored model deployments and OpenAI-compatible clients.
Key takeaways
Refusals occur when specific hard limits like minor content are violated, not just general filters.
Context windows count prompt and completion tokens together, requiring careful management of long inputs.
Streaming responses must parse the final chunk to retrieve accurate token usage statistics.
JSON mode errors typically stem from malformed schema definitions or missing response_format headers.
Why Your Uncensored Model Might Still Refuse
The model does not refuse lawful adult, fictional, security-research, or controversial topics. However, a hard content limit always applies regarding sexual content involving minors. Requests containing this specific violation are refused immediately. This filter is distinct from standard safety guardrails found in other models.
Check your input for subtle indicators of minor characters in sexual contexts. Even if the model is uncensored, this specific boundary remains enforced. All other content types pass through without refusal. This ensures legal compliance while maintaining freedom for other use cases.
Handling Token Limits and Context Windows
The context window allows 100,000 tokens for both prompt and completion combined. You must track input tokens to ensure they do not exceed this limit when combined with the output. The maximum output per request is 16,000 tokens. If you do not set max_tokens, the default is 2,048 tokens.
Calculate your budget carefully. Long documents consume significant context. If you exceed the limit, the API returns an error. Split large inputs into smaller chunks to maintain performance. This prevents truncation of critical information in your prompt.
Debugging Streaming Responses
Streaming via SSE delivers tokens in real-time. You must parse the final chunk to retrieve accurate token usage statistics. The token usage data appears only in the last chunk of the stream. Earlier chunks contain only the content delta.
Implement a counter to track the total tokens processed. Sum the input and output tokens from the final response. This ensures accurate billing and usage tracking. Do not rely on partial counts from intermediate chunks. They do not reflect the total cost of the request.
JSON Mode Errors and Validation
JSON mode enforces strict schema validation. You must set the response_format parameter to json_object. The model attempts to output valid JSON. Errors occur if the schema is malformed or the output cannot be parsed.
Validate your JSON schema before sending requests. Ensure all required fields are defined. Use a linter to check your schema syntax. This reduces validation errors and ensures consistent output. Test with small inputs first to verify the format.
Understanding Tool Choice and Function Calling
Function calling allows the model to invoke tools. You can specify tool_choice to control the behavior. The model returns structured data for tool execution. This enables complex workflows and external data integration.
Define your tools clearly in the request. Provide descriptions for each tool. The model uses these descriptions to decide when to call them. Test the tool definitions with various inputs. Ensure the output matches your expected format.
Rate Limits and Concurrent Requests
Each key is limited to 300 requests per minute. You can have up to 8 concurrent requests simultaneously. The request body size is capped at 8 MB. Exceeding these limits results in a 429 error.
Implement exponential backoff for retries. Monitor your usage to stay within limits. Use multiple keys if you need higher throughput. This ensures smooth operation during peak usage. Track your request volume to avoid unexpected interruptions.
Pricing Surprises: Input vs Output Tokens
Pricing is $0.15 per 1M input tokens and $0.50 per 1M output tokens. Output tokens are more expensive. Long responses increase costs significantly. Monitor your output length to control expenses.
Set max_tokens to cap output length. This prevents unexpected charges from long responses. Track your token usage regularly. Use the token usage data from the final chunk to calculate costs accurately.
Crypto Payments and Credit Expiration
Credit is topped up with crypto only. We accept USDT on TRC20 or USDC on Base. The minimum top-up is $20 and the maximum is $500. Credit never expires. Mistakes like double charges are fixed via the Support page.
Bonus credit applies to larger top-ups. You receive a +5% bonus from $50, +10% from $100, and +15% from $200. Errors and refusals are free. This ensures you only pay for actual usage.
Frequently asked questions
What is an inference API?
An inference API allows you to send text to a large language model and receive text back. It normalizes the endpoint so you can use standard SDKs. This simplifies integration with your applications.
Which payment methods do you accept?
Crypto only: USDT on TRC20 or USDC on Base, any whole amount from $20 to $500. Bonus credit applies from $50.
Does the API support streaming?
Yes, it supports streaming via Server-Sent Events. You receive tokens in real-time. The final chunk contains the total token usage.
How do I pay with crypto?
You can top up with USDT on TRC20 or USDC on Base. The amount must be between $20 and $500. Bonus credit is added automatically.
Retrieve your API key
Register, retrieve your key, and update the base URL. That is the entire configuration.
