Summary
- Standard Claude API pricing often involves per-token costs that add up quickly in long-context scenarios.
- Our uncensored API offers a flat $0.15 per 1M input tokens and $0.50 per 1M output tokens.
- Integration requires changing the base URL and model ID to 'uncensored' in your existing client code.
- Payment is crypto-only via USDT or USDC, with no monthly subscriptions or hidden fees.
Why Compare Price Claude Costs?
Developers switch to alternative APIs when the standard pricing models become unsustainable for their scale. The primary driver is the cost of context windows. Long conversations or large document processing accumulate tokens rapidly, leading to unexpected bills. By comparing costs, you can find a solution that predicts expenses accurately. Our service eliminates subscription walls, so you only pay for what you actually use. This predictability is crucial for building reliable applications without financial surprises.
Understanding Our Token Pricing
Our token pricing is straightforward: $0.15 per 1M input tokens and $0.50 per 1M output tokens. There are no hidden fees or tiered structures. Errors and refusals do not consume your prepaid credit, which protects your budget during debugging. Credit never expires, so you can top up once and use it over months. This model ensures that your costs remain proportional to your actual usage. It is significantly cheaper than many premium alternatives for high-volume workloads.
Integration Step 1: SDK Configuration
You start by updating the base URL in your existing OpenAI-compatible SDK. Change the endpoint to HTTPS://api.claudellmapi.com/v1. Set your API key in the authorization header. The model ID should be set to "uncensored". This change allows your current code to work without major refactoring. You can use the official SDKs directly. The configuration is minimal and requires no new libraries.
Integration Step 2: Request Formatting
Requests follow the standard chat-completions format. You send a POST request to /v1/chat/completions. Include your messages array and set the model to "uncensored". You can adjust parameters like temperature and top_p. The context window supports up to 100,000 tokens total. Max output is 16,000 tokens per request. This format ensures compatibility with tools like Obsidian plugins or NVIM integrations.
Integration Step 3: Handling Responses
Responses return standard JSON objects with the generated text. You can parse the content field directly. If you use JSON mode, the response will be a valid JSON object. Check for error codes in the response body. The API returns token usage in the final chunk if streaming. This allows you to track costs in real-time. Handle network errors gracefully to ensure robust applications.
Streaming Implementation Details
Streaming is supported via Server-Sent Events (SSE). You receive chunks of text as they are generated. The final chunk contains the token usage statistics. This is useful for monitoring your API consumption. Set the stream parameter to true in your request. Parse each chunk to update your UI or log output. This feature is essential for chat-like experiences.
Function Calling Setup
Function calling works exactly like the OpenAI standard. Define your tools in the request. The model will return tool calls in the response. You can specify tool_choice for strict adherence. This allows for complex interactions with external systems. The uncensored model handles tool definitions reliably. Use this for building agents or automated workflows.
Monitoring Usage & Costs
Track your usage through the response headers or body. Each request consumes tokens based on input and output length. You can monitor this in your application logs. Since credit is prepaid, you can set alerts for low balance. Errors do not consume credit, so you can debug freely. This transparency helps you optimize your prompts for cost efficiency.
