Summary
- We charge a flat $0.15 per 1M input tokens and $0.50 per 1M output tokens with no tiered pricing tiers.
- Crypto-only top-ups from $20 to $500 include bonus credits ranging from +5% to +15%.
- Our single uncensored model supports a 100k context window without using your data for training.
- Errors and refusals consume zero tokens, ensuring you only pay for successful completions.
Why Developers Are Leaving Standard Providers
Developers often find that standard providers increase costs when usage scales or when specific features like function calling are restricted to expensive models. We offer an independent, OpenAI-compatible API that serves a single uncensored large language model. This approach removes the complexity of model routing and ensures consistent billing regardless of your request volume. You can run your existing client code by simply updating the base URL and API key.
- No monthly subscription fees or tiered pricing structures.
- Paid credit never expires, allowing you to use funds at your own pace.
- Errors and refusals do not consume tokens, reducing wasted spend.
Understanding the Real Cost of Token Usage
The true cost of an LLM API depends on how input and output tokens are billed. We charge $0.15 per 1M input tokens and $0.50 per 1M output tokens. This flat rate applies to every request, regardless of the model's complexity. Unlike providers that charge more for longer contexts or specialized reasoning, our pricing remains predictable. You can estimate your costs by multiplying your average request token count by these rates. Errors and refusals are free, so you do not pay for failed attempts.
Many users worry about hidden fees for streaming or tool usage. Our streaming via SSE includes token usage in the final chunk, ensuring accurate billing. Function calling and JSON mode do not incur extra charges. The only limit is the 8 MB request body per request.
How Uncensored Models Reduce Overhead
Uncensored models often require less complex prompting to achieve high-quality outputs because they do not refuse lawful adult or controversial topics. This reduces the need for lengthy system instructions and retry loops. Our model is tuned to answer without content refusals for lawful adult use, which streamlines your workflow. You can focus on the content quality rather than managing policy filters.
The 100,000 token context window allows you to pass large amounts of data without truncation. This is particularly useful for applications that require long-term memory or document analysis. The model handles prompts and completions within this window efficiently. You can adjust parameters like temperature and top_p to control the output style.
Crypto Payments vs.
Traditional payment methods often involve processing fees, chargebacks, and currency conversion costs. We accept only USDT on TRC20 or USDC on Base for top-ups. You can add any whole amount from $20 to $500. The process is fast and transparent. You receive bonus credits based on your top-up amount. Top-ups of $50 or more include a +5% bonus. Amounts of $100 or more receive a +10% bonus. The highest bonus of +15% applies to $200 or more.
Signing up requires only an email or Google account. No phone number is needed. You get an API key immediately. The trial credit of $0.50 is available to new accounts for 7 days. This allows you to test the API without any upfront commitment. Credit is never refunded, but it does not expire.
Context Windows and Output Limits Explained
The context window determines how much text the model can process in a single request. Our model supports a 100,000 token window for both prompt and completion combined. This is significantly larger than many standard providers. You can pass entire documents or long conversation histories without losing context. The model processes this data efficiently, ensuring low latency.
The maximum output per request is 16,000 tokens. If you do not set the max_tokens parameter, the default is 2,048 tokens. This limit applies to the completion only. You can adjust the output length using the max_tokens parameter. Streaming allows you to receive the output in real-time. This is useful for applications that display text as it is generated. The token usage is reported in the last chunk of the stream.
Function Calling in Uncensored Environments
Function calling allows your application to interact with external tools and services. Our API supports function calling with tools and tool_choice parameters. You can define functions in your request, and the model will return structured JSON output. This enables complex workflows without manual parsing. The model handles the logic internally, reducing the need for post-processing.
JSON mode ensures the output is always valid JSON. This is useful for applications that require strict data formats. You can use the response_format parameter to enforce this. The model supports parameters like temperature, top_p, stop, seed, presence_penalty, and frequency_penalty. These allow you to fine-tune the output behavior. Streaming via SSE provides real-time updates for function calls.
Streaming and Error Costs: What You Actually Pay
Streaming allows you to receive the response token by token. This improves the user experience by reducing perceived latency. Our API supports streaming via Server-Sent Events (SSE). The token usage is included in the final chunk, ensuring accurate billing. You do not pay for partial outputs if the request fails. Errors and refusals are free, so you only pay for successful completions.
The request body is limited to 8 MB. This ensures efficient processing and prevents excessive data transmission. You can make up to 300 requests per minute per key. The concurrency limit is 8 requests at the same time per key. This is sufficient for most applications. If you need more concurrency, you can generate additional keys. Each account can have one active key at a time. A new key replaces the old one.
Making the Switch: SDK Compatibility
Our API is fully compatible with the official OpenAI SDKs. You can switch by updating the base URL and API key in your client configuration. The base URL is HTTPS://api.llamaapipricing.com/v1. The model ID to send is "uncensored". This ensures that your existing code works without modification. You can use the same endpoints for chat completions and model listing.
- POST /v1/chat/completions for text generation.
- GET /v1/models for listing available models.
- No embeddings, images, audio, or video generation.
- No fine-tuning or model routing.
The API serves one uncensored large language model. It is an open-weight model run on our own servers. It is not GPT, Claude, Gemini, Grok, DeepSeek, Qwen, or Llama. You can use the official OpenAI SDKs for Python, Node.js, and other languages. The API key is shown immediately after signup. No phone number is required.
Final Verdict: Is It Cheaper Than Claude?
Comparing our API pricing to Claude involves looking at the total cost of ownership. We charge $0.15 per 1M input tokens and $0.50 per 1M output tokens. This is a flat rate with no tiered pricing. Many providers charge more for larger contexts or specialized features. Our model supports a 100k context window without extra fees. This makes it cost-effective for long-context applications.
Additionally, our crypto-only payment model eliminates transaction fees. You receive bonus credits for top-ups, reducing the effective cost. Errors and refusals are free, saving you money on failed requests. The lack of monthly subscriptions further reduces overhead. If you need a reliable, uncensored API for NSFW chatbot or unfiltered chatbot use cases, our pricing is competitive. The trial credit allows you to test the API risk-free.
