Choose an uncensored LLM for your workflow
An uncensored LLM is useful only if its answers work for your task. Uncensured AI lets you compare hosted Uncensored and Abliterated editions in browser chat, then use an available model through an OpenAI-compatible API. Start with the output you need and evaluate the exact edition.
What do you need the model to produce?
A writing assistant, a coding client and a document-processing script need different capabilities. Define the expected output: editable prose, a working patch, valid JSON or an answer grounded in supplied material. Use this to set concrete acceptance criteria.
Test your actual instructions, language and input length. A fluent response to a short prompt says little about a long conversation. Review results against requirements written before testing.
What the uncensored LLM label tells you
Uncensored is a broad description, not a shared technical specification. It does not establish a particular training method, modification process or measured level of capability. Uncensured AI uses Uncensored and Abliterated labels to distinguish catalog editions; compare each by its exact model ID.
Abliterated points to modification aimed at refusal behavior. It does not guarantee correctness or reliable instruction following. The abliterated model guide explains the terminology and separates hosted access from modifying a checkpoint.
Compare quality, refusals and integration behavior
Score refusal behavior and task quality separately. Count an answer as useful only when it meets your requirements; a confident response that fails your checks is still a failed task. Keep a few evaluation examples out of your prompt-tuning process so the comparison tests more than memorized instructions.
- Check factual claims against the supplied material or another verifiable reference.
- For code, run the relevant checks; for structured output, validate the schema and required fields.
- If your client uses tools, verify tool selection and arguments with that exact model and client.
- Measure latency and usage on representative requests, including retries and corrections.
Connect an uncensored LLM to your application
Use the OpenAI-compatible /v1/chat/completions endpoint. Follow the API documentation for credentials and connection settings, then send an exact model ID from the current catalog. Test one ordinary request before connecting a longer conversation, editor or automation.
Verify the features your client needs. A successful text response does not establish support for every parameter or tool workflow. Start with the setup guides for supported clients, then test your configuration.
Choose between hosted access and local weights
Browser chat and API requests use the same prepaid dollar balance. Check the catalog and live pricing before estimating a workload. Include retries and corrections in the cost of obtaining a usable result.
Hosted access lets you call the listed editions without operating their inference hardware. Running a checkpoint yourself is a separate choice with its own hardware, licensing and deployment work. This service does not supply downloadable weights or modify checkpoints uploaded by customers.
Models available on Uncensured AI
The list below follows the current catalog. Open a model page for its API ID and current token prices.
- GLM 5.3 Flash Abliterated
glm-5.3-flash-abliterated - GLM 5.3 Abliterated
glm-5.3-abliterated - DeepSeek V4.1 Flash Abliterated
deepseek-v4.1-flash-abliterated - DeepSeek V4 Pro Uncensored
deepseek-v4-pro-uncensored - Qwen 3.8 Max Uncensored
qwen3.8-max-uncensored - GLM 5.3 Uncensored
glm-5.3-uncensored - GLM 5.3 Flash Uncensored
glm-5.3-flash-uncensored - DeepSeek V4.1 Flash Uncensored
deepseek-v4.1-flash-uncensored
Questions and answers
Which uncensored LLM should I choose?
Choose from the current catalog by testing your own tasks. Compare correctness, instruction following, integration behavior, latency and usage. The label alone cannot identify the best edition for a particular application.
Can I use the same model in chat and my app?
Choose an edition available in the catalog and use its exact model ID for API requests. Browser chat is useful for inspecting prompts; test your application's full request separately, including its instructions and generation settings.
Does OpenAI-compatible mean every feature is supported?
It means you can use the documented compatible API format. Check the documentation and verify any parameter, structured output or tool behavior your client requires with the chosen edition.
Are chat and API billed separately?
Both draw from the same prepaid dollar balance. Consult the current model pricing before running a workload, and include retries and longer responses in your usage estimate.