Abliterated AI models

Compare abliterated models

How abliterated models work

Uncensored models are reduced-refusal language model variants for open-ended generation, coding and creative work. Uncensored variants use prompting, fine-tuning, weight changes, or a combination of these techniques.

Abliteration reduces refusal-related behavior by modifying model weights. RouterHub serves uncensored and abliterated variants under separate API IDs, with their own token prices and context limits.

Read What is abliteration? for the weight-level technique and primary research.

Choose a model for your workload

Compare model families, API protocols, context windows and token prices. Then run the same prompts and settings across your shortlist to measure task quality and cost.

1. Fix the task. Use the same inputs, output format, reasoning setting and output budget for each exact model ID.
2. Separate refusal from failure. Record an explicit refusal separately from a timeout, empty response, invalid JSON, wrong answer or truncated generation.
3. Keep every attempt. Repeat your tests and retain unsuccessful outputs, not just the best answer.
4. Measure useful work. Compare task acceptance, instruction following, tool-call validity and cost per successful task, not token price alone.
5. Re-test changes. A hosted alias or model update can change behavior even when your client configuration stays the same.

Guides and API setup

• Uncensored vs Abliterated AI Models: Differences & API IDsCompare the techniques and model variants.
• What Is Abliteration? Abliterated AI Models ExplainedUnderstand weight-level refusal reduction.
• Uncensored AI API: Python, cURL & Model SetupConnect with Python or cURL.
• DeepSeek Uncensored & Abliterated Models: API GuideContinue with the full setup guide.
• GLM Uncensored & Abliterated Models: API GuideContinue with the full setup guide.
• Qwen Uncensored & Abliterated Models: API GuideContinue with the full setup guide.
• Kimi K3.1 Pro Uncensored: Pricing & API GuideContinue with the full setup guide.
• How to Evaluate Uncensored & Abliterated AI ModelsCompare task quality and cost with repeatable tests.
• Hosted Uncensored API vs Local Open-Weight ModelsContinue with the full setup guide.

Public references

• Community model libraryRead the source, examples and project notes on GitHub.
• Evaluation prompts, outputs and resultsRead the source, examples and project notes on GitHub.