Choosing an uncensored AI API
An uncensored LLM API is an endpoint that serves models with their refusal behaviour removed or reduced, and puts no moderation layer of its own in front of them. Both halves matter: an uncensored model behind a filtering platform still comes back blocked, and an unfiltered platform serving an aligned model still refuses.
Where a refusal can come from
When a request comes back declined, one of three layers said no, and only one of them is the model:
- A platform filter. A classifier run on the prompt or the reply by the host, which blocks rather than answers. Azure OpenAI and the Gemini API both run one by default; see LLM APIs without a content filter.
- A provider policy. The host's terms decide which models it serves and what it will let you send. A router passes each request to a provider whose policy applies.
- The model itself. Alignment training teaches a model to decline. This is what an uncensored or abliterated build changes.
An uncensored API has to remove the first and pick models that have had the third taken out. The second is the host's choice of what to carry.
Uncensored, abliterated, or just less strict
Three different things get sold under the word. A less strict frontier model (Grok is the usual example) refuses less but is still aligned. An uncensored finetune had its refusal training taken off. An abliterated model had the refusal direction projected out of its weights. Only the last two are uncensored models in the sense the term usually means, and an honest API says which one each model is.
How to check an API before you build on it
- Run your own refusal test. Ten prompts from the work you actually do, sent to each candidate. A vendor's claim is not evidence; your test set is.
- Check tool calling, if you are building an agent. Many uncensored finetunes cannot take a tools array at all, and tool support depends on the host serving the model, not the model card. Look for supports_tools in the model list.
- Read the data policy. What the host stores about each request, for how long, and whether it is used for training. Policies differ widely and change; read the current one.
- Check the spend control. A key that can be capped before the request goes out is a different thing from a balance that is reconciled afterwards. See per-key spend caps.
- Check the format. An OpenAI-compatible endpoint works with almost every client; an Anthropic-compatible one is what Claude Code needs.
The options
Roughly three ways to get an uncensored model behind an API: run the weights yourself on a GPU you own or rent (RunPod, Ollama), subscribe to a host with a large open catalogue (Featherless, Arli AI), or pay per token on a host or gateway that carries uncensored builds (Venice, abliteration.ai, Wild West API). The alternatives pages describe each one.
Wild West API sells a short line of uncensored models, every one of which calls tools, through both API formats, with a hard cap on every key. One of them is abliterated; the rest are uncensored. The models page lists them with live prices.
curl https://wildwestapi.com/v1/chat/completions \ -H "Authorization: Bearer sk-ww-..." \ -H "Content-Type: application/json" \ -d '{ "model": "outlaw-1-xploded", "messages": [{"role": "user", "content": "Hello"}] }'
What it does not change
FAQ
What is an uncensored LLM API?
An API that serves language models with their refusal behaviour removed or reduced, and does not run its own content filter on the prompt or reply.
Is an uncensored LLM API legal?
Serving and calling openly published model weights is not in itself unlawful. The use of the output is governed by the same law as anything else you write or publish.
Does an uncensored model answer everything?
No. Uncensored finetunes can still decline, and abliterated models occasionally do. Neither adds knowledge the base model lacked.
Can an uncensored model call tools?
Some can. It depends on the model and on the host serving it, so check tool support per model before you design an agent around one.