Best Uncensored LLMs (2026): Abliterated and Unrestricted Models Compared

TL;DR

Last updated November 5, 2026. Prices read from each provider's live pricing page that day.

An uncensored LLM is a language model that answers requests a standard chat model refuses. In 2026 most of them are abliterated: open-weight models with the refusal direction projected out of their weights, no retraining required. The best uncensored LLM for production and agent work is DeepSeek V4.1 Flash Abliterated, the DeepSeek V4.1 Flash 1017 checkpoint with refusals removed and a 1M-token context. Morph serves it as morph-dsv41flash-abliterated on an OpenAI-compatible API at $0.30/M input and $1.20/M output, the lowest per-token rate we found for that checkpoint. For casual chat and roleplay, Venice Uncensored 1.2 is the cheapest small option. For a large neutral-alignment fine-tune, Hermes 4 405B. For fully offline use on one GPU, an abliterated Qwen 3.8 27B GGUF. Every abliterated model gives up some quality; the size of the loss depends on the method.

Best Uncensored LLMs in 2026

Five models cover nearly every reason people look for an uncensored LLM. They differ on three axes that matter more than the word "uncensored": how big the base model is, how the refusals were removed, and whether you can call it from code.

What Is an Uncensored LLM?

Every major chat model ships with refusal training. The lab fine-tunes it to decline requests that match a policy: malware, exploit code, weapons, explicit content, and a long tail of false positives. An uncensored LLM is any model where that behavior has been removed or never added. The terms uncensored AI, unrestricted AI, unfiltered AI and "AI with no restrictions" all describe the same thing from different directions.

The false positives are why professionals care. Incident responders paste attack logs that contain real exploit payloads. Malware analysts need a model to explain what a sample does. Red teams need working proof-of-concept code to confirm a bug is real. A refusal-trained model cannot tell those people from an attacker, so it refuses both. We wrote about that failure in Why We Serve an Uncensored DeepSeek.

What Does Abliterated Mean?

Abliterated means the model's refusal behavior was removed by editing its weights directly. The word blends ablate and obliterate, which is why people also search for "obliterated models". The method comes from Arditi et al. (2024), who found that refusal in 13 open chat models up to 72B parameters is mediated by a single direction in the residual stream.

1. Run the model on a set of harmful prompts and a set of harmless prompts.
2. Take the difference of the mean activations at a chosen layer. That vector is the refusal direction.
3. Orthogonalize the weight matrices that write to the residual stream against it, so no layer can write that direction again.

No gradient step is involved, which is why abliteration takes hours, not weeks. Maxime Labonne's Hugging Face write-up made it popular in June 2024, and Heretic automates it by searching abliteration parameters that minimize both refusals and KL divergence from the original model.

Abliterated vs Uncensored vs Jailbroken

Jailbreaks are the least reliable path. They work against closed models until the provider patches them, and they add a long adversarial preamble to every request. If your workload needs refusals gone, use a model whose weights say so.

Does Abliteration Hurt Quality?

Yes, by an amount that depends on the tool and the model. A 2025 cross-architecture study ran four abliteration tools (Heretic, DECCP, ErisForge, FailSpy) on 16 instruction-tuned models between 7B and 14B.

Math reasoning was the most sensitive capability. Labonne's original write-up showed the same pattern: the abliterated 8B model lost ground on every benchmark, and a DPO "healing" pass recovered most of it except GSM8K. Two practical rules follow. Start from the largest base you can afford, because a small loss on a frontier-scale model still leaves you ahead of a 7B model at full strength. And evaluate the abliterated checkpoint against its base on your own tasks before you depend on it.

Uncensored LLM API Pricing Compared

Per-token prices for hosted uncensored and abliterated models, per 1M tokens, read from each provider's pricing page on November 5, 2026.

Two subscription hosts price differently. Featherless hosts abliterated and uncensored Hugging Face models on a $25/month chat plan (32K context, interactive use only) or a $50/month developer plan billed per token (up to 256K context). abliteration.ai sells an OpenAI- and Anthropic-compatible API on plans from $20/month that include matching usage credit.

Local vs Hosted Uncensored Models

Running an uncensored model locally is the most private option and costs nothing per token. Ollama lists abliterated builds from 1B to about 30B, and huihui-ai's abliterated Qwen 3.8 27B GGUF has more than 3.5M downloads on Hugging Face. The ceiling is the GPU on your desk: a 27B model at 4-bit runs on one consumer card, a frontier-scale MoE does not.

The split is simple. If you need privacy above everything and a 27B model is good enough, run it locally. If you need the strongest abliterated model, a million-token context, or hundreds of agents in parallel, use an API.

Uncensored LLM Leaderboard

The community reference is the UGI (Uncensored General Intelligence) Leaderboard on Hugging Face. It scores models on how willing they are to answer topics models usually refuse, and separately on knowledge and reasoning. Read both columns together: a model that answers everything but scores low on intelligence is uncensored and useless. The leaderboard does not measure whether a model produces working exploit code or correct incident analysis, so for security work, build a small eval from your own tickets.

DeepSeek V4.1 Flash Abliterated on Morph

Morph serves the DeepSeek V4.1 Flash 1017 checkpoint with its refusal direction removed, as morph-dsv41flash-abliterated. The base model is MIT-licensed, and the 1M-token context is large enough to load an incident's full logs or a whole repository in one request. It runs on the same OpenAI-compatible API as every other model in the Morph lineup.

The gate is the tradeoff. If you want anonymous, instant access for roleplay, Venice or a local model fits better. If you are a security team that needs the strongest abliterated model at the lowest per-token price, request access. The full terms are in our data retention policy.

How to Call the Uncensored LLM API

Once access is approved, any OpenAI client works. Change the base URL and the model name.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.morphllm.com/v1",
    api_key="YOUR_MORPH_API_KEY",
)

resp = client.chat.completions.create(
    model="morph-dsv41flash-abliterated",
    messages=[
        {"role": "system", "content": "You are assisting an authorized incident response team."},
        {"role": "user", "content": "Explain what this PowerShell stager does, step by step:\n<payload>"},
    ],
)
print(resp.choices[0].message.content)

Accounts without access get a 403. The same key works for every other Morph model, so a pipeline can route only the refused steps to the abliterated model and keep everything else on DeepSeek V4.1 Flash 1017.

Pros and Cons of Uncensored LLMs

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