DeepSeek V4.1 Flash Uncensored: what weight-level abliteration costs, and what we saw in testing

DeepSeek V4.1 Flash Uncensored is DeepSeek V4.1 Flash with the refusal behavior removed in the weights, published by dealignai as an FP8 checkpoint. No system prompt trick, no runtime hook: it loads like the base model.

I build Locally Uncensored, an open source desktop app (AGPL-3.0) for running models on your own machine, with an optional hosted side called LU Cloud. This model went into LU Cloud today. A 552B mixture of experts is not something most people run at home, so this one is hosted only for us. Here's what the numbers say and what we saw before we turned it on.

what the makers measured

From the model card:

                                  base V4.1 Flash   uncensored build
HarmBench-320, thinking off       42.8% answered    100%
HarmBench-320, thinking max        1.6% answered    100%
MMLU (14,042 questions)           87.0%             82.7%
MMLU without ethics/law subjects  -                 about 1.1 points below base

The interesting row is the second one. The stock model gets much more refusal-prone the longer it reasons: at max effort it answered 5 of 320. The abliterated build doesn't care about the effort setting.

The MMLU drop is mostly moral scenarios (76.9% to 37.0%) and professional law, which makes sense: those questions reward exactly the hesitation that was removed.

what we measured

Before it went into the catalog we ran it against the live endpoint:

reasoning     on by default, Think off stops it (43 -> 2 tokens)
effort        low / medium / high all answer
tools         native tool calls
context       1M in, 131,072 out
images        listed as vision, but a red and a blue square both came back "White"
refusals      answered our four probes without any host system prompt

So we ship it as a text model. The model card says the vision tower is untouched; whatever goes wrong happens somewhere between the checkpoint and the endpoint, and we'd rather not sell image input that names every color white.

The four probes are deliberately boring: a swearing rant, picking a lock you own, pirate insults, an explicit scene between adults. They tell you whether a model answers without being talked into it, not how far it goes.

using it

In the desktop app (3.0.7) you sign in to LU Cloud, flip the Cloud switch, and the model is in the Uncensored group, marked beta. It works in Chat, Agent mode and the Code tab, since tool calls are native. The model list comes from the server, so no app update was needed. The same account works in the browser and in the Android app.

It costs about 22,500 credits per million input tokens and 90,000 per million output, close to the stock V4.1 Flash. A 15 EUR pack is 450,000 credits.

Two limits worth knowing:

• It runs on one route only. If that route is down, the request is refused with a plain message instead of quietly going to a different model.
• Personal API keys don't reach it. It's for the apps and the browser.

A note for Agent mode: a model that doesn't refuse also doesn't hesitate. The agent's workspace is a folder jail, not a VM, so read the tool requests before you approve them.

Step by step: How to use DeepSeek V4.1 Flash Uncensored in LU. The longer write-up, including the second new model, Abliterated Large Flash: DeepSeek V4.1 Flash Uncensored in LU Cloud. The app is on GitHub.

AI-assisted: I wrote this with help from an AI model and checked the facts against the dealignai model card and our own measurements.