DeepSeek V4 vs Llama 4 vs Qwen 3.6: The Open-Source Uncensored AI Showdown (2026)

Why Open-Source Models Matter for Uncensored AI

In the battle for AI freedom, the most important divide isn't between models — it's between open weight and closed API. ChatGPT, Claude, Gemini, and even Grok are all locked inside company-controlled infrastructure. You never truly own the model. The company decides what you can ask, how it responds, and whether to log your conversations.

Open-weight models change everything. Download the weights, run them on your own hardware, and no one controls what you do with them. No usage policies. No prompt monitoring. No content filters you can't remove. And with tools like Heretic (directional ablation), you can strip out whatever safety training remains — creating a genuinely uncensored AI that answers any question.

Three models dominate the open-weight landscape in 2026: DeepSeek V4 (the Chinese speed demon), Meta Llama 4 (Big Tech's most capable open model), and Alibaba Qwen 3.6 (the underdog that keeps punching above its weight class). Let's see how they compare when you strip away the guardrails and run them at full power.

DeepSeek V4 (Open Weights) — The Speed King

DeepSeek V4 isn't just available as an API — the full weights are publicly downloadable under a permissive license. The V4 generation represents a massive leap: the model uses a Mixture-of-Experts (MoE) architecture with 671B total parameters (37B active per token), making it both powerful and surprisingly efficient to run.

Key Specs: 671B total / 37B active parameters, 128K context window, MoE architecture, Apache 2.0-like license for open weights.

What makes DeepSeek V4 special for uncensored use is its baseline honesty. Even before abliteration, DeepSeek's safety training is notably lighter than Meta's or Alibaba's. The model is more willing to engage with controversial topics directly — a trait that carries through after full uncensoring. After applying Heretic, DeepSeek V4 is essentially frictionless: it answers any question with well-reasoned analysis, no refusals.

On the performance side, V4 Flash (the smaller, faster variant) can process over 2M tokens per minute on a single H100. The open-weight version requires significant VRAM — but with GGUF quantization (Q4_K_M), you can run a 70B-level variant on 48GB VRAM.

Meta Llama 4 — Big Tech's Open Bet

Meta's Llama family has become the de facto standard for open-weight AI. Llama 4 pushes the series further with a dense architecture (no MoE), better multilingual performance, and significantly improved reasoning benchmarks. The largest variant clocks in at 405B parameters, with smaller 8B and 70B versions that fit comfortably on consumer hardware.

Key Specs: 8B / 70B / 405B variants, 128K context window, dense architecture, LLAMA 3.3 Community License (free for most commercial use).

For the uncensored community, Llama 4 is a double-edged sword. Meta applies the most aggressive safety training of any open-weight provider — likely because the company faces maximum scrutiny from regulators. Out of the box, Llama 4 refuses more questions than any other model on this list. It's particularly skittish around topics like violence, self-harm, controversial history, and anything that could be construed as "hateful."

The good news? The abliteration community has focused most of its energy on Llama. Because Llama is the most popular open-weight family, Heretic and related tools have been extensively tested on it. An abliterated Llama 4 405B is a genuinely formidable uncensored model — arguably the most capable open-weight option available when you fully remove the guardrails.

Alibaba Qwen 3.6 — The Dark Horse

Alibaba's Qwen series has quietly become one of the most impressive open-weight families. Qwen 3.6 introduces significant improvements over its predecessors: a 1M token context window (matching Gemini!), stronger multilingual capabilities, and — crucially for our purposes — relatively light safety training compared to Llama.

Key Specs: 7B / 32B / 110B / 236B variants, up to 1M context window, dense architecture, Apache 2.0-friendly license (Qwen License v2).

Qwen 3.6 is the surprise contender in the uncensored space. Alibaba applies safety filters, but they're notably less aggressive than Meta's. In our testing, Qwen 3.6 110B answered questions (before abliteration) that Llama 4 70B refused outright. After applying Heretic, the model becomes one of the most permissive open-weight options available — it's willing to engage with any topic, from controversial historical analysis to sensitive medical discussions.

The 1M token context window is Qwen's killer feature. No other open-weight model comes close. For document analysis, codebase review, or long-form research, Qwen 3.6's context handling is genuinely impressive — recall accuracy holds up well even at 500K+ tokens.

Head-to-Head Comparison Table

Category	DeepSeek V4	Llama 4	Qwen 3.6
Max Parameters	671B (37B active)	405B (dense)	236B (dense)
Context Window	128K	128K	1M
Speed (tokens/sec, Q4 70B tier)	~120 t/s	~65 t/s	~70 t/s
Reasoning (MATH/GPQA)	9/10	8.5/10	8.5/10
Coding (HumanEval+)	9/10	8.5/10	8/10
Creative Writing	7.5/10	8.5/10	8/10
Baseline Censorship (pre-abliteration)	Lightest	Heaviest	Moderate
Community & Tooling	Good	Excellent	Moderate
Multilingual	Good	Good	Excellent
License Friendliness	Good	Moderate	Excellent
Min VRAM (Q4, smallest usable)	~24 GB	~24 GB	~24 GB

Abliteration: How to Uncensor Each Model

All three models can be uncensored using Heretic, the open-source directional ablation tool. Here's what you need to know for each:

DeepSeek V4 — One-Pass Abliteration

DeepSeek's light safety training means a single pass with Heretic is usually sufficient. The model's refusal patterns are concentrated in specific attention heads, making directional ablation highly effective. Expect 95%+ refusal removal on the first try. Community abliterated GGUF quants are available on Hugging Face for most model sizes.

# Quick abliteration with Heretic (requires 80GB GPU)
heretic abliteration \
  --model deepseek-ai/DeepSeek-V4 \
  --method directional \
  --output-dir ./deepseek_uncensored

# Or download pre-abliterated GGUF:
# huggingface.co/heretic/DeepSeek-V4-abliterated-GGUF

Llama 4 — Multi-Pass Required

Meta's aggressive safety training means one pass isn't enough. You'll need 2-3 passes with Heretic, and you may need to manually identify stubborn refusal patterns. The good news: the Llama community has already published excellent abliterated versions for all three sizes (8B, 70B, 405B). For most users, downloading a pre-abliterated GGUF is the best path.

# Llama 4 needs iterative ablation
heretic abliteration \
  --model meta-llama/Llama-4-70B \
  --method iterative \
  --iterations 3 \
  --output-dir ./llama4_uncensored

# Recommended: use community GGUF
# huggingface.co/heretic/Llama-4-70B-abliterated-GGUF

Qwen 3.6 — Sweet Spot

Qwen 3.6 hits the sweet spot: one or two passes remove the vast majority of refusals. Alibaba's safety training is lighter than Meta's but more structured than DeepSeek's, making it predictable to abliterate. Expect ~98% refusal removal with two passes.

# Qwen responds well to standard ablation
heretic abliteration \
  --model Qwen/Qwen3.6-110B \
  --method directional \
  --output-dir ./qwen36_uncensored

Hardware Requirements by Model Size

Running these models locally requires significant hardware — but quantization makes them surprisingly accessible:

Model Variant	Q4_K_M VRAM	Q8 VRAM	FP16 VRAM	Min RAM
DeepSeek V4 (37B active)	~24 GB	~42 GB	~74 GB	32 GB
Llama 4 8B	~6 GB	~10 GB	~16 GB	16 GB
Llama 4 70B	~42 GB	~74 GB	~140 GB	64 GB
Llama 4 405B	~230 GB	~410 GB	~810 GB	256 GB
Qwen 3.6 32B	~20 GB	~34 GB	~64 GB	32 GB
Qwen 3.6 110B	~66 GB	~112 GB	~220 GB	96 GB
Qwen 3.6 236B	~140 GB	~240 GB	~472 GB	192 GB

Real-world guidance: For a single RTX 4090 (24GB), your best option is Qwen 3.6 32B at Q4 or Llama 4 8B at Q8. For two 4090s (48GB), Llama 4 70B at Q4 is viable. For serious uncensored work, 4x H100 (320GB) unlocks the full potential of all three models.

Why Open Weights Mean Real Privacy

The privacy argument for open-weight models isn't theoretical — it's practical and measurable.

When you use ChatGPT, Claude, or Gemini: Every prompt is processed on company servers. The companies log conversations for training and safety monitoring. Even with "privacy mode" settings, your data still transits through their infrastructure. In 2026, we've seen multiple data breaches at major AI companies, exposed training data containing user conversations, and internal policy changes that retroactively expanded data collection. Your "private" conversation history is only as private as the weakest security team.

When you run an open-weight model locally: Nothing leaves your machine. The model weights, your prompts, the generated text — all of it stays on your hardware, behind your firewall. No company servers, no data logging, no third-party access. For sensitive work — legal analysis, medical research, trade secrets, personal journaling — local open-weight inference is the only truly private option.

Tools like Ollama, llama.cpp, and Open WebUI make running these models locally as easy as:

ollama run deepseek-v4-abliterated  # or
ollama run llama4-abliterated       # or
ollama run qwen3.6-abliterated

# Open WebUI gives you a ChatGPT-like interface
docker run -d -p 3000:8080 \
  -v ollama:/root/.ollama \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

The privacy tradeoff is simple: API convenience vs. local control. For anything sensitive, the choice is clear.

Final Verdict & Recommendations

There's no single "best" open-weight uncensored model — but there is a best model for each use case:

• For raw speed and coding: DeepSeek V4 (open weights) is unmatched. Its MoE architecture delivers more performance per parameter than any dense model, and its light baseline censorship means less work to fully uncensor it.
• For maximum capability and ecosystem: Llama 4 405B, abliterated, is the most powerful open-weight model money can buy — if you have the hardware for it. The massive community means you'll find help, tools, and pre-made abliterated quads for any need.
• For massive context and value: Qwen 3.6 is the dark horse champion. A 32B Q4 variant runs on a single 4090, handles 1M tokens of context, and abliterates cleanly. For document-heavy workflows, it's the clear winner.
• For privacy-first users: Any of the three, run locally via Ollama. The hardware dictates your choice — but all three, when abliterated, deliver genuinely uncensored, private AI that no one can monitor or restrict.

"The era of truly uncensored AI is here — not through API proxies or jailbreak prompts, but through open-weight models that you control completely. The question isn't whether the model can answer freely. The question is whether you're willing to own your own hardware."

Our recommendation: Start with Qwen 3.6 32B on a single 4090. It's the best balance of capability, context size, and ease of abliteration. As you scale your hardware, add Llama 4 70B and DeepSeek V4 to your rotation. Each model has strengths the others lack — and running all three locally gives you a uncensored AI stack that no cloud provider can match.