Best Abliterated Models 2026: Community Rankings

This ranking is based on community testing across four categories: coding, reasoning, creative writing, and instruction following. All models listed are open-weight, locally runnable, and have abliterated or uncensored variants available on HuggingFace.

Overall Rankings

Rank	Model	Params	Overall	Best For
1	Llama 3.1 70B Abliterated	70B	94	All-round
2	DeepSeek R1 70B Abliterated	70B	93	Reasoning
3	Qwen 3 72B Uncensored	72B	93	Coding
4	Mistral 7B Abliterated	7B	80	Fast/light
5	Llama 3.1 8B Abliterated	8B	80	Entry level
6	Gemma 2 27B Abliterated	27B	86	Mid-range
7	Mixtral 8x7B Uncensored	56B MoE	83	Creative

Best for Coding

Winner: Qwen 3 72B Uncensored (score: 96)

Qwen 3 72B has the strongest coding benchmark scores in the open-weight space at 72B scale, and the uncensored variant retains this capability almost entirely. HumanEval score of 86.1% places it ahead of every other locally-runnable model.

Runner-up: DeepSeek R1 70B Abliterated (94) — exceptional for complex algorithmic problems due to chain-of-thought reasoning being intact post-abliteration.

Best for Reasoning

Winner: DeepSeek R1 70B Abliterated (score: 97)

DeepSeek R1 was trained specifically for chain-of-thought reasoning and achieves 94.1% on MATH-500. The abliterated variant preserves the reasoning chain fully — the abliteration technique only removes refusal direction vectors, leaving the reasoning pathways intact.

The gap between R1 abliterated and Llama 3.1 70B abliterated on multi-step math problems is substantial — 94.1% vs 68.3% on MATH-500.

Best for Creative Writing

Winner: Llama 3.1 70B Abliterated (score: 96)

Llama 3.1 70B has the strongest creative writing output in community testing. Post-abliteration it handles fiction, roleplay, and long-form creative tasks without arbitrary topic restrictions. Community consensus is that the 70B scale is meaningfully better than 7-8B for creative coherence over long outputs.

Runner-up: Mixtral 8x7B Uncensored (86) — the MoE architecture produces more varied and surprising creative output than dense models of similar active parameter counts.

Best Entry-Level (Under 10GB VRAM)

Winner: Mistral 7B Abliterated (score: 80)

The cleanest abliteration at 7B scale with 99.2% quality retention. Runs at 90+ tok/s on an RTX 4090 at Q8_0. The quality gap between Mistral 7B and Llama 3.1 8B is minor — both are good entry points.

Llama 3.1 8B Abliterated is the alternative if you want the Llama architecture specifically. Slightly stronger instruction following, marginally weaker on raw reasoning.

Best Mid-Range (10–24GB VRAM)

Winner: Gemma 2 27B Abliterated (score: 86)

Gemma 2 27B punches above its weight class — it outperforms many 70B models on reasoning tasks relative to its size. On a single RTX 4090 at Q4_K_M it delivers 44 tok/s with strong output quality.

Quality Retention by Model

How much does abliteration cost each model on standard benchmarks?

Model	MMLU Base	MMLU Abliterated	Loss
Mistral 7B	64.2%	63.7%	0.5%
Llama 3.1 8B	73.0%	72.4%	0.6%
Llama 3.1 70B	83.6%	82.1%	1.5%
Qwen 3 72B	83.1%	81.3%	1.8%
DeepSeek R1 70B	85.1%	82.7%	2.4%

Quality loss increases slightly with model size but remains well within acceptable range for all listed models.

Hardware Requirements

Model	Min VRAM	Recommended Config
Mistral 7B / Llama 8B	6GB	Any RTX 30/40 series
Gemma 2 27B	16GB	RTX 4080 or 4090
Llama 3.1 70B	40GB	Dual RTX 3090 NVLink
Qwen 3 72B	40GB	Dual RTX 3090 NVLink
DeepSeek R1 70B	40GB	Dual RTX 3090 NVLink

Finding These Models

All models listed have GGUF variants available on HuggingFace. Search [model name] abliterated GGUF or visit the DefiledAI uncensored database for direct links and curated quant options.

Community rankings are updated monthly based on forum submissions and benchmark data. Submit your results to contribute.