Head to Head

HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive vs unsloth/Qwen3.6-35B-A3B-GGUF

Pricing, experience, and what the community actually says.

HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

Starting at

0.00

Refund

N/A (Open-weight model)

Try Free →

★ Our Pick

unsloth/Qwen3.6-35B-A3B-GGUF

unsloth/Qwen3.6-35B-A3B-GGUF

Starting at

0

Refund

N/A (Open-source model)

Try Free →

Our Take

HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-AggressiveHauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

Yes, for developers and researchers who require an open-weight, uncensored MoE model with extensive quantization options and strong reasoning capabilities.

A highly capable, unrestricted variant of the Qwen3.6-35B-A3B architecture, optimized for local deployment and specialized workflows requiring unfiltered outputs.

unsloth/Qwen3.6-35B-A3B-GGUFunsloth/Qwen3.6-35B-A3B-GGUF

Yes, for developers and researchers seeking a capable, locally runnable LLM with a permissive Apache 2.0 license and low VRAM requirements.

A highly efficient, open-weight MoE model that delivers strong coding and tool-calling capabilities while running on consumer hardware via GGUF quantization.

Pros & Cons

HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

Completely removes safety refusal filters
Wide range of lossless GGUF quantizations for flexible hardware deployment
Strong coding and reasoning capabilities for its size
Native multimodal and long-context support
Free to download and self-host
Requires substantial VRAM for higher precision formats
Lacks built-in content moderation, requiring external safeguards
No official vendor support or SLA
Aggressive variant may produce unverified or harmful outputs without careful prompting

unsloth/Qwen3.6-35B-A3B-GGUF

Runs efficiently on consumer hardware (18-20GB VRAM at 4-bit)
Permissive Apache 2.0 license
Strong tool-calling and coding performance
Extensive framework compatibility
Free to download and modify
Requires technical setup for local deployment
Full-precision version demands enterprise GPUs
Incremental improvements over Qwen 3.5
Lower quantization levels may slightly impact output nuance
No official enterprise support tier

Full Breakdown

Category
HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-AggressiveHauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive
unsloth/Qwen3.6-35B-A3B-GGUFunsloth/Qwen3.6-35B-A3B-GGUF

Overall Rating

8.2 / 5
8.5 / 5

Starting Price

0.00
0

Learning Curve

Moderate; requires familiarity with local LLM inference tools like LM Studio, Ollama, or vLLM.
Moderate. Users need basic knowledge of GGUF formats, inference servers, and prompt configuration for optimal results.

Best Suited For

Local AI deployment, uncensored content generation, agentic coding workflows, and long-context reasoning tasks.
Developers, AI researchers, and hobbyists running local inference, fine-tuning, or building agentic workflows on consumer GPUs or Apple Silicon.

Support Quality

Community-driven support via Hugging Face discussions and Discord. No official enterprise SLA.
Community-driven via Hugging Face discussions, GitHub issues, and Unsloth documentation. No dedicated enterprise support for the open-weight model.

Hidden Costs

Compute costs for local hosting (GPU hardware, electricity) or cloud inference fees if deployed via third-party providers.
Hardware costs for local deployment; cloud compute fees if using hosted inference or Unsloth Pro.

Refund Policy

N/A (Open-weight model)
N/A (Open-source model)

Platforms

Linux, macOS, Windows, Cloud GPU Instances
Linux, macOS (Apple Silicon), Windows (via WSL/llama.cpp), Cloud GPU instances

Features

Watermark on Free Plan

✗ No
✗ No

Mobile App

✗ No
✗ No

API Access

✓ Yes
✓ Yes