Head to Head

HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive vs z-lab/Qwen3.6-35B-A3B-DFlash

Pricing, experience, and what the community actually says.

★ Our Pick

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

Starting at

0.00

Refund

N/A (Open-weight model)

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z-lab/Qwen3.6-35B-A3B-DFlash

Starting at

Refund

Open-weight model; no refunds applicable.

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Our Take

HauhauCS/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.

z-lab/Qwen3.6-35B-A3B-DFlash

“Yes for developers and researchers with adequate GPU resources who prioritize open licensing, local deployment, and agentic coding workflows.”

A highly capable open-weight MoE model that delivers strong coding and reasoning performance with efficient inference, though it requires substantial local hardware and technical setup.

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

z-lab/Qwen3.6-35B-A3B-DFlash

✓Strong coding and repository-level reasoning

✓Efficient MoE architecture reduces active compute

✓Thinking preservation improves iterative workflows

✓Permissive Apache 2.0 licensing

✓Compatible with major open-source inference frameworks

✗Requires ~24GB VRAM for full deployment

✗Setup and optimization require technical expertise

✗No official enterprise support or SLA

✗Raw inference speed depends heavily on backend configuration

Full Breakdown

Category

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

z-lab/Qwen3.6-35B-A3B-DFlash

Overall Rating

★8.2 / 5

4.3 / 5

Starting Price

0.00

Learning Curve

Moderate; requires familiarity with local LLM inference tools like LM Studio, Ollama, or vLLM.

Moderate to high; requires familiarity with LLM inference frameworks (vLLM, SGLang, Transformers) and hardware optimization.

Best Suited For

Local AI deployment, uncensored content generation, agentic coding workflows, and long-context reasoning tasks.

Software engineers, AI researchers, and developers building local or self-hosted AI agents, code assistants, and long-context applications.

Support Quality

Community-driven support via Hugging Face discussions and Discord. No official enterprise SLA.

Community-driven support via Hugging Face discussions, GitHub issues, and developer forums. No official enterprise SLA.

Hidden Costs

Compute costs for local hosting (GPU hardware, electricity) or cloud inference fees if deployed via third-party providers.

Hardware requirements (24GB+ VRAM) and potential cloud GPU rental fees for inference hosting.

Refund Policy

N/A (Open-weight model)

Open-weight model; no refunds applicable.

Platforms

Linux, macOS, Windows, Cloud GPU Instances

Features

Watermark on Free Plan

✗ No

Mobile App

✗ No

API Access

✓ Yes

HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive Review →Try Free →

z-lab/Qwen3.6-35B-A3B-DFlash Review →Try Free →