Head to Head

HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive vs google/gemma-4-31B-it

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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google/gemma-4-31B-it

Starting at

0.00 (Self-hosted)

Refund

N/A (Open-source model)

Try Free →

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.

google/gemma-4-31B-it

“Yes, particularly for teams that prioritize open-weight licensing, local deployment, and transparent benchmarking over managed API convenience.”

Gemma 4 31B-it delivers strong reasoning and coding performance for its size, backed by an open Apache 2.0 license and broad ecosystem support. It is a practical choice for developers seeking a capable, locally deployable model without proprietary restrictions.

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

google/gemma-4-31B-it

✓Strong reasoning and coding benchmarks for its parameter size

✓Permissive Apache 2.0 commercial license

✓Broad day-one support for local and cloud inference frameworks

✓Configurable thinking mode for task-specific accuracy

✓Efficient fp8 quantization reduces hardware requirements

✗Self-hosting requires significant GPU VRAM without quantization

✗No official managed API or enterprise SLA from Google

✗Reasoning mode increases token consumption and latency

✗Video input support varies by deployment environment

✗Requires technical expertise for optimal tuning and deployment

Full Breakdown

Category

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

google/gemma-4-31B-it

Overall Rating

★8.2 / 5

4.5 / 5

Starting Price

0.00

0.00 (Self-hosted)

Learning Curve

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

Moderate. Familiarity with local LLM runners (Ollama, vLLM, LM Studio) and basic prompt engineering for reasoning modes is recommended.

Best Suited For

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

Developers, researchers, and enterprises building custom AI pipelines, local inference setups, or fine-tuning projects requiring strong reasoning and multilingual capabilities.

Support Quality

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

Community-driven support via Hugging Face, GitHub, and Discord. Google provides official documentation and developer guides but no dedicated enterprise SLA for the open-weight release.

Hidden Costs

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

GPU/TPU infrastructure, electricity, and potential engineering time for deployment and optimization.

Refund Policy

N/A (Open-weight model)

N/A (Open-source model)

Platforms

Linux, macOS, Windows, Cloud GPU Instances

Linux, macOS, Windows (via WSL/containers), Cloud (GCP, AWS, Azure), On-premise servers

Features

Watermark on Free Plan

✗ No

Mobile App

✗ No

API Access

✓ Yes

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

google/gemma-4-31B-it Review →Try Free →