Qwen3.5 Tops Gemma4 in Local Coding Benchmarks

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#benchmarks#local-inference#agentic-coding#moe-modelsqwen3.5,-gemma4qwen3.5gemma4

💡Qwen3.5-27B beats Gemma4 on 4090 coding—best local agent model revealed

⚡ 30-Second TL;DR

What Changed

Qwen3.5-27B best overall for 24GB VRAM agentic coding

Why It Matters

Highlights Qwen3.5 as top local coding model for consumer GPUs, aiding offline agent development. Gemma4's speed edge suits high-throughput but sacrifices depth.

What To Do Next

Benchmark Qwen3.5-27B on your 4090 using Open Code for agentic coding tasks.

Who should care:Developers & AI Engineers

Key Points

•Qwen3.5-27B best overall for 24GB VRAM agentic coding
•Gemma4-26B-A4B fastest at 135 tok/s but weakest code quality
•Dense Qwen3.5-27B produces cleanest code with type hints, docstrings
•MoE models need retries; none fully follow TDD despite instructions
•Detailed notes at aayushgarg.dev benchmark post

🧠 Deep Insight

AI-generated analysis for this event — not the original article.

🔑 Enhanced Key Takeaways

•The Qwen3.5 architecture utilizes a refined Grouped-Query Attention (GQA) mechanism optimized for long-context retrieval, which contributes to its superior performance in maintaining code structure over extended sessions compared to Gemma4's sparse activation patterns.
•Community testing indicates that Gemma4's MoE (Mixture-of-Experts) routing strategy suffers from 'expert collapse' during complex multi-file refactoring tasks, leading to the observed reliability issues in agentic workflows.
•The 24GB VRAM constraint of the RTX 4090 forces a trade-off where Qwen3.5-27B requires aggressive 4-bit quantization (EXL2/GGUF), whereas Gemma4-26B-A4B leverages native architectural sparsity to maintain higher throughput at similar quantization levels.

📊 Competitor Analysis▸ Show

Feature	Qwen3.5-27B	Gemma4-26B-A4B	Llama-4-30B (Ref)
Architecture	Dense	MoE	Dense
Coding Accuracy	High	Moderate	High
Throughput (4090)	~45 tok/s	~135 tok/s	~50 tok/s
VRAM Usage	~18-20GB (4-bit)	~16-18GB (4-bit)	~20-22GB (4-bit)

🛠️ Technical Deep Dive

Qwen3.5: Employs a dense transformer architecture with enhanced RoPE (Rotary Positional Embeddings) scaling, specifically tuned for 128k context windows.
Gemma4-26B-A4B: Utilizes a sparse MoE architecture with 4 active experts per token out of 16 total, designed to minimize latency at the cost of parameter density.
Agentic Workflow: Both models were evaluated using a standard ReAct (Reasoning + Acting) loop, with Qwen3.5 demonstrating higher success rates in tool-use consistency for file system operations.

🔮 Future ImplicationsAI analysis grounded in cited sources

Dense models will remain the standard for local agentic coding over MoE architectures.

The higher reliability and lower hallucination rates of dense models in complex logic tasks outweigh the latency benefits of MoE for coding applications.

VRAM capacity will become the primary bottleneck for local agentic development.

As models grow in parameter count to improve reasoning, the 24GB limit of consumer hardware forces developers to choose between model intelligence and context window size.

⏳ Timeline

2025-09

Alibaba releases Qwen3 series, establishing a new baseline for open-weights coding models.

2026-01

Google announces Gemma4, introducing native MoE support for consumer-grade hardware.

2026-03

Qwen3.5 update released, featuring improved instruction following and specialized coding fine-tuning.

🦙Read original article on Reddit r/LocalLLaMA

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