What Happened

A developer on r/LocalLLaMA ran an informal but reproducible benchmark: feeding the identical prompt — "write a python turtle program that draws a cat" — to six different models. Local models tested included Gemma 4 31B (IQ3_XXS quantized via llama.cpp GGUF), Qwen3.5 9B at Q8_0, and Qwen3.5 27B Opus Distilled at Q4_K_S. Cloud models included DeepSeek via browser, Claude Sonnet 4.6 with extended thinking, and Gemini Pro with thinking mode enabled. Hardware was a 16 GB VRAM GPU, forcing quantization on the larger local models.

Why It Matters

Python turtle is an underrated code-generation benchmark because output is visually verifiable without a test suite. The task requires spatial reasoning, color selection, and structured procedural code — not just syntax correctness. Key findings from this test:

  • Gemma 4 31B and Gemini Pro produced visually similar outputs — same color palette and minimalist detail level — suggesting shared training data lineage or RLHF preference alignment.
  • Qwen3.5 27B Opus Distilled runs at Q4_K_S on 16 GB VRAM, making it accessible to mid-range consumer hardware.
  • Cloud models with reasoning modes (Claude extended, Gemini thinking, DeepSeek) are now being directly compared to quantized local models by indie developers — a sign the capability gap is narrowing.

For indie developers and SMEs evaluating local deployment, this test confirms that quantized 27B models are viable on a single consumer GPU for creative coding tasks.

Asia-Pacific Angle

Qwen3.5 — developed by Alibaba's Qwen team — continues to be a strong default choice for developers in China and Southeast Asia who need local inference without cloud API costs or data residency concerns. The 9B Q8_0 variant fits entirely in 16 GB VRAM with no quantization compromise, while the 27B Opus Distilled at Q4_K_S offers higher capability at acceptable quality loss. For teams in markets with unreliable API access to OpenAI or Anthropic, Qwen3.5 27B quantized represents a production-viable local alternative. DeepSeek's inclusion as a browser-based cloud option also reflects its growing adoption across the Asia-Pacific region as a cost-effective reasoning model.

Action Item This Week

Download Qwen3.5 9B Q8_0 via Ollama or llama.cpp, run the exact prompt "write a python turtle program that draws a cat", then compare output visually against a Claude or Gemini API call — this gives you a concrete, zero-cost baseline for local vs. cloud code generation quality on your own hardware.