Alibaba Cloud (Qwen)Qwen3.6-35B-A3BVSGoogleGemma 4 12B
Our Take
We recommend Qwen3.6-35B-A3B for superior reasoning capability, or Gemma 4 12B for faster local inference on smaller GPU hardware. While both models run locally for zero API costs, Qwen3.6-35B-A3B carries a larger parameter memory footprint. Choose Qwen3.6-35B-A3B if you have the VRAM to support it, or Gemma 4 12B for consumer-grade hardware compatibility.
▶WHY?
Benchmark Calculations & Evidence:
Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. Qwen3.6-35B-A3B scored 86%, while Gemma 4 12B scored 78.8% (+7.2% gap).
Coding Performance: Evaluated on different benchmarks. Qwen3.6-35B-A3B scored 49.5% on SWE-bench Pro (complex codebases, multi-file repositories, and architectural planning), while Gemma 4 12B scored 72% on LiveCodeBench (scripting single-file apps or clearly defined functions).
Hardware Footprint: Qwen3.6-35B-A3B is a 35B parameter model requiring more VRAM, while Gemma 4 12B is a 11.95B parameter model.
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Benchmarks & Scores
Coding (swe-bench-pro)
49.5%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+7.2%)
86%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
Self HostLocal execution (zero API fees)Context Window
262.14k tokensBenchmarks & Scores
Coding (live-code-bench)
72%scripting single-file apps or clearly defined functions
Reasoning (gpqa-diamond)
78.8%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
Self HostLocal execution (zero API fees)Context Window
262.14k tokensFrequently Asked Questions about Qwen3.6-35B-A3B vs Gemma 4 12B
For coding tasks, Qwen3.6-35B-A3B scores 49.5% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while Gemma 4 12B scores 72% on live-code-bench (scripting single-file apps or clearly defined functions).
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