GoogleGemma 4 12BVSGoogleGemma 4 E2B
Our Take
We recommend Gemma 4 12B for superior reasoning capability, or Gemma 4 E2B for faster local inference on smaller GPU hardware. While both models run locally for zero API costs, Gemma 4 12B carries a larger parameter memory footprint. Choose Gemma 4 12B if you have the VRAM to support it, or Gemma 4 E2B for consumer-grade hardware compatibility.
▶WHY?
Benchmark Calculations & Evidence:
Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. Gemma 4 12B scored 78.8%, while Gemma 4 E2B scored 43.4% (+35.4% gap).
Coding Performance: Both models were evaluated on the LiveCodeBench benchmark. Gemma 4 E2B scored 44%, while Gemma 4 12B scored 72% (+-28.0% gap).
Hardware Footprint: Gemma 4 12B is a 11.95B parameter model requiring more VRAM, while Gemma 4 E2B is a 2.3B parameter model.
Was this recommendation helpful?
Benchmarks & Scores
Coding (live-code-bench)Winner (+28.0%)
72%scripting single-file apps or clearly defined functions
Reasoning (gpqa-diamond)Winner (+35.4%)
78.8%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
Self HostLocal execution (zero API fees)Context WindowLarger
262.14k tokensBenchmarks & Scores
Coding (live-code-bench)
44%scripting single-file apps or clearly defined functions
Reasoning (gpqa-diamond)
43.4%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
Self HostLocal execution (zero API fees)Context Window
131.07k tokensFrequently Asked Questions about Gemma 4 12B vs Gemma 4 E2B
Gemma 4 12B is better for coding tasks on this benchmark. It scores 72% on live-code-bench (scripting single-file apps or clearly defined functions) compared to Gemma 4 E2B which scores 44%.
Related Matchups
Explore similar comparisons for Gemma 4 12B and Gemma 4 E2B.
Do you want to find a model for your constraints?
Use our interactive model finder to filter LLMs by reasoning capability, coding performance, cost, and context length.