G4

Comparison

Gemma 4 vs Qwen 3

A head-to-head comparison of Google's Gemma 4 and Alibaba's Qwen 3 — two powerful open-source model families with overlapping use cases but very different strengths. See which model wins on benchmarks, ease of deployment, and real-world tasks.

Benchmarks Multilingual Coding

Quick Summary

FeatureGemma 4Qwen 3
DeveloperGoogle DeepMindAlibaba Cloud (Qwen Team)
ReleaseMarch 2026April 2025 (Qwen 2.5) / 2026 (Qwen 3)
LicenseApache 2.0Apache 2.0 (most models)
ArchitectureDense + MoE, Hybrid AttentionDense + MoE (Qwen3-MoE)
MultimodalText + Image + AudioText + Image (Qwen-VL series)
Multilingual140+ languages29 languages (strong CJK)
Model SizesE2B, E4B, 31B, 26B A4B0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B
Thinking ModeBuilt-in (thinking tokens)Built-in (QwQ / thinking variant)
Context Window128K–256K128K (32B) / 1M (72B)

Benchmark Comparison

Comparable Size Models (~30B range)

BenchmarkGemma 4 31BGemma 4 26B A4BQwen 3 32BQwen 2.5 72B
MMLU Pro85.2%82.6%~83.0%85.0%
MATH (AIME)89.2%88.3%~85.0%~72.0%
GPQA Diamond84.3%82.3%~71.0%~59.0%
LiveCodeBench80.0%77.1%~68.0%~55.0%
HumanEval~92%~90%92.7%88.4%
Multilingual MMLU88.4%86.3%~79.0%82.3%

Gemma 4 leads on science reasoning (GPQA) and math. Qwen 3 32B is competitive on coding and general knowledge.

Small / Edge Models (under 8B)

BenchmarkGemma 4 E4BGemma 4 E2BQwen 3 7BQwen 3 3B
MMLU Pro69.4%60.0%~66.0%~54.0%
MATH42.5%37.5%~58.0%~45.0%
LiveCodeBench52.0%44.0%~50.0%~38.0%

At small sizes, Qwen 3 7B has an edge on math; Gemma 4 E4B leads on science and multilingual tasks.

Where Each Model Excels

Gemma 4 Strengths

  • Science reasoning: leads on GPQA Diamond across all sizes
  • Multimodal: audio support on edge models is unique
  • Breadth of languages: 140+ vs Qwen's 29
  • Edge deployment: E2B runs on 3 GB VRAM or CPU
  • Apache 2.0 purity: zero commercial restrictions anywhere
  • Ollama support: first-class, easy to set up

Qwen 3 Strengths

  • Chinese language: best-in-class for Chinese text tasks
  • Math at small sizes: Qwen 3 7B punches above Gemma E4B on math
  • Model variety: 0.5B to 72B+, fine-grained size selection
  • Long context: 1M token window available at 72B
  • Coding: strong HumanEval scores at all sizes
  • Wider community fine-tunes: large pool of Qwen-based derivatives

Chinese Language Performance

For Chinese-language applications, this is the most important factor:

TaskGemma 4Qwen 3Winner
Chinese MMLUGood (140-lang training)Excellent (native Chinese)Qwen 3
Chinese creative writingAdequateNative qualityQwen 3
Chinese code commentsGoodExcellentQwen 3
Chinese + English mixingVery goodExcellentQwen 3
Chinese + image analysisGoodQwen-VL seriesTie

For Chinese users: If your primary use case involves Chinese text, Qwen 3 is the better choice. Gemma 4 supports Chinese well but Qwen 3 was built with Chinese as a first-class language.

Deployment Comparison

Gemma 4

# Ollama (easiest)
ollama pull gemma4:e4b
ollama run gemma4:e4b

# Python
pip install transformers torch
# Load google/gemma-4-E4B-it

Qwen 3

# Ollama
ollama pull qwen3:8b
ollama run qwen3:8b

# Python
pip install transformers torch
# Load Qwen/Qwen3-8B-Instruct

Both models are equally easy to deploy with Ollama or Hugging Face Transformers. The main practical difference is VRAM: Gemma 4 E4B runs in 5 GB (4-bit) while the comparable Qwen 3 7B needs ~7 GB.

Verdict

Choose Gemma 4 for:

  • Maximum science/reasoning quality at any size
  • Audio + image multimodal tasks
  • Tight VRAM constraints (especially <6 GB)
  • Non-Chinese multilingual applications
  • Fully unrestricted Apache 2.0 commercial use

Choose Qwen 3 for:

  • Chinese-language applications
  • Finer-grained size selection (0.5B–72B)
  • Long-context tasks needing 1M+ tokens
  • Math-heavy tasks at <10B scale
  • Large established fine-tune community

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