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The rapid evolution of large language models (LLMs) has brought forth a new generation of open-source AI models that are more powerful, efficient, and versatile than ever. Two of the most prominent contenders in 2024–2025 are Gemma 3 by Google and Qwen 3 by Alibaba.
Both models have attracted significant attention for their performance, features, and open availability, but they cater to slightly different needs and excel in distinct areas.
This comprehensive comparison explores every aspect of Gemma 3 and Qwen 3, including architecture, capabilities, benchmarks, multilingual support, efficiency, deployment, and practical considerations. By the end, you’ll have a clear understanding of which model is best suited for your specific requirements.
Gemma 3 is Google’s latest open-weight LLM, designed as a distilled, efficient version of its Gemini models. It’s tailored for broad usability, with a strong focus on multimodal capabilities, extended context handling, and multilingual support.
Qwen 3 is Alibaba’s flagship LLM series, notable for its mixture-of-experts (MoE) architecture and a unique “thinking mode” that enhances reasoning, math, and coding abilities. Qwen 3 is especially lauded for its agent capabilities and flexible deployment under the Apache 2.0 license.
Feature | Gemma 3 | Qwen 3 |
---|---|---|
Core Architecture | Decoder-only Transformer | Dense & Mixture-of-Experts (MoE) |
Parameter Sizes | 1B, 4B, 12B, 27B | 4B, 8B, 14B, 30B MoE, 32B, 235B MoE |
Vision Support | Yes (except 1B) | No |
Context Window | Up to 128K tokens (4B, 12B, 27B); 32K (1B) | Up to 128K tokens (varies by model) |
Multilingual | 140+ languages | 100+ languages |
License | Google Gemma license | Apache 2.0 |
Capability | Gemma 3 | Qwen 3 |
---|---|---|
Text | Yes | Yes |
Image Input | Yes (except 1B) | No |
Vision Tasks | Yes | No |
Video Understanding | Limited | No |
Gemma 3 leads in multimodal support, offering advanced vision features via its SigLIP encoder. Qwen 3 is currently limited to text-based tasks.
Task/Benchmark | Gemma 3 (12B/27B) | Qwen 3 (14B/30B/32B/235B) |
---|---|---|
Math (AIME’24/25) | 43.3–45.7 | 65.6–85.7 |
GSM8K (grade school) | 71 | 62.6 |
Code Generation | Competitive | Best-in-class |
General Reasoning | Strong | Slightly better |
Commonsense (HellaSwag) | Good | Best |
Multilingual Reasoning | Good | Best (on some tasks) |
Qwen 3 dominates in complex reasoning, math, and programming tasks, while Gemma 3 performs competitively in STEM and structured reasoning.
Feature | Gemma 3 | Qwen 3 |
---|---|---|
Single GPU Support | Yes (27B fits on 1 GPU) | Yes (MoE models are efficient) |
Quantization | Yes (all sizes) | Yes (all sizes) |
Cloud Support | Google Cloud, Vertex AI, local | Flexible, open deployment |
Mobile Support | 1B model optimized | Smaller models possible |
License | Google Gemma (restrictive) | Apache 2.0 (permissive) |
Gemma 3 is easy to run even on a single GPU and integrates seamlessly with Google’s ecosystem. Qwen 3 is better suited for those needing licensing freedom and large-scale deployment efficiency.
Qwen 3 excels in:
Gemma 3 shines in:
Feature | Gemma 3 | Qwen 3 |
---|---|---|
Architecture | Decoder-only Transformer | Dense & MoE Transformer |
Max Parameters | 27B | 235B (MoE) |
Vision Support | Yes (except 1B) | No |
Context Length | Up to 128K tokens | Up to 128K tokens |
Multilingual | 140+ languages | 100+ languages |
Math & Coding | Strong (step-based math) | Best-in-class |
Agent Capabilities | Yes | Best-in-class |
Function Calling | Yes | Yes |
License | Google Gemma | Apache 2.0 |
Efficiency | High (single GPU for 27B) | High (MoE for large models) |
Quantization | Yes | Yes |
Choose Gemma 3 if you need a reliable, well-rounded open-source LLM with strong support for vision, multilingual tasks, and general reasoning—particularly in STEM domains.
Choose Qwen 3 if you're building AI agents, doing advanced coding or math, or need the flexibility of open licensing with large-scale deployment options.
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