6 min to read
Gemma 3 and Qwen 3 are the new generation of open-source large language models (LLMs) launched by Google and Alibaba. With significant improvements in context handling, reasoning, and deployment flexibility, both models are leading choices for developers and businesses in 2025.
Gemma 3 is Google’s latest open-weight LLM, designed as a distilled, efficient version of its Gemini models. It boasts strong multimodal capabilities (text + vision).
It handles extended context windows, supports 140+ languages, and fits even large variants like 27B parameters on a single GPU. Gemma 3 is best deployed in Google's ecosystem, but is also easy to run locally.
Key Features
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 shines with agent development capabilities and a permissive Apache 2.0 license, making it ideal for scaling and flexible integrations.
Key Features
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 |
Quantization | Yes | Yes |
Cloud Support | Google Cloud, Vertex AI, local | Flexible, open deployment |
License | Google Gemma | Apache 2.0 |
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 |
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) |
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.
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.
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.
Q1. Which model should I use for coding and math applications?
Q2. Can I run these models on my own hardware?
Q3. Which model supports vision tasks?
Q4. Which license is more flexible?
Q5. What about multilingual support?
pythonfrom transformers import AutoTokenizer,
AutoModelForCausalLMtokenizer = AutoTokenizer.from_pretrained("google/gemma-3-4b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-3-4b")
pythonfrom transformers import AutoTokenizer,
AutoModelForCausalLMtokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
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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