Gemma 4 vs Qwen 3.6
Gemma 4 vs Qwen 3.6: two open model families, different strengths
Google's Gemma 4 and Alibaba's Qwen 3.6 are the two most capable open model families of 2026. Gemma leads on math reasoning (89.2% AIME) and multimodal. Qwen leads on agentic coding (73.4% SWE-Bench). Here's the full breakdown.
Quick verdict
When to choose each model
Both are excellent. The right choice depends on your primary use case.
Choose Gemma 4 when
Math reasoning, multimodal, edge deployment, or privacy
Gemma 4 excels at mathematical reasoning (89.2% AIME), multimodal understanding (76.9% MMMU Pro), and offers the widest deployment range from 2.3B edge models to 31B flagship. Apache 2.0 license provides maximum commercial freedom.
Best for: math tutoring, document analysis, on-device AI, multimodal applications, and deployments where Apache 2.0 licensing matters.
Choose Qwen 3.6 when
Agentic coding, SWE-Bench tasks, or 1M context
Qwen 3.6 dominates autonomous coding benchmarks with 73.4% on SWE-Bench Verified (vs Gemma's 52%). The 35B A3B MoE activates only 3B parameters per token. Qwen 3.6 Plus offers a 1M token context window.
Best for: AI coding agents, autonomous code editing, very long context tasks, and Chinese language applications.
Google DeepMind
Gemma 4 31B Dense
#3 on Arena AI. 89.2% AIME, 80% LiveCodeBench, 76.9% MMMU Pro. Dense architecture with 256K context.
30.7B parameters, all active. Best for maximum quality across reasoning, coding, and multimodal tasks.
Google DeepMind
Gemma 4 26B A4B MoE
Near-31B quality at 4B inference cost. 88.3% AIME, 77.1% LiveCodeBench. 256K context.
25.2B total, 3.8B active per token. 128 experts, 8 active + 1 shared.
Alibaba
Qwen 3.6 35B A3B MoE
73.4% SWE-Bench Verified. 35B total, 3B active per token. Strong agentic coding and tool use.
Dominates autonomous coding benchmarks. 51.5% Terminal-Bench 2.0 vs Gemma's 42.9%.
Alibaba
Qwen 3.6 Plus
1M token context window. Strong multilingual performance. Competitive reasoning benchmarks.
Extended context for very long documents and codebases. Strong Chinese language support.
Head to head
Where each model wins
A category-by-category breakdown of strengths and weaknesses.
Math reasoning: Gemma wins
Gemma 4 31B: 89.2% AIME 2026. Qwen 3.6 35B: ~81.5%. Gemma's thinking mode produces clearer reasoning chains for mathematical problems.
Agentic coding: Qwen wins
Qwen 3.6: 73.4% SWE-Bench Verified. Gemma 4: 52%. For autonomous code editing and debugging, Qwen has a significant lead.
Code generation: Close
Gemma 4: 80% LiveCodeBench. Qwen 3.6: ~75%. For code generation (not autonomous editing), Gemma has a slight edge.
Multimodal: Gemma wins
Gemma 4: 76.9% MMMU Pro. Qwen 3.6: ~70%. Gemma's vision encoder with variable resolution gives it an edge on visual tasks.
Context window: Qwen wins
Qwen 3.6 Plus: 1M tokens. Gemma 4: 256K. For very long documents, Qwen has a clear advantage.
Edge deployment: Gemma wins
Gemma 4 has E2B (2.3B) and E4B (4.5B) edge models with audio. Qwen 3.6 doesn't have comparable ultra-compact variants.
Architecture comparison
MoE efficiency: Qwen 3B active vs Gemma 4B active
Both families offer MoE models, but with different efficiency tradeoffs.
Gemma 4 26B A4B
- 25.2B total parameters, 3.8B active per token
- 128 experts, 8 active + 1 shared
- 256K context window
- Native multimodal (text + image)
- 14x throughput advantage on H100 (vs dense)
Qwen 3.6 35B A3B
- 35B total parameters, 3B active per token
- Lower active parameters = less compute per token
- Strong agentic coding (73.4% SWE-Bench)
- Better at autonomous code editing tasks
- Competitive reasoning and knowledge benchmarks
Benchmarks
Complete benchmark comparison
Head-to-head benchmark results across reasoning, coding, multimodal, and agentic tasks.
Both model families excel in different areas. Gemma leads on reasoning and multimodal, Qwen leads on agentic coding. The choice depends on your primary use case.
Math: Gemma 4 31B (89.2% AIME) vs Qwen 3.6 35B (~81.5%) - Gemma wins by 8 points
Agentic coding: Qwen 3.6 (73.4% SWE-Bench) vs Gemma 4 (52%) - Qwen wins by 21 points
Multimodal: Gemma 4 (76.9% MMMU Pro) vs Qwen 3.6 (~70%) - Gemma wins
Throughput: Both MoE models offer 14x+ throughput vs dense on H100
Head to head
Gemma 4 vs Qwen 3.6 on key benchmarks
Direct comparison across the most important evaluation benchmarks.
| Benchmark | Gemma 4 31B Dense 31B | Gemma 4 26B MoE 4B active 26B | Qwen 3.6 35B MoE 3B active 35B | Qwen 3.6 27B Dense 27B |
|---|---|---|---|---|
MMLU Pro Knowledge & reasoning | 85.2% | 82.6% | 83.1% | 81.0% |
AIME 2026 Mathematics | 89.2% | 88.3% | 81.5% | 78.0% |
LiveCodeBench v6 Code generation | 80.0% | 77.1% | 75.2% | 72.0% |
SWE-Bench Verified Agentic coding | 52.0% | - | 73.4% | - |
Terminal-Bench 2.0 Terminal tasks | 42.9% | - | 51.5% | - |
MMMU Pro Multimodal | 76.9% | 73.8% | 70.2% | 67.0% |
Context Window Max tokens | 256K | 256K | 128K | 128K |
Active params Per token | 30.7B | 3.8B | 3B | 27B |
License Commercial use | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |
Data from official model cards and independent evaluations. Scores may vary by evaluation methodology.
Coding
The coding showdown: generation vs autonomous editing
Gemma 4 and Qwen 3.6 split the coding benchmarks. Gemma leads on code generation (LiveCodeBench), while Qwen dominates autonomous code editing (SWE-Bench). The distinction matters for your use case.
- Code generation: Gemma 4 80% vs Qwen 3.6 75% (LiveCodeBench v6)
- Autonomous editing: Qwen 3.6 73.4% vs Gemma 4 52% (SWE-Bench)
- For AI coding agents, Qwen 3.6 is currently the better choice
Reasoning
Math and science: Gemma 4 has a clear lead
Gemma 4's thinking mode produces exceptional results on mathematical reasoning. 89.2% on AIME 2026 vs Qwen's ~81.5% is a significant gap. For math tutoring and scientific reasoning, Gemma 4 is the stronger choice.
- AIME 2026: Gemma 4 89.2% vs Qwen 3.6 ~81.5%
- GPQA Diamond: Gemma 4 84.3% vs Qwen 3.6 ~80%
- Gemma's thinking mode shows clearer reasoning chains
Deployment
Edge to cloud: Gemma 4 covers more ground
Gemma 4 offers four model sizes from 2.3B to 31B, including edge models with native audio. Qwen 3.6 focuses on the server tier. If you need on-device AI or browser deployment, Gemma 4 is the only option.
- Gemma 4: E2B (2.3B), E4B (4.5B), 26B MoE, 31B Dense
- Qwen 3.6: 27B Dense, 35B MoE (server-focused)
- Only Gemma 4 has edge models with native audio support
Try both
Test the models yourself
The best comparison is hands-on experience.
Gemma 4 resources
Get started with Gemma 4
Everything you need to start building with Gemma 4.
Qwen 3.6 resources
Learn more about Qwen 3.6
Official Qwen 3.6 resources and documentation.
Open model landscape
The best open models of 2026
Gemma 4 and Qwen 3.6 lead the open model landscape, but they're not the only options.
Try Gemma 4
Experience Gemma 4's strengths firsthand
Try Gemma 4 for free and see how it performs on your specific tasks. Math reasoning, multimodal understanding, and edge deployment are where it shines brightest.