Model Comparison
Llama 4 vs Qwen 3.6 - context length champion meets coding specialist
Meta's Llama 4 family offers the longest context window in open models (10M tokens) and strong multimodal capabilities. Alibaba's Qwen 3.6 family delivers exceptional agentic coding performance with SWE-Bench scores up to 78.8% and industry-leading dense model efficiency. Two families, very different strengths.
Performance
Head-to-head benchmark comparison
Llama 4 leads on context length and multimodal understanding, while Qwen 3.6 dominates agentic coding benchmarks and offers exceptional efficiency in its dense and small MoE variants.
Llama 4 and Qwen 3.6 represent different optimization targets. Llama 4 Scout's 10M context window is unmatched, and Maverick delivers strong all-around quality. Qwen 3.6's dense 27B model hits 77.2% on SWE-Bench Verified - remarkable for its size - while the Plus variant pushes to 78.8%. The 35B A3B MoE model activates just 3B parameters per token for edge deployment.
Qwen 3.6 27B: SWE-Bench Verified 77.2%, Terminal-Bench 59.3%, MMLU Pro 86.2%
Qwen 3.6 Plus: SWE-Bench Verified 78.8%, 1M context window
Maverick: MMLU Pro 80.5%, MMMU 73.4%, GPQA Diamond 69.8%
Scout: 10M token context - 78x longer than Qwen 3.6's 128K default
Qwen 3.6 35B A3B: only 3B active parameters for edge and mobile deployment
Full comparison
Llama 4 family vs Qwen 3.6 family
Complete benchmark results across reasoning, coding, multimodal, and architecture metrics for both model families.
| Benchmark | Llama 4 Maverick 400B / 17B active Open Weight | Llama 4 Scout 109B / 17B active Long Context | Qwen 3.6 27B 27B dense Coding | Qwen 3.6 Plus API model Flagship | Qwen 3.6 35B A3B 35B / 3B active Efficient |
|---|---|---|---|---|---|
MMLU Pro Knowledge & reasoning | 80.5% | 74.3% | 86.2% | - | - |
GPQA Diamond Scientific knowledge | 69.8% | 57.2% | - | - | - |
MMMU Multimodal understanding | 73.4% | 69.4% | - | - | - |
SWE-Bench Verified Agentic coding | - | - | 77.2% | 78.8% | 73.4% |
LiveCodeBench Live coding eval | 43.4% | 32.8% | - | - | ~75% |
Terminal-Bench Terminal tasks | - | - | 59.3% | - | - |
Context Window Max tokens | 1M | 10M | 128K | 1M | 128K |
Total Parameters Model size | 400B | 109B | 27B | - | 35B |
Active Parameters Per token | 17B | 17B | 27B (dense) | - | 3B |
Architecture Model type | MoE (128 experts) | MoE (16 experts) | Dense | API | MoE |
Data from Meta's official model card, Alibaba's technical reports, and independent evaluations.
Choose Llama 4
When to choose Llama 4 over Qwen 3.6
Llama 4 is the better choice when you need massive context windows, native multimodal understanding, or fully open-weight models with broad ecosystem support. Scout's 10M context is 78x longer than Qwen 3.6's default 128K.
- 10M token context (Scout) - process entire codebases in one call
- Native multimodal with early fusion architecture (text + image)
- Fully open-weight under Llama 3.1 compatible license
- MMMU 73.4% - strong multimodal understanding
- Broad ecosystem support across all major cloud providers
Choose Qwen 3.6
When Qwen 3.6 has the edge
Qwen 3.6 dominates agentic coding benchmarks and offers exceptional dense model efficiency. The 27B dense model hits 77.2% on SWE-Bench Verified, and the 35B A3B MoE variant activates just 3B parameters - ideal for edge deployment.
- SWE-Bench Verified up to 78.8% (Plus) - frontier coding performance
- 27B dense model: 77.2% SWE-Bench at a fraction of Maverick's size
- 35B A3B: only 3B active parameters for mobile and edge deployment
- MMLU Pro 86.2% (27B) - exceeds Maverick's 80.5%
- Terminal-Bench 59.3% - strong real-world terminal task performance
Llama 4 Family
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