Llama 4 Scout vs Qwen3.5-35B-A3B
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Llama 4 Scout and Qwen3.5-35B-A3B.
Llama 4 Scout
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
Qwen3.5-35B-A3B
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...
Technical Specifications
| Specification | Llama 4 Scout | Qwen3.5-35B-A3B |
|---|---|---|
| Provider | Meta | Alibaba |
| Context Window | 10,000,000 tokens | 262,144 tokens |
| Agent Suitability | 82/100 | N/A |
| Time to First Token (TTFT) | 350 ms | N/A |
| Deployment Model | self hostable | self hostable |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-04-05 | 2026-02-25 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Scout
$0.10
Qwen3.5-35B-A3B
$0.14
Output Price per Million Tokens
Llama 4 Scout
$0.30
Qwen3.5-35B-A3B
$1.00
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling
Qwen3.5-35B-A3B Quirks & Gotchas
No developer gotchas reported.