Llama 4 Scout vs Qwen3 235B A22B
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 235B A22B.
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 235B A22B
Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and...
Technical Specifications
| Specification | Llama 4 Scout | Qwen3 235B A22B |
|---|---|---|
| Provider | Meta | Alibaba |
| Context Window | 10,000,000 tokens | 131,072 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 | 2025-04-28 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Scout
$0.10
Qwen3 235B A22B
$0.46
Output Price per Million Tokens
Llama 4 Scout
$0.30
Qwen3 235B A22B
$1.82
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
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 235B A22B Quirks & Gotchas
No developer gotchas reported.