Hunyuan Pro vs Llama 4 Scout
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 Hunyuan Pro and Llama 4 Scout.
Hunyuan Pro
Hunyuan Pro (ๆททๅ Pro) is Tencent's flagship large language model developed by Tencent AI Lab, designed to power AI features across Tencent's vast ecosystem including WeChat, QQ, and enterprise cloud services. It achieves top-tier performance on Chinese-language benchmarks and strong multilingual capabilities, with native support for text, image, and video understanding. Hunyuan Pro is deployed at massive scale across Tencent's advertising, content recommendation, and customer service systems, processing billions of inference requests daily.
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...
Technical Specifications
| Specification | Hunyuan Pro | Llama 4 Scout |
|---|---|---|
| Provider | Tencent | Meta |
| Context Window | 256,000 tokens | 10,000,000 tokens |
| Agent Suitability | 85/100 | 82/100 |
| Time to First Token (TTFT) | 320 ms | 350 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2025-11-01 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Hunyuan Pro
$0.60
Llama 4 Scout
$0.10
Output Price per Million Tokens
Hunyuan Pro
$1.20
Llama 4 Scout
$0.30
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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.
Hunyuan Pro Quirks & Gotchas
- โธMassive daily inference volume โ battle-tested at WeChat scale
- โธAPI available through Tencent Cloud โ requires Chinese enterprise account
Llama 4 Scout Quirks & Gotchas
- โธ10M context causes significant VRAM pressure โ recommend 4-bit quantization
- โธPrimarily designed for RAG, not agentic tool calling