Hy3 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 Hy3 and Llama 4 Scout.
Hy3
Hy3 is a 295B-parameter Mixture-of-Experts model from Tencent (21B active, 192 experts with top-8 routing) built for reasoning, agentic workflows, and real-world production use. It supports a configurable reasoning effort:...
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 | Hy3 | Llama 4 Scout |
|---|---|---|
| Provider | Tencent | Meta |
| Context Window | 262,144 tokens | 10,000,000 tokens |
| Agent Suitability | N/A | 82/100 |
| Time to First Token (TTFT) | N/A | 350 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2026-07-06 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Hy3
$0.20
Llama 4 Scout
$0.10
Output Price per Million Tokens
Hy3
$0.80
Llama 4 Scout
$0.30
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.
Hy3 Quirks & Gotchas
No developer gotchas reported.
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling