Doubao 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 Doubao Pro and Llama 4 Scout.
Doubao Pro
Doubao Pro (่ฑๅ Pro) is ByteDance's flagship large language model, powering the Doubao AI assistant used by over 100 million monthly active users in China. Built by ByteDance's Seed team, Doubao Pro achieves top-tier results on Chinese-language benchmarks, excelling at long-context understanding, multimodal reasoning, and creative writing. Its architecture is optimized for the massive scale of ByteDance's inference infrastructure, enabling cost-efficient deployment across Douyin, TikTok, and enterprise API customers via Volcano Engine.
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 | Doubao Pro | Llama 4 Scout |
|---|---|---|
| Provider | ByteDance | Meta |
| Context Window | 256,000 tokens | 10,000,000 tokens |
| Agent Suitability | 86/100 | 82/100 |
| Time to First Token (TTFT) | 300 ms | 350 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2026-01-15 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Doubao Pro
$0.80
Llama 4 Scout
$0.10
Output Price per Million Tokens
Doubao Pro
$1.60
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.
Doubao Pro Quirks & Gotchas
- โธMassive user base โ one of the most deployed Chinese models in production
- โธAPI available through Volcano Engine โ requires Chinese enterprise registration
Llama 4 Scout Quirks & Gotchas
- โธ10M context causes significant VRAM pressure โ recommend 4-bit quantization
- โธPrimarily designed for RAG, not agentic tool calling