Llama 4 Scout vs MoonshotAI Kimi Latest
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 MoonshotAI Kimi Latest.
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...
MoonshotAI Kimi Latest
This model always redirects to the latest model in the MoonshotAI Kimi family.
Technical Specifications
| Specification | Llama 4 Scout | MoonshotAI Kimi Latest |
|---|---|---|
| Provider | Meta | Moonshot AI |
| 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 | managed api |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-04-05 | 2026-04-27 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Scout
$0.10
MoonshotAI Kimi Latest
$0.66
Output Price per Million Tokens
Llama 4 Scout
$0.30
MoonshotAI Kimi Latest
$3.41
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
MoonshotAI Kimi Latest Quirks & Gotchas
No developer gotchas reported.