Kimi K2.6 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 Kimi K2.6 and Llama 4 Scout.
Kimi K2.6
Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and...
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 | Kimi K2.6 | Llama 4 Scout |
|---|---|---|
| Provider | Moonshot AI | 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 | 2026-04-20 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Kimi K2.6
$0.66
Llama 4 Scout
$0.10
Output Price per Million Tokens
Kimi K2.6
$3.41
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.
Kimi K2.6 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