Simulating realistic Customer support chatbot parameters (3,500 in / 350 out with 70% cache reuse). Llama 4 Scout (109B MoE) delivers a 71% cost reduction over Kimi K2.7 Code (OpenRouter).
| Traffic Volume Tier | Llama 4 Scout (109B MoE) Monthly | Kimi K2.7 Code (OpenRouter) Monthly | Monthly Savings by picking Llama 4 Scout (109B MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $0.6825 | $2.359 | Save $1.677 / mo |
| 10,000 reqs/mo (Small App) | $6.825 | $23.59 | Save $16.765 / mo |
| 100,000 reqs/mo (Growth Production) | $68.25 | $235.90 | Save $167.65 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $682.50 | $2,359.00 | Save $1,676.50 / mo |
Llama 4 Scout (109B MoE) is 71% cheaper for Customer support chatbot workloads. At standard Customer support chatbot parameter ratios (3,500 input tokens, 350 output tokens, 70% cache hit), Llama 4 Scout (109B MoE) costs $0.000683 per request compared to $0.002359 on Kimi K2.7 Code (OpenRouter).
Llama 4 Scout (109B MoE) offers a context window of 10,000,000 tokens (max output: 16,384), while Kimi K2.7 Code (OpenRouter) offers 262,144 tokens (max output: 235,929).
At 100,000 requests per month, using Llama 4 Scout (109B MoE) saves $167.65 every month (or $2,011.80 annually) compared to Kimi K2.7 Code (OpenRouter).
Cache the system prompt and static documentation chunks — cached input is often 4–10× cheaper. Route simple FAQ turns to a nano-tier model and escalate only complex tickets. Cap max_output per reply; support answers rarely need more than a few hundred tokens.