Simulating realistic Customer support chatbot parameters (3,500 in / 350 out with 70% cache reuse). DeepSeek Coder V2.5 delivers a 97% cost reduction over Kimi K3 (Moonshot).
| Traffic Volume Tier | DeepSeek Coder V2.5 Monthly | Kimi K3 (Moonshot) Monthly | Monthly Savings by picking DeepSeek Coder V2.5 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $0.2793 | $9.135 | Save $8.856 / mo |
| 10,000 reqs/mo (Small App) | $2.793 | $91.35 | Save $88.557 / mo |
| 100,000 reqs/mo (Growth Production) | $27.93 | $913.50 | Save $885.57 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $279.30 | $9,135.00 | Save $8,855.70 / mo |
DeepSeek Coder V2.5 is 97% cheaper for Customer support chatbot workloads. At standard Customer support chatbot parameter ratios (3,500 input tokens, 350 output tokens, 70% cache hit), DeepSeek Coder V2.5 costs $0.000279 per request compared to $0.009135 on Kimi K3 (Moonshot).
DeepSeek Coder V2.5 offers a context window of 128,000 tokens (max output: 8,192), while Kimi K3 (Moonshot) offers 1,000,000 tokens (max output: 64,000).
At 100,000 requests per month, using DeepSeek Coder V2.5 saves $885.57 every month (or $10,626.84 annually) compared to Kimi K3 (Moonshot).
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.