Simulating realistic Customer support chatbot parameters (3,500 in / 350 out with 70% cache reuse). DeepSeek Coder V2.5 delivers a 76% cost reduction over Mistral Large 3.
| Traffic Volume Tier | DeepSeek Coder V2.5 Monthly | Mistral Large 3 Monthly | Monthly Savings by picking DeepSeek Coder V2.5 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $0.2793 | $1.172 | Save $0.8932 / mo |
| 10,000 reqs/mo (Small App) | $2.793 | $11.725 | Save $8.932 / mo |
| 100,000 reqs/mo (Growth Production) | $27.93 | $117.25 | Save $89.32 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $279.30 | $1,172.50 | Save $893.20 / mo |
DeepSeek Coder V2.5 is 76% 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 $0.001173 on Mistral Large 3.
DeepSeek Coder V2.5 offers a context window of 128,000 tokens (max output: 8,192), while Mistral Large 3 offers 1,000,000 tokens (max output: 64,000).
At 100,000 requests per month, using DeepSeek Coder V2.5 saves $89.32 every month (or $1,071.84 annually) compared to Mistral Large 3.
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.