Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). DeepSeek Coder V2.5 delivers a 85% cost reduction over Groq LPU — Llama 3.3 70B.
| Traffic Volume Tier | DeepSeek Coder V2.5 Monthly | Groq LPU — Llama 3.3 70B Monthly | Monthly Savings by picking DeepSeek Coder V2.5 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $1.333 | $8.66 | Save $7.327 / mo |
| 10,000 reqs/mo (Small App) | $13.328 | $86.60 | Save $73.272 / mo |
| 100,000 reqs/mo (Growth Production) | $133.28 | $866.00 | Save $732.72 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $1,332.80 | $8,660.00 | Save $7,327.20 / mo |
DeepSeek Coder V2.5 is 85% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), DeepSeek Coder V2.5 costs $0.001333 per request compared to $0.00866 on Groq LPU — Llama 3.3 70B.
DeepSeek Coder V2.5 offers a context window of 128,000 tokens (max output: 8,192), while Groq LPU — Llama 3.3 70B offers 128,000 tokens (max output: 8,192).
At 100,000 requests per month, using DeepSeek Coder V2.5 saves $732.72 every month (or $8,792.64 annually) compared to Groq LPU — Llama 3.3 70B.
Output is the expensive side — prefer models with cheap output for autocomplete-style calls. Cache repository context between keystrokes; diffs change far less than the full file. Measure acceptance rate: paying for output users delete is pure waste.