Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). DeepSeek R1 (Reasoner) delivers a 71% cost reduction over Llama 3.1 405B Instruct.
| Traffic Volume Tier | DeepSeek R1 (Reasoner) Monthly | Llama 3.1 405B Instruct Monthly | Monthly Savings by picking DeepSeek R1 (Reasoner) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $8.028 | $28.00 | Save $19.972 / mo |
| 10,000 reqs/mo (Small App) | $80.28 | $280.00 | Save $199.72 / mo |
| 100,000 reqs/mo (Growth Production) | $802.80 | $2,800.00 | Save $1,997.20 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $8,028.00 | $28,000.00 | Save $19,972.00 / mo |
DeepSeek R1 (Reasoner) is 71% 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 R1 (Reasoner) costs $0.008028 per request compared to $0.028 on Llama 3.1 405B Instruct.
DeepSeek R1 (Reasoner) offers a context window of 64,000 tokens (max output: 8,000), while Llama 3.1 405B Instruct offers 128,000 tokens (max output: 4,096).
At 100,000 requests per month, using DeepSeek R1 (Reasoner) saves $1,997.20 every month (or $23,966.40 annually) compared to Llama 3.1 405B Instruct.
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.