Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). GPT-5.6 Luna delivers a 28% cost reduction over DeepSeek V4 Flash.
| Traffic Volume Tier | DeepSeek V4 Flash Monthly | GPT-5.6 Luna Monthly | Monthly Savings by picking GPT-5.6 Luna |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $4.853 | $3.504 | Save $1.349 / mo |
| 10,000 reqs/mo (Small App) | $48.528 | $35.04 | Save $13.488 / mo |
| 100,000 reqs/mo (Growth Production) | $485.28 | $350.40 | Save $134.88 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $4,852.80 | $3,504.00 | Save $1,348.80 / mo |
GPT-5.6 Luna is 28% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), GPT-5.6 Luna costs $0.003504 per request compared to $0.004853 on DeepSeek V4 Flash.
DeepSeek V4 Flash offers a context window of 1,000,000 tokens (max output: 128,000), while GPT-5.6 Luna offers 1,050,000 tokens (max output: 128,000).
At 100,000 requests per month, using GPT-5.6 Luna saves $134.88 every month (or $1,618.56 annually) compared to DeepSeek V4 Flash.
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.