Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Gemini 3.1 Pro delivers a 60% cost reduction over GPT-5.6 Sol.
| Traffic Volume Tier | GPT-5.6 Sol Monthly | Gemini 3.1 Pro Monthly | Monthly Savings by picking Gemini 3.1 Pro |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $87.60 | $35.04 | Save $52.56 / mo |
| 10,000 reqs/mo (Small App) | $876.00 | $350.40 | Save $525.60 / mo |
| 100,000 reqs/mo (Growth Production) | $8,760.00 | $3,504.00 | Save $5,256.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $87,600.00 | $35,040.00 | Save $52,560.00 / mo |
Gemini 3.1 Pro is 60% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), Gemini 3.1 Pro costs $0.035 per request compared to $0.0876 on GPT-5.6 Sol.
GPT-5.6 Sol offers a context window of 1,050,000 tokens (max output: 128,000), while Gemini 3.1 Pro offers 2,000,000 tokens (max output: 128,000).
At 100,000 requests per month, using Gemini 3.1 Pro saves $5,256.00 every month (or $63,072.00 annually) compared to GPT-5.6 Sol.
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.