Simulating realistic Customer support chatbot parameters (3,500 in / 350 out with 70% cache reuse). Gemini 3.1 Pro delivers a 60% cost reduction over GPT-5.6 Sol.
Quick answer
For Customer support chatbot, Gemini 3.1 Pro is the lower-cost option at $0.00679 per request versus $0.017 for GPT-5.6 Sol, a modeled saving of 60%.
Method & trust
The comparison uses 3,500 input tokens, 350 output tokens, and 70% cache reuse for the selected workload. Pricing is applied per model, then scaled to monthly request volumes.
| 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) | $16.975 | $6.79 | Save $10.185 / mo |
| 10,000 reqs/mo (Small App) | $169.75 | $67.90 | Save $101.85 / mo |
| 100,000 reqs/mo (Growth Production) | $1,697.50 | $679.00 | Save $1,018.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $16,975.00 | $6,790.00 | Save $10,185.00 / mo |
Gemini 3.1 Pro is 60% cheaper for Customer support chatbot workloads. At standard Customer support chatbot parameter ratios (3,500 input tokens, 350 output tokens, 70% cache hit), Gemini 3.1 Pro costs $0.00679 per request compared to $0.017 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 $1,018.50 every month (or $12,222.00 annually) compared to GPT-5.6 Sol.
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.