Simulating realistic RAG / search-augmented answers parameters (8,000 in / 500 out with 50% cache reuse). Gemini 3.1 Pro delivers a 60% cost reduction over GPT-5.6 Sol.
Quick answer
For RAG / search-augmented answers, Gemini 3.1 Pro is the lower-cost option at $0.0148 per request versus $0.037 for GPT-5.6 Sol, a modeled saving of 60%.
Method & trust
The comparison uses 8,000 input tokens, 500 output tokens, and 50% 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) | $37.00 | $14.80 | Save $22.20 / mo |
| 10,000 reqs/mo (Small App) | $370.00 | $148.00 | Save $222.00 / mo |
| 100,000 reqs/mo (Growth Production) | $3,700.00 | $1,480.00 | Save $2,220.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $37,000.00 | $14,800.00 | Save $22,200.00 / mo |
Gemini 3.1 Pro is 60% cheaper for RAG / search-augmented answers workloads. At standard RAG / search-augmented answers parameter ratios (8,000 input tokens, 500 output tokens, 50% cache hit), Gemini 3.1 Pro costs $0.0148 per request compared to $0.037 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 $2,220.00 every month (or $26,640.00 annually) compared to GPT-5.6 Sol.
retrieved-context dominates cost — tune top-k and chunk size before switching models. Re-rank and drop marginal chunks; halving context roughly halves input cost. Deduplicate repeated chunks across queries to raise the cache hit rate.