Two long-context flagships: $5/$30 versus $2/$12 with tiered billing above 200K.
gpt-5.6-sol · openai
gemini-3.1-pro · google
Benchmark
| Workload Scenario | GPT-5.6 Sol | Gemini 3.1 Pro | Price Delta |
|---|---|---|---|
| 1M input tokens (raw text) | $5.00 | $2.00 | +$3.00 |
| 1M output tokens (generation) | $30.00 | $12.00 | +$18.00 |
| 1M tokens · 70% input / 30% output mix | $12.50 | $5.00 | +$7.50 |
| Standard chat turn (4K in / 800 out, 50% cached) | $0.035 | $0.014 | +$0.021 |
| Monthly scale (10K requests / day) | $10,500.00 | $4,200.00 | +$6,300.00 |
Negative difference = GPT-5.6 Sol is cheaper. Positive = Gemini 3.1 Pro is cheaper.
Scaling Curve
Total cost of a token volume at 70/30 input/output split (uncached). Log-log scale.
Verdict
Gemini 3.1 Pro is cheaper on both input and output rates, so it costs less at every input/output mix. Price alone still isn't the whole decision: capability, latency and context limits (GPT-5.6 Sol: 1.1M, Gemini 3.1 Pro: 1M) may justify the premium for your task.
FAQ
On input, Gemini 3.1 Pro is cheaper ($2.00/M vs $5.00/M). On output, Gemini 3.1 Pro is cheaper ($12.00/M vs $30.00/M). The same model is cheaper on both sides, so it wins at every mix.
A chat-style request (4,000 input + 800 output tokens, 50% cached) costs $0.035 on GPT-5.6 Sol and $0.014 on Gemini 3.1 Pro — Gemini 3.1 Pro is 2.5× more expensive for that workload.
GPT-5.6 Sol supports 1,050,000 tokens (128K max output); Gemini 3.1 Pro supports 1,048,576 (65.5K max output). GPT-5.6 Sol fits 1.0× more context, which matters for long documents and agents.