Comparison page
GPT-5.4 mini vs Gemini 3.1 Pro Preview
Compare a lower-cost OpenAI model with Google's current Gemini Pro preview.
Side-by-side pricing
The table uses the same example request on both models so you can compare lower-cost production tasks and multimodal reasoning without changing the prompt shape.
Input 1,000 / Output 250
| Model | Input / 1M | Cached input / 1M | Output / 1M | Example request | Source |
|---|---|---|---|---|---|
GPT-5.4 mini Latest mini model in the GPT-5.4 family for coding and agentic workflows. | $0.750 | No public rate | $4.50 | $0.0019 | OpenAI pricing |
Gemini 3.1 Pro Preview Google's latest Gemini Pro preview for complex multimodal reasoning workloads. | $2.00 | No public rate | $12.0 | $0.0050 | Google Gemini pricing |
When to choose each model
GPT-5.4 mini
Choose GPT-5.4 mini when you want the economics or capability profile of OpenAI and you can justify the published rate.
Gemini 3.1 Pro Preview
Choose Gemini 3.1 Pro Preview when you need the second option in this comparison and want to test whether it lowers spend without hurting the workflow.
Use cases
- • High-volume support or workflow automation.
- • Teams choosing between price efficiency and multimodal reach.
- • Early-stage budget planning for a mixed prompt workload.
If the prompt is noisy or repetitive, run it through the prompt optimizer first.
If the brief is too loose, use the context engineer to tighten the instructions before you compare models again.
For the broader pricing strategy, read the LLM cost optimization pillar and the cost-per-million-tokens cheat sheet.
FAQ
Which model is cheaper?
GPT-5.4 mini is cheaper on the current rates and is often the first comparison point when teams want to lower spend.
When would Gemini win?
Gemini can still be the better choice if your workload is centered on the Google ecosystem or you specifically need its multimodal behavior.
Can I compare more than one prompt profile?
Yes. Use the calculator with multiple prompt samples, then treat this page as a quick reference for the model pair itself.