Google DeepMind API: models, pricing and limits
A practical guide to google, using data from the AI & LLM Index. The goal is a shortlist and a decision, not a tour of every option.
The Google DeepMind lineup
Google DeepMind has 31 models in the index. Alphabet's AI research lab and maker of Gemini. The lineup spans 5 categories, from frontier to small open-weight.
The strongest model by average score is gemini-3-pro-max. The cheapest by input price is Gemma 3 1.5B.
Pricing
Across the lineup, the average input price is $12.46 per million tokens and the average output price is $57.05. That average hides a wide range: individual models run from $0.40 to $20.57 on input.
Use the average as a starting point, then check the specific model you plan to deploy. Output price and cached input pricing can change the ranking for long or repetitive workloads.
Strengths and limits
7 of the 31 models are open weights. That gives you a self-hosting path for part of the lineup, while the proprietary models remain API-only.
The best benchmark result in the lineup is 87.9 on the index average. Compare that with the best model from other providers before treating Google DeepMind as the default.
How to choose
- Start with the task, not the brand.
- Pick the cheapest model that clears your evaluation set.
- Keep a second provider as a fallback for outages and price changes.
- Check licence and data-handling terms for every model you shortlist.
Google DeepMind models at a glance
| Model | Input / 1M | Context | Avg score |
|---|---|---|---|
| Gemini 3 Pro Max | $20.57 | 2,000,000 | 87.9 |
| Gemini 3 Pro Reasoning | $19.69 | 200,000 | 87.5 |
| Gemini 3 Pro Pro | $19.80 | 200,000 | 87.3 |
| Gemini 3 Pro Turbo | $19.62 | 400,000 | 86.4 |
| Gemini 3 Pro | $18.24 | 2,000,000 | 86.1 |
| Gemini 3 Pro Vision | $17.59 | 200,000 | 85.5 |
| Gemini 3 Flash Max | $17.43 | 1,000,000 | 85.0 |
| Gemini 2.5 Pro Max | $16.94 | 200,000 | 84.8 |
| Gemini 3 Flash Pro | $17.71 | 400,000 | 84.8 |
| Gemini 3 Flash Turbo | $17.07 | 200,000 | 84.1 |
Related
FAQ
Which Google DeepMind model should I start with?
Start with gemini-3-pro-max for quality-sensitive work and Gemma 3 1.5B for high-volume tasks.
What is the most important factor in google?
Fit to the task beats raw benchmark rank. Define the failure modes, pick the benchmarks that resemble them, then test a small shortlist on your own examples.
How many models should I evaluate?
Three is usually enough: a quality candidate, a value candidate and a fallback. More candidates add work without changing the decision.
How do I control cost?
Compare blended 3:1 price, watch output tokens, use caching where available and route simple requests to a cheaper model.