Google Gemini 3 Pro

Google Gemini 3 Pro

by [Google]

“The most intelligent model from Google” — state-of-the-art reasoning and multimodal understanding

See https://ai.google/

Quick summary

• Gemini 3 Pro is Google’s latest flagship large language model (announced Nov 18, 2025).
• Focus areas: advanced reasoning, multimodal understanding (text, images, UI/design assets), agentic coding and autonomous agents, and long-context workflows.
• Available via Gemini API (commercial pricing), free within Google AI Studio with rate limits, and enterprise availability on Vertex AI.

Features

• State-of-the-art reasoning across major benchmarks
• Strong multimodal understanding (images, UI/visual layouts; improved image generation reported in early tests)
• Agentic workflows: built for agents that can plan and execute multi-step tasks across apps
• Text-to-App / Generative UI capabilities: can produce working UI code and interactive interfaces from high-level prompts
• Integrated tooling: Gemini CLI, Google AI Studio, and new Google coding apps/IDE integrations
• Large context handling reported (community reports of 1M+ token windows on some checkpoints)

Superpowers

Gemini 3 Pro is designed for teams that need high-quality multimodal reasoning, rapid prototyping of UIs and apps, and reliable agentic behavior. Use cases where Gemini 3 Pro shines:

• Agentic automation that interacts with web apps and Google services
• One-shot complex coding and building prototypes from minimal prompts
• Multimodal analytics and extraction (documents, screenshots, UI snapshots)
• Creative production workflows needing consistent characters and scene composition

Pricing

• Public commercial pricing reported at: $2permillioninputtokensand$12 per million output tokens (for prompts ⇐ 200k tokens) — check Google for latest pricing tiers and volume discounts.
• Free access tier in Google AI Studio with rate limits; enterprise options through Vertex AI.

Caveats & unknowns

• Google has not published a full technical whitepaper (parameters, training data details, formal benchmark breakdowns) as of publication.
• Community evidence shows multiple checkpoints with variable behavior; pick the right checkpoint (e.g., X58/2HT in early reports) for the best results.
• Safety/mitigation details and detailed TOU for enterprise deployments should be reviewed before production use.

Practical tips

• Test multiple checkpoints and modes (Pro/Flash/Ultra where available) for your workload — early checkpoints vary in capability and safety tuning.
• Use the Gemini CLI and AI Studio integrations for rapid iteration when building agentic flows and UI/code generation tasks.
• Watch token usage for long-context projects; early reports suggest very large windows but pricing and limits may vary.

Further reading / sources

• Google’s AI announcements and Gemini API docs (watch for the official whitepaper and benchmark reports)
• Community tests and early benchmark reports (developer videos and AI researchers’ tests) — treat leaked/early tests as indicative but unverified.