Gemini 3 Pro by Google — 1.0M Context
The best model in the world for multimodal understanding, and our most powerful agentic and vibe-coding model yet, delivering richer visuals and deeper interactivity, all built on a foundation of state-of-the-art reasoning.
At a glance
Modalities
Context window
1,048,576
Pricing
input / output per 1M
Reasoning
Capabilities
Streaming
Real-time token-by-token response streaming
Function calling
Connect the model to external tools and systems
Structured outputs
Return responses in JSON schema format
Reasoning
Extended thinking before responding
Caching
Cache responses to reduce latency and costs
Batch API
Process multiple requests in batch
Web search
Search the internet for real-time information
File search
Search and retrieve from uploaded files
Code execution
Execute code in a sandboxed environment
Details
| Release date | 2025-11-01 |
| Knowledge cutoff | 2025-01-01 |
| Model ID | gemini-3-pro |
| Provider |
What You Need to Know About Gemini 3 Pro
Complete Overview of Gemini 3 Pro by Google
Get detailed information about Gemini 3 Pro, including its context window of 1048576 tokens, pricing per million tokens, supported input and output modalities, and benchmark scores. This model from Google offers specific capabilities for natural language processing, code generation, and complex reasoning tasks that set it apart from alternatives.
Pricing and Cost Analysis for Gemini 3 Pro
Compare input and output token pricing for Gemini 3 Pro against other models in its class. Understanding LLM pricing is essential for budgeting your AI applications at scale. We break down the cost per million tokens for both input and output so you can estimate the total cost of your workloads and compare value across providers.
Benchmarks and Performance Metrics for Gemini 3 Pro
Review benchmark performance data for Gemini 3 Pro across key evaluation metrics. Compare its reasoning, coding, and language understanding capabilities against competing models to determine if it is the right fit for your specific requirements, whether that involves complex analysis, creative generation, or efficient inference at scale.