# Evaluate Model: Google Launches Gemini 3 with 2M Token Context and Multimodal Capabilities
## What This Is
Google has released Gemini 3, a new AI model that can understand text, images, audio, and video simultaneously. It boasts a very large memory (2-million-token context window) and is 30% more efficient and 15% cheaper than competitors, making it a powerful new option for developers.
Source: https://aidailyshot.com/blog/google-gemini-3-launch-2026-analysis
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and current AI integrations
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
- Which AI models I currently use and for what purposes
Then ask me before proceeding:
1. Am I interested in evaluating this model for my project, or just want a summary of what it offers?
2. If I want to try it — which part of my current AI stack should it replace or complement?
## Source Access Note
The source URL (https://aidailyshot.com/blog/google-gemini-3-launch-2026-analysis) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is a **New AI Model** — a model release, update, or capability announcement.
- Analyze the best use cases for this model within my project and current AI stack
- Compare its strengths, pricing, and context window against whatever I currently use
- Give me a clear, convincing argument for why this model would (or would not) be a good fit for my project
- If I want to try it: update my API configuration (provider, model ID, any new parameters) to point to this model
- If it requires a new API key or provider signup, tell me exactly what to do
## Additional Context
- Analyze the user's current project to identify opportunities where Gemini 3's 2-million-token context window and multimodal features would provide a significant advantage over their existing model.
- Benchmark the performance and cost of Gemini 3 against the user's currently configured AI provider for a representative task. Present a cost-benefit analysis based on the stated 15% lower pricing.
- If the user approves switching, update the project's AI API client configuration to use the new Google Gemini 3 model endpoint. Scan environment variables for an existing `GOOGLE_API_KEY` before prompting the user to add one.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it