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Overview Studio Agents Models Notebooks

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Technology areas
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- Overview
- Studio
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- Notebooks
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- Engineering Blog
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Console

Overview
Beginner's guide
Get started
Get started with Agent Platform
Develop Gemini API code with the Gen AI SDK
Connect to the Knowledge MCP server
Get an API key
Configure application default credentials
Migrate from Google AI Studio to Agent Platform
Get started with Gemini 3
Google GenAI libraries
Generative AI cookbook
Access Gemini models using OpenAI libraries
Express mode
Select models
- Model Garden
- Overview of Model Garden
- Use models in Model Garden
- Test model capabilities
- Google Models
- All Google models
- Gemini
  - Migrate to the latest Gemini models
  - Pro
  - 3.1 Pro
  - 3 Pro Image
  - 2.5 Pro
  - Flash
  - Gemini Omni Flash
  - 3.7 Flash
  - 3.6 Flash
  - 3.5 Flash
  - 3.1 Flash Image
  - 3 Flash
  - 2.5 Flash
  - 2.5 Flash Image
  - 2.5 Flash Live API
  - Flash-Lite
  - 3.5 Flash-Lite
  - 3.1 Flash-Lite Image
  - 3.1 Flash-Lite
  - 2.5 Flash-Lite
  - Embedding
  - Gemini Embedding 2
  - Robotics
  - Gemini Robotics ER 2
    Overview
    Spatial reasoning
    Agentic capabilities
    Task orchestration
    Video understanding
- Veo
- Lyria
  - Lyria 2
  - Lyria 3
- Virtual Try-On
- Model versions
- Partner Models
- Partner models overview
- Claude
- Grok
  - Overview
  - Responses API
  - Function calling
  - Structured output
  - Reasoning
  - Model details
  - Grok 4.1 Fast
  - Grok 4.20
  - Grok 4.3
- Mistral AI
  - Overview
  - Model details
  - Mistral Medium 3
  - Mistral OCR (25.05)
  - Mistral Small 3.1 (25.03)
  - Codestral 2
- Deploy partner models from Model Garden
- Partner model deprecations
- Open Models
- Overview
- AlphaFold 3
- AlphaGenome
- DeepSeek
- Embedding (e5)
  - Multilingual E5 Small
  - Multilingual E5 Large
- Google Gemma
- Kimi
  - Overview
  - Kimi K2 Thinking
- Llama
  - Overview
  - Request predictions
  - Model details
  - Llama 4 Maverick
  - Llama 4 Scout
  - Llama 3.3
- MiniMax
  - Overview
  - MiniMax M2
- OpenAI
- Qwen
- ZAI.org
- Managed open models (MaaS)
- Self-deployed open models
Build
- Prompt design
- Introduction to prompting
- Prompting strategies
- Task-specific prompt guidance
  - Design multimodal prompts
  - Design chat prompts
- Capabilities
- Safety
- Text and code generation
- Image generation
- Video generation
- Music generation
- Media analysis
- URL context
- Thinking
- Live API
- Embeddings
  - Overview
  - Text embeddings
    Get text embeddings
    Choose an embeddings task type
  - Get multimodal embeddings
  - Get batch embeddings inferences
- Translation
- Generate speech from text
- Transcribe speech
- Model tools
- Code execution
- Computer use
- Function calling
- Grounding
- Development tools
- Use AI-powered prompt writing tools
  - Overview
  - Optimize prompts
    Overview
    Zero-shot optimizer
    Few-shot optimizer
    Data-driven optimizer
  - Use prompt templates
- Model tuning
- Introduction to tuning
- Tuning Gemini models
  - Supervised fine-tuning
    About supervised fine-tuning
    Prepare your data
    Use supervised fine-tuning
    
    Supported modalities
    Text tuning
    Document tuning
    Image tuning
    Audio tuning
    Video tuning
    Tune function calling
  - Reinforcement learning fine-tuning
    About reinforcement learning fine-tuning
    Quick start
    
    Reinforcement learning fine-tuning job
    Overview
    Tuning dataset
    Hyperparameters
    Reward functions
    Metrics and monitoring
    Continuous tuning
  - Preference tuning
    About preference tuning
    Prepare your data
    Use preference tuning
  - Use tuning checkpoints
  - Use continuous tuning
  - Tuning recommendations with LoRA and QLoRA
- Open models
  - Supervised and distillation fine-tuning
- Embeddings models
  - Tune text embeddings models
- Translation models
- Migrate
- Call Agent Platform models using OpenAI libraries
Evaluate
- Overview
- Tutorial: Perform evaluation using the console
- Perform evaluation using the GenAI Client in Agent Platform SDK
- Alternative evaluation methods