Google Gemini Models

Google's Gemini models provide powerful multimodal capabilities with advanced reasoning features. Tambo supports Gemini models across three generations: Gemini 3, Gemini 2.5, and Gemini 2.0.

Model Families

Gemini 3 Family

The latest generation of Gemini models with enhanced multimodal and reasoning capabilities.

gemini-3-pro-preview

Status: Tested

Google's most powerful model as of November 2025, best for multimodal understanding and agentic use cases.

• API Name: gemini-3-pro-preview
• Context Window: 1,048,576 tokens
• Provider Documentation: Gemini 3.0 Pro

Best For:

• Complex multimodal understanding tasks
• Agentic workflows requiring reasoning
• Advanced problem-solving scenarios

Notes:

• Expected to have improved performance over 2.5 models
• Supports reasoning via thinking configuration

Gemini 2.5 Family

Advanced reasoning models with extended thinking capabilities.

gemini-2.5-pro

Status: Known Issues

Gemini 2.5 Pro is Google's most advanced reasoning model, capable of solving complex problems.

• API Name: gemini-2.5-pro
• Context Window: 1,048,576 tokens
• Provider Documentation: Gemini 2.5 Pro

Best For:

• Complex reasoning tasks
• Multi-step problem-solving
• Tasks requiring deep analysis

Notes:

• May occasionally resist rendering as requested
• Behavior can be inconsistent with formatting
• Supports reasoning via thinking configuration

gemini-2.5-flash

Status: Known Issues

Gemini 2.5 Flash is Google's best model in terms of price and performance, offering well-rounded capabilities.

• API Name: gemini-2.5-flash
• Context Window: 1,048,576 tokens
• Provider Documentation: Gemini 2.5 Flash

Best For:

• Production workloads requiring speed
• Cost-effective reasoning tasks
• Balanced performance across various use cases

Notes:

• Fast and efficient for production use
• May have formatting quirks occasionally
• Supports reasoning via thinking configuration

Gemini 2.0 Family

Next-generation features designed for the agentic era with superior speed and built-in tool use.

gemini-2.0-flash

Status: Known Issues

Gemini 2.0 Flash delivers next-generation features and improved capabilities designed for the agentic era, including superior speed, built-in tool use, multimodal generation, and a 1M token context window.

• API Name: gemini-2.0-flash
• Context Window: 1,048,576 tokens
• Provider Documentation: Gemini 2.0 Flash

Best For:

• Agentic workflows with built-in tool use
• Applications requiring superior speed
• Multimodal generation tasks

Notes:

• Optimized for the agentic era
• May occasionally have inconsistent rendering behavior
• Strong tool-calling capabilities

gemini-2.0-flash-lite

Status: Known Issues

Gemini 2.0 Flash Lite is a model optimized for cost efficiency and low latency.

• API Name: gemini-2.0-flash-lite
• Context Window: 1,048,576 tokens
• Provider Documentation: Gemini 2.0 Flash Lite

Best For:

• High-volume applications
• Cost-sensitive deployments
• Low-latency requirements

Notes:

• Most cost-efficient Gemini model
• Optimized for speed over capability
• May have formatting quirks

Provider-Specific Parameters

Reasoning Configuration

Gemini models support reasoning capabilities through a thinking configuration. All Gemini models can use these parameters to control reasoning behavior.

thinkingConfig

Configure thinking behavior with a JSON object:

Example Configuration:

{
  "thinkingBudget": 5000,
  "includeThoughts": true
}

Recommended Settings:

• Quick reasoning (1000-2000 tokens): Simple tasks requiring minimal thinking
• Standard reasoning (3000-5000 tokens): Most use cases (recommended)
• Extended reasoning (7000+ tokens): Complex problems requiring deep analysis

Configuring in the Dashboard

1. Navigate to your project in the dashboard
2. Go to Settings → LLM Providers
3. Select Google Gemini as your provider
4. Choose your model
5. Under Custom LLM Parameters, click + thinkingConfig
6. Enter your configuration as a JSON object:

   {
     "thinkingBudget": 5000,
     "includeThoughts": true
   }

7. Click Save to apply the configuration

Best Practices

Model Selection

• Gemini 3.0 Pro Preview: Use for cutting-edge multimodal and agentic tasks (test thoroughly as untested)
• Gemini 2.5 Pro: Best for complex reasoning requiring extended thinking
• Gemini 2.5 Flash: Recommended for production workloads needing speed and cost efficiency
• Gemini 2.0 Flash: Choose for agentic workflows with strong tool-calling needs
• Gemini 2.0 Flash Lite: Select for high-volume, cost-sensitive applications

Performance Optimization

• Start with Gemini 2.5 Flash for balanced performance
• Use lower thinking budgets for simple tasks to reduce latency
• Monitor token usage when using reasoning features
• Test formatting requirements carefully due to known inconsistencies

Cost Considerations

• Gemini 2.0 Flash Lite offers the best cost efficiency
• Reasoning tokens (thinking budget) are billed separately
• Balance thinking budget with task complexity
• Consider caching for repeated queries with large context windows

Troubleshooting

Inconsistent rendering behavior?

• Try clarifying instructions more explicitly
• Use specific formatting directives (e.g., "Return a bulleted list only")
• Test with different prompt phrasings
• Consider using a tested OpenAI model for production-critical formatting

Reasoning not appearing in responses?

• Verify thinkingConfig is added in your dashboard settings
• Ensure includeThoughts is set to true
• Check that you've saved your configuration
• Try increasing the thinkingBudget value

Model performance issues?

• Lower the thinkingBudget for faster responses
• Use Gemini 2.0 Flash Lite for speed-critical applications
• Consider Gemini 2.5 Flash for balanced performance
• Monitor your context window usage

See Also

• Labels - Understanding model status labels and observed behaviors
• Custom LLM Parameters - Configure additional model parameters
• Reasoning Models - Comprehensive guide to reasoning capabilities