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Available Models

Model: gemini-pro 
Capabilities:
- Fast responses
- Excellent reasoning
- Good for most use cases
- Strong cost/quality balance

Cost: ~$0.0005 per 1K tokens
Speed: < 2 seconds response time
Best For: General customer service, lead generation

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Gemini Pro Vision

Model: gemini-pro-vision 
Capabilities:
- All Gemini Pro features
- Plus image understanding
- Analyze documents and images
- Process receipts, IDs, etc.

Cost: ~$0.001 per 1K input tokens
Speed: 2-3 seconds
Best For: Document processing, verification tasks

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Setup

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Step 1: Get API Key

  1. Go to Google AI Studio
  2. Click “Create API Key”
  3. Select your GCP project
  4. Copy the API key
Example Format:
AIzaSyDXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

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Step 2: Enable Gemini API

In Google Cloud Console:
  1. Go to Google Cloud Console
  2. Search for “Generative Language API”
  3. Click “Enable”
  4. Wait for API to activate (usually instant)

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Step 3: Add to CallIntel

For Super Admins:
  1. Go to Settings → Developer Settings
  2. Click “API Keys”
  3. Select “Google Gemini” from provider list
  4. Paste API key
  5. Click “Test Connection”
  6. Save
For Organization Admins:
  1. Go to Settings → AI Models
  2. Click “Add Google Gemini”
  3. Paste API key
  4. Select which models to enable
  5. Save

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Step 4: Configure Agent

  1. Create or Edit an Agent
  2. Under “Language Model”, select:
    • gemini-pro or
    • gemini-pro-vision
  3. Configure settings:
    • Temperature: 0.7 (default)
    • Max Tokens: 200
  4. Save agent

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Step 5: Test

Make a test call:
  1. Use Web Call feature
  2. Speak with agent
  3. Verify responses
  4. Check logs

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Model Selection

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Gemini vs OpenAI

FeatureGeminiGPT-3.5
Quality⭐⭐⭐⭐⭐⭐⭐⭐⭐
Speed⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐
Cost⭐⭐⭐⭐⭐⭐⭐⭐⭐
Vision✓✗
Function Calling✓✓

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When to Use Gemini

Advantages:
  • Slightly cheaper than GPT-3.5
  • Faster responses
  • Native vision support
  • Great for document analysis
  • Excellent multilingual support
Best Use Cases:
  • High-volume customer service
  • Document verification
  • Multilingual agents
  • Vision-based tasks
  • Cost-sensitive operations

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Configuration

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Temperature

Controls response variability (0-2): 
Conservative: 0.3-0.5
Balanced: 0.7 (default)
Creative: 1.0-1.5

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Top P

Controls diversity (0-1): 
0.8 = More focused
0.95 = More diverse
Default: 0.95

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Top K

Controls output (1-40): 
1 = Most likely token
40 = More diversity
Default: 40

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Cost Comparison

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Monthly Costs (1000 calls)

OpenAI GPT-3.5:
1000 calls × 500 tokens × $0.0000005 = $0.25/month

Google Gemini:
1000 calls × 500 tokens × $0.0000005 = $0.25/month

Savings: Similar cost, but Gemini is slightly faster

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Gemini Vision Costs

Image Analysis:
Per call: ~$0.01 (with image)
Text-only: ~$0.0005

Best For:
- Document scanning
- Receipt processing
- Identity verification
- Limited use cases (cost matters)

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Advanced Features

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Image Analysis

Process images with Gemini Pro Vision: 
Supported Inputs:
- JPEG images
- PNG images
- GIF images
- PDF documents

Use Cases:
- Receipt analysis
- Document scanning
- Quality inspection
- ID verification
Cost Note: Image processing is more expensive per call.

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Function Calling

Enable agents to interact with external systems: 
{
  "name": "lookup_customer",
  "description": "Find customer information",
  "parameters": {
    "type": "object",
    "properties": {
      "customer_id": {
        "type": "string",
        "description": "The customer ID"
      }
    }
  }
}

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Streaming Responses

Get responses progressively: 
Traditional: Wait for entire response (slower)
Streaming: Get tokens as they're generated (faster perceived speed)
Better for: Real-time interactions

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Optimization

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1. Use Gemini for High Volume

High-volume calling (>1000/month):
→ Gemini Pro (same cost, faster)
→ Save response time costs

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2. Optimize Prompts

Keep system prompts concise: 
Bad: Long detailed instructions
Good: 1-2 sentence instructions
Savings: 40-50% fewer tokens

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3. Reduce Token Usage

Technique        | Impact
-----------------|-------
Shorter KB       | 30-50%
Concise prompts  | 20-40%
Lower max_tokens | 10-30%

Combined: 50% reduction possible

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Monitoring

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API Usage

Check Gemini API usage:
  1. Go to Google AI Studio
  2. View usage statistics
  3. Check spending rate
  4. Monitor quota limits

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Call Metrics

Track in CallIntel dashboard: 
- Average response time
- Success rate
- Token usage
- Cost per call
- Error rate

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Best Practices

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1. Start with Gemini Pro

Perfect for: Customer service, lead generation, general conversations
Cost-effective and fast

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2. Use Vision Sparingly

Gemini Pro Vision costs more
Use for: Document verification, quality checks
Pair with Gemini Pro for regular calls

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3. Monitor Response Quality

Weekly Review:
- Sample 10-20 calls
- Check response accuracy
- Verify tone and style
- Look for errors
- Adjust prompts if needed

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4. Test Before Deploying

1. Create test agent with Gemini
2. Make 20 test calls
3. Compare with other models
4. Measure quality and cost
5. Deploy if satisfied

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Troubleshooting

Verify key format is correct, API is enabled in Google Cloud Console, and key is for Gemini API.
Gemini is typically fast, but check network latency. Try reducing max_tokens.
Ensure image format is supported (JPEG, PNG, GIF). Check file size isn’t too large.
Check if using vision model. Vision requests cost 2-3x more. Switch to Gemini Pro for text-only.

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See Also

AI Models Overview

Compare all AI providers

OpenAI Models

Using OpenAI models

Agent Setup

Create and configure agents

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Support

Contact Support

Email: callintel01@gmail.com