AI Cost Analyzer
Interactive charts and filters for AI model cost comparison and analysis
Explore AI pricing through dynamic bar charts with advanced filtering options. Filter by provider, price range, and model type to visualize input (prompts) and output (generated content) per 1 million tokens costs across different AI services in an intuitive dashboard.
Prices are shown in USD per 1 million tokens. Token counts vary by model and language, but as a rough guide: 1 token ≈ 4 characters or ¾ of a word in English.
Display Price
Price Range ($/1M tokens)
Select Models
Input & Output Prices ($/1M tokens)
Disclaimers
- Prices may vary based on enterprise agreements and volume discounts.
- Prices are subject to change without notice. Always check the official pricing pages of providers.
- Context lengths and capabilities may vary for different use cases and implementations.
- This information is provided for reference only and should not be considered financial advice.
Understanding AI Pricing & Terminology
What are Tokens?
Tokens are the basic units of text that AI models process. They represent pieces of words, not entire words. For example, the word "unhappiness" might be broken into the tokens "un", "happiness".
Input Tokens
These are the tokens in your prompt or question to the AI. Input tokens include:
- Your instructions to the AI
- Context information you provide
- Examples you include
- System messages defining AI behavior
Output Tokens
These are the tokens in the AI‘s response. Output tokens usually cost more than input tokens because:
- They require more computational work
- The model must make predictions for each token
- They represent the AI‘s unique "work product"
How Many Tokens in Text?
As a general rule of thumb:
- 1 token ≈ 4 characters in English text
- 1 token ≈ ¾ of a word in English
- 100 tokens ≈ 75 words or ≈ 1 paragraph
- 1,000 tokens ≈ 750 words or ≈ 1 page
- 1M tokens ≈ 750,000 words or ≈ 1,500 pages
Interactive AI Cost Analysis
Our interactive cost analyzer transforms complex AI pricing data into intuitive visual charts. Unlike static tables, this tool provides dynamic filtering and real-time visualization to help you understand cost patterns and make data-driven decisions about AI model selection.
Visual Cost Comparison
- • Interactive bar charts showing price relationships
- • Color-coded providers for easy identification
- • Side-by-side input vs output cost comparison
- • Real-time chart updates based on your selections
Advanced Filtering
- • Filter by price type (input, output, or both)
- • Price range categories (low, medium, high)
- • Provider and model selection controls
- • Dynamic chart refreshing based on filters
Price Type Analysis
Compare different pricing dimensions to understand cost structures.
Price Ranges
Filter models by cost categories to match your budget.
Interactive Features
Dynamic controls for customized analysis.
How to Read the Charts
Chart Elements
- • Bar Height: Represents cost per 1M tokens
- • Bar Color: Indicates the AI provider
- • X-axis: Shows model names
- • Y-axis: Displays price in USD
- • Grouped Bars: Input vs Output comparison (when both selected)
Analysis Tips
- • Compare bar heights to identify cost differences
- • Look for patterns within provider colors
- • Use filters to focus on relevant price ranges
- • Consider input/output ratios for your use case
- • Check model count badge to see filtered results
Analysis Scenarios
Scenario 1Budget-Conscious Selection
Finding the most cost-effective models for high-volume applications.
Steps:
- Set price range to "Low (< $1.00)"
- Select all providers
- Compare input prices if you have long prompts
- Identify the lowest-cost options
What to Look For:
- • Shortest bars in the chart
- • Models clustered at the left side (lowest cost)
- • Balance between input and output costs
Scenario 2Provider Comparison
Comparing pricing strategies across different AI providers.
Steps:
- Select "Both" for price type
- Choose all price ranges
- Select default models from each provider
- Analyze color-coded provider patterns
What to Look For:
- • Color patterns showing provider pricing strategies
- • Input vs output price ratios by provider
- • Clustering of similar-priced models
Scenario 3Use Case Optimization
Optimizing model selection based on input/output patterns.
Steps:
- Identify your primary cost driver (input vs output)
- Filter by that price type
- Select relevant price range
- Compare models within your use case requirements
What to Look For:
- • Models optimized for your primary cost component
- • Best price/performance ratio for your needs
- • Context window vs cost trade-offs
Advanced Analysis Techniques
Cost Pattern Recognition
- • Price Clustering: Look for groups of similarly-priced models
- • Provider Strategies: Identify each provider‘s pricing approach
- • Outlier Detection: Spot unusually high or low-priced models
- • Sweet Spots: Find the best value models in each category
Comparative Analysis
- • Input/Output Ratios: Compare how providers price different token types
- • Tier Analysis: Examine low/medium/high cost model distribution
- • Feature vs Cost: Balance capabilities with pricing
- • Scaling Patterns: Understand how costs scale with model complexity
Why Use Interactive Charts vs. Static Tables?
✓ With Interactive Charts
- • Instant visual comparison of price relationships
- • Quick identification of cost patterns and outliers
- • Dynamic filtering to focus on relevant options
- • Color-coded provider identification
- • Real-time updates as you change selections
- • Intuitive understanding of price distributions
✗ With Static Tables
- • Must manually scan rows to compare prices
- • Difficult to spot patterns across providers
- • No way to dynamically filter or focus
- • Hard to visualize price relationships
- • Overwhelming when comparing many models
- • Time-consuming to identify best options
Optimization Workflow
Follow this systematic approach to find the optimal AI model for your needs:
Define Your Usage Pattern
Determine if you‘re input-heavy (long prompts), output-heavy (long responses), or balanced.
Set Budget Constraints
Use price range filters to eliminate models outside your budget.
Compare Relevant Models
Select providers and models that meet your technical requirements.
Analyze Cost Patterns
Use the charts to identify the most cost-effective options for your use case.
Make Informed Decision
Select the model that offers the best balance of cost, performance, and features.
Real-World Analysis Examples
Content Generation Agency Analysis
A marketing agency needs to choose AI models for client content creation
Requirements:
- • High output volume (blog posts, social media)
- • Budget-conscious approach
- • Quality balance for different content types
Analysis Steps:
- Set price type to 'Output' (content generation focus)
- Filter by 'Low' and 'Medium' price ranges
- Select all providers to compare options
- Identify models with good output pricing
Findings:
GPT-4o mini and Claude Haiku emerge as cost-effective options for high-volume content, while Claude Sonnet provides better quality for premium clients.
Chart Insights:
Output price comparison reveals 3x cost difference between budget and premium models
Document Analysis Platform
A legal tech company analyzing large document sets
Requirements:
- • High input token usage (long documents)
- • Accurate processing capability
- • Moderate output needs (summaries)
Analysis Steps:
- Set price type to 'Input' (document processing focus)
- Include all price ranges initially
- Select models with large context windows
- Compare input pricing efficiency
Findings:
Claude models with 200K context windows offer best value for large documents, despite higher per-token costs due to reduced API calls.
Chart Insights:
Input pricing analysis shows context window size significantly impacts total cost efficiency
Interactive Chatbot Development
E-commerce company building customer service chatbot
Requirements:
- • Balanced input/output usage
- • Fast response times
- • Scalable pricing for growth
Analysis Steps:
- Set price type to 'Both' (balanced usage)
- Focus on 'Low' to 'Medium' price ranges
- Compare default models from each provider
- Analyze input/output price ratios
Findings:
GPT-4o provides optimal balance of cost and quality, while Gemini Flash offers excellent value for high-volume scenarios.
Chart Insights:
Side-by-side comparison reveals different providers optimize for different usage patterns
Advanced Filtering Techniques
Price Range Stacking
Systematically analyze each price tier
Implementation Steps:
- Start with 'Low' range to identify budget options
- Move to 'Medium' range for balanced choices
- Check 'High' range for premium capabilities
- Compare value propositions across tiers
Provider Comparison Method
Compare strategies across AI providers
Implementation Steps:
- Select all models from one provider
- Analyze their pricing structure
- Repeat for each provider
- Compare provider strategies side-by-side
Use Case Filtering
Filter based on specific application needs
Implementation Steps:
- Identify your primary cost driver
- Filter by relevant price type
- Select models meeting technical requirements
- Optimize for your usage pattern
Chart Pattern Recognition Guide
Cost Clustering
Models grouped by similar pricing
What to Look For:
- • Horizontal clusters of similar-height bars
- • Clear separation between price tiers
- • Provider positioning within clusters
Insight: Reveals market pricing standards and competitive positioning
Provider Strategies
Different pricing approaches by provider
What to Look For:
- • Color-coded patterns across the chart
- • Provider concentration in specific price ranges
- • Input vs output pricing ratios by provider
Insight: Shows how each provider positions their models in the market
Value Outliers
Models with exceptional value propositions
What to Look For:
- • Unusually low bars with good capabilities
- • High-value models in competitive ranges
- • Gaps between similar models
Insight: Identifies hidden gems and pricing inefficiencies
Step-by-Step Analysis Walkthrough
Example: Finding the Best Value Chatbot Model
Initial Setup
Set price type to "Both" and price range to "All"
Chart shows: All models with input/output comparison
Budget Filtering
Change price range to "Low" to focus on budget options
Chart shows: ~15 models under $1.00 per 1M tokens
Provider Comparison
Select default models from each provider for fair comparison
Chart shows: 5-6 representative models color-coded by provider
Final Analysis
Compare input/output ratios and identify best value
Result: GPT-4o mini offers best balance for chatbot use case
Best Practices for Chart Analysis
Effective Strategies
- ✓ Start broad, then narrow down with filters
- ✓ Use color patterns to identify provider strategies
- ✓ Compare similar models across providers
- ✓ Consider your specific input/output ratio
- ✓ Look for value outliers and hidden gems
- ✓ Factor in context window requirements
Common Mistakes
- ✗ Focusing only on the lowest price
- ✗ Ignoring input vs output cost ratios
- ✗ Not considering your usage patterns
- ✗ Comparing models without similar capabilities
- ✗ Overlooking context window limitations
- ✗ Making decisions based on single data points