AI Model Comparison Guide

This guide provides a comprehensive comparison of AI models available in Cursor and Claude Code, helping you choose the right model for your specific development tasks.

Model Overview

Primary Development Models (2025)

• Claude Sonnet 4: Workhorse model - excellent balance of capability and cost
• Claude Opus 4: Premium model (5x cost) - complex architectural planning
• OpenAI o3: Specialized for debugging and intricate problem-solving
• Gemini 2.5 Pro: Best-in-class for long context scenarios
• OpenAI GPT-5: Excellent for one-shot app creation from detailed PRDs; highly steerable (Cursor blog)

Quick Selection Guide

By Task

Task Type	Recommended Model	Why
Routine coding	Claude Sonnet 4	Fast, accurate, cost-effective
Complex refactoring	Claude Opus 4	Deep reasoning capabilities
Bug hunting	OpenAI o3	Specialized problem-solving
Large codebase analysis	Gemini 2.5 Pro	1M+ token context
Quick completions	GPT-4.1	Low latency, good accuracy
PRD → one-shot app creation	GPT-5	Highly steerable; effective at end-to-end builds from detailed PRDs (source)

By Cost

Budget	Primary Model	Backup Model
Minimal	Claude Sonnet 4	Gemini 2.5 Flash
Moderate	Mix Sonnet 4 + o3	GPT-4.1
Generous	Claude Opus 4	o3 for specific tasks
Unlimited	Claude Opus 4	All models as needed

Detailed Model Specifications

Claude Models (Anthropic)

Model	Context Window	Max Mode	Strengths	Best For	Relative Cost
Claude Sonnet 4	128k default	200k	Fast, reliable, excellent code understanding	Daily development, refactoring, explanations	1x (baseline)
Claude Opus 4.1	128k	200k	Superior reasoning, complex problem solving	Architecture design, complex debugging	5x
Claude 3.7 Sonnet (Legacy)	128k	200k	Previous generation, still capable	Legacy support, cost savings	0.8x
Claude 3.5 Sonnet (Legacy)	128k	200k	Older but stable	Basic tasks	0.6x

Claude Sonnet 4 Deep Dive

Capabilities:

• Excellent at understanding large codebases
• Strong refactoring suggestions
• Accurate bug detection
• Natural conversation flow
• Maintains context well across long sessions

Limitations:

• Can be overly cautious with destructive operations
• Sometimes verbose in explanations
• May struggle with very recent frameworks

Optimal Use Cases:

// Example: Refactoring a complex function
// Sonnet 4 excels at understanding intent and suggesting improvements
async function processUserData(userData) {
  // Sonnet 4 would suggest:
  // - Add TypeScript types
  // - Implement proper error handling
  // - Extract validation logic
  // - Add comprehensive tests
}

Claude Opus 4.1 Deep Dive

Capabilities:

• Unmatched architectural understanding
• Can design entire systems from requirements
• Excellent at finding subtle bugs
• Superior code review capabilities
• Best at understanding complex business logic

When to Upgrade to Opus 4.1:

1. Designing new system architecture
2. Solving bugs that stumped Sonnet 4
3. Complex multi-file refactoring
4. Performance optimization requiring deep analysis
5. Security audit and vulnerability detection

OpenAI Models

Model	Context Window	Max Mode	Strengths	Best For	Relative Cost
GPT-5	Varies	Varies	Highly steerable; strong one-shot full-stack creation from detailed PRDs; great with background/parallel agents	One-shot app creation, cross-submodule features from PRD	Premium
o3	128k	200k	Deep reasoning, complex problem-solving	Difficult bugs, algorithmic challenges	4x
o4-mini	128k	200k	Lighter version of o3	Quick reasoning tasks	2x
GPT-4.1	128k	1M	Latest GPT, balanced performance	General coding, documentation	1.2x
GPT-4o	128k	128k	Optimized GPT-4	Quick responses, simple tasks	0.9x

OpenAI GPT-5 Overview

What it’s good at:

• One-shot, end-to-end feature or app creation when you provide a detailed PRD
• Very steerable behavior; responds well to explicit, structured instructions
• Handles complex bugs and can optimize tricky queries
• Works well with background agents and parallel foreground agents

Notes from Cursor’s engineering team:

• Being explicit improves outcomes; default style can be verbose, so set rules for concise output
• Demonstrated “one-shot” correctness across backend+frontend with protobuf regeneration

See the official announcement: GPT-5 is now available in Cursor.

OpenAI o3 Deep Dive

Unique Strengths:

• Excels at step-by-step reasoning
• Best for algorithmic problems
• Superior at finding edge cases
• Excellent debugging capabilities

Thinking Model Behavior:

# o3 approaches problems methodically
# Given: "Fix the race condition in this code"
# o3 will:
# 1. Identify all shared resources
# 2. Trace execution paths
# 3. Find timing dependencies
# 4. Propose multiple solutions
# 5. Evaluate trade-offs

Cost Optimization:

• Use for specific, complex problems only
• Switch to Sonnet 4 for implementation
• Reserve for bugs that resist other models

Google Models

Model	Context Window	Max Mode	Strengths	Best For	Relative Cost
Gemini 2.5 Pro	128k	1M	Massive context, good reasoning	Large codebase analysis	1.5x
Gemini 2.5 Flash	1M	1M	Fast, huge context	Quick searches, simple edits	0.3x

Gemini 2.5 Pro Deep Dive

Unique Advantages:

• 1 million token context window
• Can analyze entire medium-sized codebases
• Excellent cross-file understanding
• Good at maintaining consistency

Optimal Scenarios:

1. Analyzing monorepos
2. Understanding legacy codebases
3. Cross-service dependency mapping
4. Large-scale refactoring planning

Other Models

Model	Context Window	Max Mode	Strengths	Best For	Relative Cost
Grok 4 (xAI)	128k	256k	Fast, efficient	Quick tasks, experimentation	0.8x
Grok 3 Beta	128k	132k	Experimental features	Testing new capabilities	0.7x
Grok 3 Mini	128k	132k	Lightweight	Simple completions	0.4x

Model Selection Strategy

Thinking vs Non-Thinking Models

Thinking Models

Examples: o3, Claude Opus 4, Gemini 2.5 Pro

Characteristics:

• Take initiative in problem-solving
• Generate comprehensive solutions
• Consider multiple approaches
• Best for open-ended tasks

Use when:

• “Fix this architectural issue”
• “Optimize this system”
• “Find and fix all bugs”

Non-Thinking Models

Examples: Claude Sonnet 4, GPT-4.1

Characteristics:

• Wait for specific instructions
• Predictable behavior
• Easier to control
• Best for directed tasks

Use when:

• “Change variable name to X”
• “Add error handling here”
• “Write tests for this function”

Context Window Considerations

graph TD A[Task Size] --> B{< 50k tokens?} B -->|Yes| C[Any model works] B -->|No| D{< 200k tokens?} D -->|Yes| E[Use Max Mode] D -->|No| F{< 1M tokens?} F -->|Yes| G[Gemini 2.5 Pro/Flash] F -->|No| H[Split task or use specialized tools]

Cost-Performance Matrix

Use Case	Budget Option	Balanced Option	Premium Option
Daily Coding	Gemini Flash	Claude Sonnet 4	Claude Opus 4
Bug Fixing	Claude Sonnet 4	o4-mini	o3
Architecture	Gemini 2.5 Pro	Claude Sonnet 4 + o3	Claude Opus 4
Refactoring	GPT-4.1	Claude Sonnet 4	Claude Opus 4
Documentation	Gemini Flash	GPT-4.1	Claude Sonnet 4

Pricing Analysis

Token-Based Pricing (Approximate)

Model	Input (per 1M tokens)	Output (per 1M tokens)
Claude Sonnet 4	$3	$15
Claude Opus 4	$15	$75
o3	$12	$60
Gemini 2.5 Pro	$2	$10
GPT-4.1	$2.5	$10
Gemini Flash	$0.30	$1.20

Subscription Impact

Cursor Pricing

Pro ($20/month)

• ~225 Claude Sonnet 4 requests
• ~650 GPT-4.1 requests
• ~45 Claude Opus 4 requests

Ultra ($200/month)

• ~4,500 Claude Sonnet 4 requests
• ~13,000 GPT-4.1 requests
• ~900 Claude Opus 4 requests

Claude Code Pricing

Pro ($20/month)

• 10-40 prompts/5 hours with Sonnet 4
• Limited Opus 4 access

Max 5x ($100/month)

• 50-200 prompts/5 hours
• Full Opus 4 access

Max 20x ($200/month)

• 200-800 prompts/5 hours
• Unlimited practical usage

Advanced Selection Techniques

Model Routing Strategy

// Intelligent model selection based on task
function selectModel(task: CodingTask): AIModel {
  // Complex architectural decisions
  if (task.complexity > 8 || task.type === 'architecture') {
    return 'claude-opus-4';
  }

  // Debugging with multiple failures
  if (task.type === 'debug' && task.previousAttempts > 2) {
    return 'o3';
  }

  // Large codebase analysis
  if (task.contextSize > 200_000) {
    return 'gemini-2.5-pro';
  }

  // Default to cost-effective option
  return 'claude-sonnet-4';
}

Multi-Model Workflows

Example: Complex Feature Implementation

1. Planning Phase: Use Claude Opus 4 for architecture
2. Implementation: Claude Sonnet 4 for coding
3. Debugging: o3 for complex issues
4. Documentation: GPT-4.1 for clear explanations
5. Review: Claude Opus 4 for final security audit

Performance Benchmarks

Task	Claude Sonnet 4	Claude Opus 4	o3	Gemini 2.5 Pro
Code Generation	95%	98%	92%	90%
Bug Detection	88%	95%	97%	85%
Refactoring	92%	97%	90%	88%
Architecture	85%	98%	93%	87%
Speed (relative)	100%	70%	60%	85%

Model-Specific Tips

Claude Models

• Use clear, conversational prompts
• Provide context about coding standards
• Leverage their strong safety features
• Excellent for collaborative development

OpenAI Models

• More direct, task-focused prompts work well
• Good at following specific formats
• Strong at mathematical computations
• Best for algorithmic challenges

Gemini Models

• Maximize their context window advantage
• Use for cross-file operations
• Good for polyglot codebases
• Efficient for large-scale analysis

Future Model Considerations

Upcoming Developments

Expected in 2025:

• Claude 5 series with enhanced reasoning
• GPT-5 is now available in Cursor (see announcement)
• Specialized models for specific languages
• Local model options for privacy

Trends to Watch:

• Increasing context windows (2M+ tokens)
• Faster inference times
• Better multi-modal understanding
• Enhanced security features

Model Selection Checklist

1. Assess Task Complexity

   • Simple: Any model
   • Medium: Claude Sonnet 4 or GPT-4.1
   • Complex: Claude Opus 4 or o3

2. Consider Context Size

   • < 100k tokens: Standard models
   • 100k-200k: Use Max Mode

   • 200k: Gemini 2.5 Pro

3. Evaluate Budget

   • Calculate tokens needed
   • Compare subscription vs API costs
   • Consider long-term usage

4. Test and Iterate

   • Start with cost-effective models
   • Upgrade if needed
   • Track what works for your use cases

Best Practices

1. Start with Sonnet 4 - It handles 80% of tasks excellently
2. Upgrade strategically - Use premium models for specific challenges
3. Monitor usage - Track which models provide best ROI
4. Combine models - Use each model’s strengths
5. Stay updated - Model capabilities evolve rapidly