2025 AI Developer Tools Benchmark: Comprehensive IDE & Assistant Comparison

Overview

This comprehensive benchmark evaluates the capabilities of 10 leading AI-powered developer tools and IDEs. The focus is on their ability to autonomously complete real-world programming tasks through natural language conversations, minimizing the need for manual coding. The evaluation excludes common features like code explanation, completion, unit testing, and documentation generation to focus on advanced AI capabilities.

Testing Period: Late December 2024 to Mid-January 2025
Tools Tested: 10 major AI development assistants
Tasks Evaluated: 3 real-world programming scenarios
Note: Several tools were tested across multiple versions during this period.

Test Methodology

The evaluation focuses on real-world programming scenarios, measuring each tool's ability to act as an autonomous developer through natural language conversations. We employed two distinct testing approaches based on the tool's capabilities:

1. AI Agentic Approach

Applied to: Tools with full agentic capabilities (Cursor, Cline, Continue, Windsurf)

Key Characteristics:

• Complete codebase context awareness
• Autonomous exploration and decision-making
• Multi-tool utilization (file system, terminal, etc.)
• Minimal human intervention (only approving changes)
• Performance measured by autonomous work quality

2. Multi-Round Conversation Approach

Applied to: Tools without full agentic support

Key Characteristics:

• Task decomposition into manageable steps
• Iterative instruction and feedback cycles
• Active human developer guidance
• Focus on code generation and modifications
• Performance measured by code quality and iteration count

Evaluation Criteria

Each tool was evaluated across four key dimensions:

1. Task Completion Accuracy

   • Solution correctness
   • Requirement adherence
   • Code functionality

2. Code Quality & Maintainability

   • Code structure and organization
   • Documentation quality
   • Best practices adherence

3. Human Intervention Requirements

   • Number of guidance instances needed
   • Complexity of required interventions
   • Error resolution assistance

4. Efficiency Metrics

   • Time to completion
   • Resource utilization
   • Cost considerations

Test Cases

Test Environment Background

The web application used for testing is built with the following technologies:

1. Amplify Gen2: Latest version of AWS Amplify for frontend and backend development
2. Frontend: Next.js 14.x with MUI (Material-UI) and Amplify UI components
3. Backend: AWS AppSync for GraphQL API management and DynamoDB for data storage
4. Amplify AI: Integrated for GenAI capabilities and chat conversations

Test Environment

Application Stack

The benchmark was conducted using a modern web application built with the following technology stack:

Frontend Architecture

• Framework: Next.js 14.x
• UI Components:
  • Material-UI (MUI) for core components
  • Amplify UI for AWS service integrations
  • Custom themed components
• State Management: React Context and Hooks
• Styling: Tailwind CSS with custom theming

Backend Services

• API Layer: AWS AppSync (GraphQL)
• Database: Amazon DynamoDB
• Authentication: AWS Cognito
• AI Services: Amplify AI for GenAI features

Development Platform

• Infrastructure: AWS Amplify Gen2
• CI/CD: Amplify Hosting

This modern stack was chosen to evaluate the AI tools' capabilities across various technologies and integration points, providing a realistic enterprise development scenario.

Tools and IDEs Tested

AI-Powered IDEs

1. Cursor

   • Standalone AI-powered IDE built on VSCode
   • Uses Claude 3.5 Sonnet v2 as primary model
   • Built for AI-first development (https://cursor.sh)

2. Windsurf

   • Standalone AI-powered editor built on VSCode
   • Features Cascade for deep contextual awareness
   • Powered by advanced AI models
   • Includes inline commands, codelenses, and terminal integration
   • Available at https://codeium.com/windsurf

VSCode Extensions

1. Cline (Open Source)

   • Community-driven AI assistant with CLI and editor integration
   • VSCode extension with human-in-the-loop GUI
   • Features include file editing, terminal execution, browser automation
   • Supports multiple models via OpenRouter, Anthropic, OpenAI, Amazon Bedrock
   • Active open-source community
   • Available at https://github.com/cline/cline

2. Continue (Open Source)

   • Open-source VSCode extension for AI pair programming
   • Supports multiple AI models with flexible configuration
   • Community-driven development
   • Learn more at https://continue.dev

3. GitHub Copilot

   • Microsoft and GitHub's AI pair programmer
   • Multi-IDE integration
   • Available at https://github.com/features/copilot

4. Amazon Q Developer

   • AWS's AI coding assistant
   • VS Code and JetBrains IDE integration
   • Access via https://aws.amazon.com/q/developers

5. MarsCode

   • ByteDance's Douyin AI coding assistant
   • Features code completion, testing, explanation, and error fixing
   • Available as VSCode and JetBrains plugins
   • Focus on data security and privacy
   • Try at https://marscode.cn

6. Tongyi

   • Alibaba's AI coding assistant
   • Features real-time completion, multi-file editing, testing
   • IDE integration with VS Code, Visual Studio, JetBrains
   • Enterprise knowledge base integration
   • Access at https://lingma.aliyun.com/

7. Baidu Comate

   • AI coding assistant powered by ERNIE model
   • Features code completion, explanation, debugging
   • Multi-file editing and task decomposition support
   • R&D knowledge system integration
   • Available at https://comate.baidu.com/en

Task 1: Theme Management in Next.js

Task Detail: Add a new theme, set it as default, and refactor it as a shared variable. Additionally, the web application has two different theme implementations. Based on the prompt, generate a new UI theme, add it to the existing implementation, and make it configurable.
Difficulty: Easy
Explanation: For human developers, this task is relatively straightforward. It involves copying and pasting the existing theme code multiple times, then making necessary adjustments to the color and style properties. The process is simple and does not require advanced programming skills, as it mainly focuses on duplicating and modifying existing code snippets to create a new theme.

Task 2: Amplify AI Function Calling Integration

Task Detail: Implement function calling capabilities in Amplify AI conversations to fetch football scores and standings using the external api-football API. The application already has a chat dialog powered by Amplify AI conversations, but it is currently limited to model training knowledge and cannot provide refreshed game results or information. The task involves extending the existing chat functionality to fetch and display up-to-date football data through function calling.
Difficulty: Medium
Explanation: This task requires deep understanding of Amplify AI's conversation tools and function calling features. The main challenge lies in correctly implementing the tools specification and handling external API integration, as most LLMs lack comprehensive knowledge about Amplify AI's latest features.

Task 3: Next.js 15 and React 19 Migration

Task Detail: Upgrade the web application from Next.js 14.x to Next.js 15.x, which includes upgrading to React from 18.x to 19.x accordingly. There is an implicit requirement to upgrade other UI dependencies to support React 19.x, including Amplify UI React, MUI 5.x, and react-draggable.
Difficulty: Hard
Explanation: This task requires exploring the implicit dependencies between UI components and React 19. The AI developer needs to understand these dependencies through internet searches and codebase analysis. Another challenge is handling compatibility issues between Next.js 15, React 19, and existing code usage, updating routing patterns, and adapting to new Next.js or React APIs.

Note: For this challenging migration test case, we only evaluated tools/IDEs that demonstrated strong performance in the previous test cases. This focused approach allowed us to better assess the capabilities of the most promising solutions when handling complex migration tasks.

Key Takeaways

Model Performance Differences

• Claude 3.5 Sonnet v2 demonstrated consistent excellence
• Tool implementation significantly impacts model performance
• Amazon Q showed limited AWS service knowledge

AI Agentic Capabilities Matter

• Tools with agentic capabilities (Cursor, Cline) consistently outperformed traditional AI assistants
• The ability to autonomously explore codebases and make decisions significantly reduced human intervention
• Agentic tools showed better understanding of complex project structures and dependencies

Web Content Access Critical

• Tools with web access capabilities performed significantly better
• Access to up-to-date documentation, GitHub issues, and Stack Overflow was crucial for complex tasks

Cost Efficiency

• Monthly subscription-based tools (like Cursor Pro) proved more cost-effective for programming tasks, offering a low price rate for each request
• Pay-per-request tools (like Cline/Continue) showed suboptimal cost efficiency due to high API costs for programming tasks requiring multiple iterations
• Emerging models like Deepseek demonstrated potential for significantly reducing costs while maintaining reasonable performance, though reliability needs improvement

Open Source vs Commercial Tools

• Open-source tools (Cline, Continue) demonstrated several advantages:
  • Faster feature development and community-driven improvements
  • Greater flexibility in model selection and configuration
  • Transparent operation and customizable workflows
  • Cost advantages through flexible API provider choices
• Commercial products showed no absolute advantage even when using the same underlying models
• Open source solutions offered better cost control and feature customization
• Community contributions led to innovative features like MCP protocol support and expanded agentic capabilities

Additional Resources

LLM Rankings on OpenRouter

OpenRouter provides a unified API for accessing multiple LLM providers, similar to AWS Bedrock's InvokeModel API. The platform maintains comprehensive rankings of LLMs based on performance metrics and capabilities across different use cases.

According to OpenRouter's public statistics, weekly LLM request volume has shown exponential growth over the past year, increasing from approximately 14 billion to over 460 billion tokens per week.

Notable findings from the rankings:

• AI development tools dominate the top 3 LLM usage positions, including Cline, Roo Cline, Aide, and Aider
• DeepSeek v3, an open-source LLM from China, maintains a strong position in the top 6
• Usage patterns indicate growing adoption of AI-assisted development workflows

Roo Cline: Community-Driven Innovation

Roo Cline represents a significant community contribution to AI-assisted development. This open-source fork of Cline extends the original platform with:

• Chat Modes: choose between different prompts for Roo Cline to better suit your workflow
• Chat Mode Prompt Customization & Prompt Enhancements

With over 25,000 installations in the VS Code marketplace, Roo Cline has become a popular choice for developers seeking customizable AI assistance in their workflow. Its specialized chat modes have sparked significant interest in the developer community, with some seeing it as a revolutionary step in AI-assisted development.