This article was originally published on AI Study Room. For the full version with working code examples and related articles, visit the original post.

6 AI Coding Tools, 90 Days, 30 Tasks: My Honest Comparison

Introduction

Three months ago, I decided to run an experiment. Instead of picking one AI coding assistant and sticking with it (as most developers do), I would use all of them—switching between Claude Opus, GPT-4o, Gemini 2.5 Pro, DeepSeek V4, Cursor's agent mode, and GitHub Copilot —on real daily coding tasks and track which one actually performed best for each type of work.

I logged 30 distinct tasks across code generation, debugging, refactoring, code review, documentation, and architecture design. The results surprised me. The "best" AI tool depends heavily on the task, and the differences are large enough that having access to 2-3 models is genuinely worth the overhead.

Here's what I found.

Methodology

Each task was scored on three axes:

• Correctness (1-5): Does the output work on first try?
• Efficiency (1-5): How much time did it save vs doing it manually?
• Context handling (1-5): How well did it understand the broader codebase?

Tasks were drawn from real work: production bug fixes, feature development, test writing, and code review across a TypeScript/React/Node.js stack and Python data pipeline.

The Models

Claude Opus 4.7 — Best for Complex Reasoning (Avg: 4.7/5)

Claude won on refactoring, code review, and any task requiring deep understanding of cross-file dependencies. Its 200K context window meant I could paste entire files without losing coherence.

What it excels at:

• Large refactors across 5+ files
• Code review with specific, actionable feedback
• Understanding subtle bugs in complex logic
• Writing comprehensive test suites

Example — refactoring a monolithic React component:

I asked Claude to split a 900-line React component into smaller pieces. It analyzed the entire file, identified cohesive sub-components (DataTable, FilterBar, Pagination), generated their interfaces, and migrated the state logic in one shot. The result compiled on the first tsc run. No other model achieved this in a single pass.

Weakness: Slower than GPT-4o for quick, iterative coding tasks. Over-engineers simple solutions.

GPT-4o — Best for Speed and Iteration (Avg: 4.4/5)

GPT-4o is the tool I reach for when I need to write boilerplate, generate 5 function variants and pick the best one, or rapidly prototype. Its output quality is good enough for most tasks, and it's noticeably faster than Claude at generating code quickly.

What it excels at:

• Rapid prototyping and quick iterations
• Data processing scripts (Python, SQL)
• API integrations and boilerplate
• Generating multiple approaches to compare

Example — ETL pipeline in Python:

I needed to extract data from a PostgreSQL database, transform it with business logic, and load it into a reporting system. GPT-4o wrote a working pipeline with error handling, retry logic, and progress logging in about 8 minutes. Claude would have taken longer but produced a more architecturally clean version.

Weakness: Falls into "hallucination traps" more often than Claude—invented API methods that don't exist, especially with newer libraries.

Gemini 2.5 Pro — Best for Codebase-Wide Analysis (Avg: 4.3/5)

Gemini's 1M token context window is a genuine advantage for large codebase understanding. I fed it entire project directories and asked it to identify architectural issues, dead code, and improvement opportunities. The breadth of analysis was unmatched.

What it excels at:

• Large-scale codebase audit and analysis
• Dependency graph understanding
• Identifying dead code and architectural debt
• Cross-module refactoring planning

Weakness: Code generation quality lags behind Claude and GPT-4o. Often produces correct-but-verbose solutions. The latency is higher.

DeepSeek V4 — Best Free Option (Avg: 3.8/5)

DeepSeek V4 is shockingly good for a free model. It matches GPT-4o on many routine coding tasks, and it's completely free. The main limitations are occasional Chinese-influenced variable names and weaker performance on complex multi-file refactoring.

What it excels at:

• Everyday coding tasks at zero cost
• Code explanation and debugging
• Writing unit tests
• Generating code in niche languages

Weakness: Struggles with very large contexts (>50K tokens). Variable naming can be inconsistent. Multi-step reasoning is less reliable.

Cursor Agent Mode — Best IDE Integration (Avg: 4.5/5)

Cursor's agent mode is a fundamentally different experience from chat-based AI. It can read your project structure, search for relevant code, apply edits across multiple files, and run terminal commands—all from a single prompt.

What it excels at:

• End-to-end feature implementation
• Bug reproduction and fix in unfamiliar codebases
• Applying code review suggestions
• Refactoring with confidence (it sees the full project)

Weakness: The agent can make unexpected ch

---

Read the full article on AI Study Room for complete code examples, comparison tables, and related resources.

Found this useful? Check out more developer guides and tool comparisons on AI Study Room.