Claude Code vs Aider
Two command line coding assistants: which one is better?
I use Aider extensively to do AI coding on the command line Anthropic recently released Claude Code (CC), and I wanted to check it out. They’re both command-line AI coding assistants, but they have pretty different design philosophies. This post is a review of both, and commentary on their differences.
Aider and Claude Code are very similar tools — they’re both on the command line, and they both allow me to get Claude 3.7’s help with my code base. But Claude Code is a newer project, and its design reflects a shift toward the agentic that we’ve seen recently in AI tools. Claude Code behaves much more like a reasoning model, while Aider behaves much more like a single-query model helper utility.
If you write code for a living, and you use the command line, then this post is for you. If you don’t like using the command line, then this isn’t for you, but you should download Cursor if you haven’t already.
TL;DR The main differences:
Aider responds faster, and streams its thoughts in real time. Claude Code behaves more like a reasoning model, with multiple steps of internal thinking. Winner: Aider.
Claude Code autonomously searches the code base with grep and decides for itself which files to focus on. Aider maintains a sophisticated repo map, but involves the user in decisions about what files to focus on. Winner: Claude Code.
Aider is transparent about what the LLM is thinking and editing. Claude Code only surfaces content to the user when it has a question, an answer, or wants permission. Winner: Aider.
And of course, Aider can work with many different LLMs, while CC is just for Claude. I mostly use Claude on Aider anyway, but still, Winner: Aider.
Edit, April 9, ‘25: Having spent more time with Claude Code, I now believe that it does a better job with complex requests, especially in larger codebases. I’m now more likely to use claude for serious requests, although I often fire up aider for smaller tasks, because I appreciate its transparency.
Aider — Intro
Basic facts:
Open Source: Yes
LLMs: Supports multiple models (Claude, GPT-4o, DeepSeek, local models via Ollama)
Key Features:
Works with local Git repositories for seamless code editing and commits
Repository mapping for context-aware suggestions across large codebases
Multi-file editing and coordinated changes across files
Browser-based experimental UI and voice coding support
Flexible integration with most popular programming languages
Target Users: Developers who value flexibility, open-source tools, and multi-model support
Strengths:
Highly customizable and extensible due to open-source nature
Broad LLM compatibility for diverse use cases
Frequent automatic commits as a safety net during edits
Limitations: Requires manual file specification for context; lacks advanced Git automation features like PR creation
Aider addresses a basic need that programmers have in the days of LLMs. We want to do this:
Copy and paste our code into the text box for ChatGPT or Claude or what-have-you
Ask it for help with some stupid bug, or implementing a feature
Copy the solution back into our IDE, and check if it works
Aider pretty much just automates that process, and it’s amazing. I love aider.
Here’s the basic flow that Aider does:
It looks through your code to create a repomap, which looks like this:
aider/commands.py:
⋮...
│class Commands:
│ voice = None
│
⋮...
│ def get_commands(self):
⋮...
│ def get_command_completions(self, cmd_name, partial):
⋮...
│ def run(self, inp):
⋮...
It takes your query, like “add pretty printing to get_commands”, and composes a prompt that basically says: “Here’s my code: {repomap}. Please do {request}. Format your response like this…”
Then it shoves a ton of advice about formatting into the prompt in order to make sure the LLM writes its response in a coherent way
It parses that response and edits the code. The LLM is encouraged to use a Search+Replace edit format for this:
mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
It checks the code with a linter, to double check that the edit applied correctly, and makes the LLM fix it if it’s bad
So, that’s pretty much it. Aider can edit multiple files at once, and it has orchestration to enable “infinite output”, which enables it to make large edits. When you combine this with linting and automated testing, it’s able to handle pretty large requests.
It also makes it easy to copy and paste errors from your program directly into Aider, and have Aider fix them.
Claude Code — Intro
Basic Facts:
Open Source: No (Proprietary, developed by Anthropic)
LLMs: Optimized for Claude 3.7 Sonnet
Key Features:
Comprehensive Git integration (commits, PRs, rebasing, conflict resolution)
Project-wide awareness without manual file additions
Extended thinking mode for handling complex problems
Non-interactive mode for automation in CI/CD pipelines
Natural language commands for debugging, refactoring, and testing
Target Users: Developers seeking seamless integration with Claude models and advanced Git workflows
Strengths:
Deep Git automation
Security-first architecture with direct API connections
Adaptive responses for complex tasks using extended thinking
Limitations: Proprietary, inconsistent performance in some scenarios, limited to Claude models
Claude Code (CC) is pretty similar to Aider in its purpose. You give it a request, and it edits the code to make the change.
It’s closed-source, but a user de-obfuscated its code using Claude. The story of how they did it is pretty cool, and the code is available here. One amazing use case of these AI coding tools is that you can load up any repository and ask the LLM to tell you about the source code. Reading someone else’s code has never been easier!
Here’s what it’s like to use Claude:
$ git clone <https://github.com/ghuntley/claude-code-source-code-deobfuscation.git>
$ cd claude-code-source-code-deobfuscation
claude
╭──────────────────────────────────────────────────────────╮
│ ✻ Welcome to Claude Code research preview! │
│ /help for help │
│ cwd: /home/keenan/Dev/Understanding/claude-code-source-code-deobfuscation
╰───────────────────────────────────────────────────────────╯
> Look in this code (It's your source code!) Explain to me how the project allows Claude to have a view of the
entire source code. How does it manage the source even when it's way to big to all fit in context? Does it use
a repo map like aider does? You may want to examine your own experiences to do some introspective
self-reporting about this.
● I'll examine my own source code to understand how I handle viewing large codebases.
● Task(Find files related to codebase analysis, source code understanding, or repo mapping. Look for files that …
might handle large codebases or create representations of the codebase structure. Examine files in the
codebase/ directory first, and then look for related functionality in other directories. Return a concise
summary of the relevant files and their purposes.)
⎿ Done (4 tool uses · 26.5k tokens · 20.4s)
● Read(file_path: "claude-code/src/codebase/analyzer.ts")…
⎿ /**
* Codebase Analyzer
*
... (+639 lines)
● Task(Find all files that might be related to how Claude Code interacts with AI or manages large codebases. Loo…
for information on how it might process, chunk, or summarize source code to fit into context windows.
Specifically look for any approaches like repo mapping, file indexing, embedding, vectorization, or
chunking strategies. Check in the ai/ directory first, but also look for relevant code in other
directories.)
⎿ Done (13 tool uses · 39.6k tokens · 56.2s)
● Read(file_path: "claude-code/src/ai/client.ts")…
⎿ /**
* AI Client
*
... (+429 lines)
● Read(file_path: "claude-code/src/ai/prompts.ts")…
⎿ /**
* AI Prompts
*
... (+328 lines)
❄ Computing… (52s · esc to interrupt)
One thing immediately stands out as different! Claude Code has a task-centric model of development, where the LLM takes multiple actions in sequence to read the code, make decisions, and then to edit the code.
The biggest difference this makes is that you don’t need to manually update which files are in context for it, it will autonomously look through the codebase and decide which files to read. It provides an affordance to the LLM to perform searches over the codebase, which allows it to figure out what it needs to look at for each query you give it. Honestly, this is a huge; it’s an enormous improvement over the way Aider works.
In order to understand the codebase better, I had source Claude Code look at its own source code.
Here’s a list of the commands that CC has access to:
File System Tools:
View - Reads file contents
Edit - Modifies files
Replace - Overwrites files
LS - Lists directory contents
Search Tools:
GlobTool - Finds files matching pattern
GrepTool - Searches file contents
Execution Tools:
Bash - Executes shell commands
BatchTool - Runs multiple tools in parallel
Agent Tools:
dispatch_agent - Creates a sub-agent to perform complex tasks
You can see that search tools are included; they let Claude search your code base for relevant files. It seems like a conversational context is also included in prompts, because it contains a note at the bottom of my chat which says “Context left until /compact: 24%”, which seems to imply that it periodically compactifies the conversational context.
I was curious about the dispatch_agent tool. It appears to allow CC to dispatch a copy of itself to investigate pernicious confusions. It gave the example of a bug, where an agent is created to look at files until it understands what’s going on, then report back. It is notably not able to edit files or execute commands, so it’s solely able to look at the code and synthesize an explanation.
Claude also tells me that it does Tool Composition, which just means that it runs multiple steps (View, Edit, GrepTool, etc) sequentially or in parallel. Basically, it might decide to execute several searches in parallel, then from the results of that, it might decide to “View” multiple files to basically load them into context. Then, it might decide to edit one file, then another.
Using Both Tools
Ironically, the project I’m currently working on is Aider Advanced, a fork of Aider which adds additional commands. In fact, I’m doing this deep dive into Claude Code because I want to understand the different paradigm that it uses, as inspiration for my own project.
So, let’s fire up both aider and claude to edit my fork of Aider.
One thing I notice is that aider defaults to a sort of YOLO mode in which it just makes edits right away, but CC asks you before making edits. Another big difference is that aider wants you to use commands like /ask, /code, /architect, /test, and so on, while CC wants you to do everything in natural language, and it routes it internally.
It’s refreshing that CC doesn’t need me to manually manage which files it has access to. It’s a little bit slower because it’s constantly asking itself which files to look at, but the slow-down is worth it.
One thing I prefer about Aider is that it streams its thought process to you, so you get quick feedback about its thinking. That means you get more immediate insight into how it’s approaching the problem.
But, at the end of the day, both tools are doing the same thing. They’re both sending my code to Claude and asking it to make edits. They’re both going to end up with very similar edits because the core model is the same. Of course, you can use other models with Aider. The central difference between the tools is how they manage context.
Summary of Differences
Speediness
Aider: Very fast. When you write a command and hit enter, the tool very quickly assembles a prompt, dispatches it to the LLM, and streams the response to the terminal.
Claude Code: Slow and thoughtful. Basic parts of the response, like assembling the proper context, are done through an LLM query. It appears that there is significant back-and-forth between your machine and Claude that is not streamed to the terminal — you only see a summary. CC will get back to you when it has a response or a request.
Repository Understanding
Aider: Uses sophisticated repository mapping with embeddings, semantic chunking, and graph-based code relationships. Implements token-aware context window optimization to fit large codebases. Composes the prompt programmatically by using
tree-sitterto summarize the codebase.Claude Code: Uses simpler file-based analysis, directory scanning, and basic dependency tracking. Lacks specialized context window management. Instead, the agent is encouraged to
grepandlsthe codebase to find the places it needs to edit.
Console UI
Aider: Pretty text formatting for code and markdown. Most responses from the LLM are streamed, so you can see its thought process.
Claude Code: A more condensed UI where each thing happens in its own box. Each operation is mostly hidden from the user. File edits are only shown to the user if the tool wants to ask for permission to edit, and then they are hidden again. This leads to a cleaner, simpler UI, but with less transparency.
Overall Review
For the past year, I’ve used Aider as my primary AI coding tool. I basically switch back and forth between it and an IDE. Overall, I still prefer Aider, but Claude Code does a great job and the core task-based philosophy it uses is pretty interesting. Will I switch to Claude Code sometimes? I think I’m likely to use CC for some projects, especially when the repository is very large.
I did this review as a precursor to adding more automation to Aider in Aider-Advanced. The plan is to get the best of both approaches there. Stay tuned.