Understanding the Tools

Disclaimer: Not all tools are created equal. The examples listed here might be invalid fairly soon as tools evolve fairly rapidly.

You can plot the different types of coding assistants on a graph showcasing the amount of human involvement required (lesser involvement = more automation). The first GitHub Copilot release I used allowed tab completions. It would either complete single lines or entire blocks of code. You could describe your intent by creating a function with a good name or by writing a comment. GitHub Copilot then supported inline prompting or chat sessions.

Coding agents are the current state of the art toolset for most developers on a day-to-day basis. They allow you to have conversations with them and you should treat them as teammates, albeit ones with anterograde amnesia.

Some problems can be parallelised and background agents triggered locally are incredibly powerful. Claude Code supports subagents is frequently used for analysis and solving multiple issues in parallel using git worktrees. Similarly, some people hook up agents to remote instances for things like code reviews using Claude Code or Copilot.

The extreme version of this is pure vibe coding. There is enough content out there about why this is a bad idea and the number of issues on real systems because of this.

Challenges with Using These Tools

When picking up a tool, I have started looking at different aspects of these tools

Choice of models

LLMs change quite quickly. Claude Sonnet 3.7 started off being the favourite model for most developers I know. When Claude Sonnet 4 came out at the same cost as 3.7, it became the new favourite model. Claude Opus 4 is great for larger codebases but expensive.

As I write this (mid-July 2025), the word on the street is that Grok 4 is currently the best model on the block. Choose something that has good coding insights and a large context window. Claude Sonnet has some of the smaller context windows but is tuned quite well for software development.

Cursor supports most of the best models and provides diversity. Tools like Claude Code and Gemini CLI are built and maintained primarily for use with a single model.

Ease of use

This one is fairly subjective and dependent on the developer’s preference. Tools like Cursor are VS Code forks and, thus, provide tight integration with the editor. Others like Claude Code, Codex, and Gemini CLI run on the terminal. Claude Code provides decent integration with the IDEs from the JetBrains family, and, thus provide good support to pair with your AI assistant.

Speed factors into ease of use too. While Jetbrains AI is the best integrated tool amongst all of these (if you prefer using their IDEs), their AI tool is one of the slowest. Slower tools mean slower feedback cycles. Slower feedback cycles are some of the worst things for dev experience.

Cost per change

Cost pays a huge part in someone’s choice of tools and running LLMs are fairly expensive to run. Most tools charge you per use, some by tokens, some by APIs. Since we are in the relatively early days of these tools and they are competing to capture the market, some still provide fixed investment offers in exchange for “unlimited plans.

Cursor used to be $20/month with unlimited usage till June 2025. While all “unlimited” usage is rate limited, if the usage limits are generous or the rate limits are not severe, users can manage to have a decent developer experience. More recently, Cursor updated their prices to make the $20/month Pro plan for “light users”. Daily users are recommended to use their $60/month Pro+ plan and power users are recommended to use their $200/month Ultra plan. Users on reddit have complained about how the Ultra plan is insufficient, though Cursor’s documentation says that it should be sufficient. This seems to be primarily because of heavy Claude Opus 4 usage, one of the most expensive models.

Another fixed usage tool is Claude Code for individuals with its Pro and Max plans. The $100/month Max plan seems to be the sweet spot for most heavy users and is probably the best value for money, at least until you look at the licensing.

Google’s Gemini CLI, at launch, announced the most insane free tier (that allows you to spend an estimated $620/day) but at the cost of training on your projects. More on this, in the next section. The free tier might not be this generous forever so if the “training on your data” bit isn’t a concern, enjoy Google’s generosity.

IP ownership indemnity and licensing

Licensing is a complicated topic and I go off of the advice that people much more qualified than me give in this space. The current understanding in this area suggests that you should prioritise tools with:

Company-level licensing: This avoids individual licenses and ensures broader protection.
No data training: The tool should not use your data for training purposes.
IP indemnity: The tool provider should offer indemnification against intellectual property claims.

Individual licenses should be avoided because their protections typically cover only the individual, not the organisation. If you work for a services company and develop intellectual property for clients, it’s crucial to prevent situations where these protections might not extend to your clients.

Avoid tools that train on your data if you are building something commercially. If you’re on an FOSS tool/system, you can ignore this fact. Google Gemini CLI’s free tier is a great example of this. They get to use your data to make the system better in exchange for you having a good coding assistant free of cost.

Anthropic, the creator of Claude Code, offers indemnity to its commercial users against lawsuits. While many other tools provide similar protection, Cursor, as of this article’s writing, does not offer such indemnity in its standard terms of service. However, Cursor’s Master Services Agreement (MSA) does include this protection, but only for customers with over 250 seats. This policy may be subject to change, and direct communication with Cursor’s support is recommended for clarification.

What do I use and recommend at this point?

For team members who are new to using coding assistants, start off with Copilot, where users will appreciate the fixed cost. Learn and experiment. Strengthen your core skills in this new world: Prompt Engineering and Context Engineering (more on these skills in another blog).

When you have mastered these skills, you should consider moving to an API-based tool that allows you to switch between models. Personally, I’m a fan of the Claude Sonnet and Opus models over OpenAI (and to some extent, Gemini). If you can manage costs well, move to Claude Code (or an open source tool like OpenCode or Aider). I would put OpenCode above Claude Code due to its flexibility.

Ultimately, the right coding assistant is the one that best empowers you and your team to write better code, faster, and with confidence.

How to Choose your Coding Assistants

Why it’s harder for a professional developer to use a tool despite the wide variety of choices