Developer's buying guide · AI assistants compared
Five good tools.
The right one
fits how you work.
ChatGPT and Codex, Claude and Claude Code, Gemini, GitHub Copilot, Cursor — every one of them can now chat, edit your code, run commands and open a pull request. The models trade the lead every few months. What doesn't change as fast is where each tool lives, how it's billed, and what it does with your code. That's what this page compares. 👇
Questions, debugging help, design discussion, learning a new stack.
Completions, inline edits and an agent that sees the file you're in.
Terminal or cloud agents that take a task and come back with a diff or PR.
No winner is declared here — on purpose. All five are capable enough that the deciding factors are your editor, your Git host, your company's data rules and what you already pay for. If you've already narrowed it to Anthropic vs OpenAI, the Claude Code vs Codex in your IDE guide goes deeper on setup and daily workflow.
02 · Compare like with like
🗺️ The five surfaces an AI assistant can live on
Most bad comparisons pit one vendor's chat app against another's IDE agent. Every major vendor now covers several of these surfaces, so compare them surface by surface.
| Surface | What it's good at | What to watch |
|---|---|---|
| 💬 Chat app (web, desktop, mobile) | Explaining, brainstorming, one-off snippets, reading logs and docs you paste in. | It only knows what you paste. Copy-paste is also where secrets leak. |
| ⚡ Inline completion | Finishing the line or block you're typing. Low friction, constant small wins. | Easy to accept code you didn't read. |
| 🧩 IDE agent / chat panel | Multi-file edits you review as diffs, with your open file and errors as context. | Only as good as the context it's given; one project at a time. |
| ⌨️ CLI agent | Long, multi-step tasks: run tests, fix, re-run, commit. Scriptable and CI-friendly. | It runs real commands — permissions and sandboxing matter. |
| ☁️ Cloud / background agent | Delegated tasks that run in a remote sandbox and return a branch or pull request. | Review happens after the fact; needs repo access and good tests. |
| 🔌 API | Building AI into your product, scripts or internal tools. Billed per token. | Costs scale with usage — estimate before you ship. |
The practical consequence: "Which AI is best?" is really three questions — which model reasons best on your code, which tool fits your editor and Git flow, and which plan your company will approve. The answers are often three different vendors, and several tools let you mix them.
03 · Who's who
🧰 The main AI assistants for developers
What each is, where it runs, and the honest trade-off. Strengths are qualitative on purpose — benchmark leads change hands too often to be worth printing.
🟢 ChatGPT & Codex
OpenAI
Surfaces: ChatGPT on web, desktop and mobile; Codex as an open-source CLI, a VS Code extension (also works in Cursor and Windsurf), a desktop app, cloud tasks that open PRs, and GitHub code review. Plus the OpenAI API.
Strengths: delegation. Cloud tasks run in sandboxes you can start from the app, the IDE or your phone and check later; approval modes are one simple dial.
Watch for: usage limits differ a lot between ChatGPT tiers — heavy agent use is what pushes people up a plan.
🟠 Claude & Claude Code
Anthropic
Surfaces: Claude chat on web, desktop and mobile; Claude Code in the terminal, VS Code and JetBrains, the desktop app, the browser and mobile app (cloud sessions), Slack and GitHub Actions. Plus the Claude API.
Strengths: configurability and long, careful codebase work. Per-repo permission rules, CLAUDE.md memory, skills, hooks, subagents and MCP servers live as files your team can commit.
Watch for: long agent sessions consume plan usage quickly; power users tend to need higher tiers or API billing.
🔵 Gemini & Antigravity
Surfaces: the Gemini app; Antigravity, an agent-first editor built on a VS Code fork, with a desktop app and CLI announced at I/O 2026; Gemini API and AI Studio; Gemini Code Assist for business customers.
Strengths: very large context windows, tight Google Cloud / Firebase / Android integration, and a strong free-to-start story via AI Studio.
Watch for: the lineup churned in 2026 — consumer Gemini Code Assist and consumer Gemini CLI were retired in June in favour of Antigravity. Check current names before you standardise.
🐙 GitHub Copilot
GitHub · Microsoft
Surfaces: completions and chat in VS Code, Visual Studio, JetBrains, Xcode, Eclipse and more; agent mode in the IDE; a cloud coding agent you assign issues to; PR code review; Copilot CLI (generally available since early 2026).
Strengths: it lives where your code already is. Multi-model (Claude, GPT and Gemini models to choose from), broadest IDE coverage, and mature org policy, audit and seat management.
Watch for: agent and premium-model use draws on a monthly credit allowance; completions don't.
🖱️ Cursor
Anysphere
Surfaces: an AI-native editor (VS Code fork) with fast Tab completion and an in-editor agent; cloud agents that return draft PRs; a CLI, a web/mobile agent view, and Slack, GitHub and Linear integrations.
Strengths: the smoothest editor-first experience — completions, multi-file edits and agent runs in one polished UI, with a choice of frontier models.
Watch for: you switch editors to get it, and paid plans are usage-based on top of the monthly fee — heavy agent use can cost more than the headline price.
🧪 Also worth knowing
Niche or bring-your-own-key
- JetBrains AI / Junie — native to IntelliJ, Rider, PyCharm
- Windsurf — another agentic VS Code-based editor
- Open-source agents (Aider, Cline, OpenCode) — use any model via your own API key; you control the data path
As of October 2026 — check the vendor's page. Surfaces, plan names and included usage change monthly. OpenAI, for example, announced reusable cloud environments for Codex in late September 2026, and Copilot moved to a credit-based allowance this year. Treat every specific on this page as a pointer to verify, not a quote.
04 · At a glance
📊 AI coding tools side by side
Coverage of each surface, not quality. A ✅ means a first-party offering exists as of October 2026.
| Surface | ChatGPT / Codex | Claude / Claude Code | Gemini / Antigravity | Copilot | Cursor |
|---|---|---|---|---|---|
| 💬 General chat app | ✅ | ✅ | ✅ | Chat in IDE & github.com | In-editor only |
| ⚡ Inline completion | — | — | ✅ (Antigravity, Code Assist) | ✅ core feature | ✅ core feature |
| 🧩 Extension for your existing IDE | VS Code & forks | VS Code & forks, JetBrains | Code Assist (business) | Widest range | — (is the IDE) |
| 🖥️ Own editor / desktop app | Codex app | Claude desktop app | Antigravity | — | Cursor |
| ⌨️ CLI agent | ✅ | ✅ | ✅ | ✅ | ✅ |
| ☁️ Cloud agent → PR | ✅ | ✅ | Background agents — check current offering | ✅ | ✅ |
| 🔀 Choice of model vendor | OpenAI models | Claude models | Mainly Gemini, some others | Multi-vendor | Multi-vendor |
| 🔌 MCP tool connections | ✅ | ✅ | ✅ | ✅ | ✅ |
| 📄 Project instructions file | AGENTS.md | CLAUDE.md | Rules files | copilot-instructions.md | Rules files |
| 💳 Typical billing | ChatGPT plan or API | Claude plan or API | Google AI plan, Cloud, or API | Seat + credits | Plan + usage |
Convergence is the real story. A year ago the columns looked very different. Today every tool has an agent, a CLI and a cloud mode, and most read an AGENTS.md-style instructions file. The differences that remain are defaults and polish — how permissions work, how review feels, how usage is metered.
05 · Before you paste company code
🔒 Privacy, data use and security
This is where the choice is often made for you. The same vendor can have very different data terms depending on which plan you're on.
🛡️ Agent-specific risks
- 🔑 Secrets in the workspace. Agents read files to understand your project — including
.env, config and keys. Keep secrets out of the repo and use ignore/exclusion settings. - ⚙️ Command execution. A CLI agent runs real shell commands. Start in a read-only or ask-before-running mode, and loosen it per project.
- 🧪 Prompt injection. Text in an issue, web page or dependency README can try to steer an agent. Be careful granting network access and write access together.
- ☁️ Cloud agents clone your repo into the vendor's sandbox. Confirm your organisation allows that before connecting a private repository.
At work, ask first. If your employer has an approved tool, use that one — even if you prefer another at home. Pasting proprietary code into a personal account is the most common way developers break policy without realising.
06 · What it really costs
💳 How AI coding tools are priced
Headline monthly prices are similar across vendors at each tier, so they rarely decide anything. What differs is how usage is metered — and agents use far more than chat.
📦 Subscription + limits
ChatGPT, Claude, Google AI plans
A flat fee with usage windows that reset. Predictable, until a long agent session hits the cap mid-task.
🎟️ Seat + credits
Copilot, Cursor
A seat price includes an allowance of agent and premium-model usage; extra is billed or blocked. Completions are usually unmetered.
🔢 Pay per token
Every vendor's API
Input and output tokens billed separately, output costing more. Cheapest for light use, most expensive for careless agents.
💡 What drives the bill up
- 📚 Context size. Agents re-send large parts of your codebase on each step. Big repos cost more per task.
- 🔁 Iterations. A test-fix-retest loop of ten rounds is ten large requests.
- 🧠 Model tier and reasoning effort. Top models and "think harder" settings can cost many times more per task.
- 👥 Team features. SSO, audit and admin controls usually live only on business tiers.
Estimate before you commit. Paste a typical prompt, file or log into the AI Token Calculator to see its token count and rough API cost across models — then multiply by the number of steps an agent takes. It's the quickest way to sanity-check an API budget or tell whether a plan's limits fit your day.
07 · Decide for your situation
🏁 Which AI coding assistant should you use?
Start from your constraints, not the leaderboard. Each row names a sensible starting point — not the only good answer.
| If this is you… | Start with | Why |
|---|---|---|
| 🏢 Your company already approved a tool | That one | Data terms, SSO and billing are solved. Learn it properly before looking elsewhere. |
| 🐙 Team lives in GitHub, mixed IDEs | Copilot | Works in nearly every editor, assigns issues to an agent, reviews PRs, and lets you pick models. |
| 🧩 JetBrains / Rider / Visual Studio user | Copilot or Claude Code | Both have native plugins; JetBrains AI is also worth a look. |
| ⚡ You want the fastest in-editor feel and don't mind switching editor | Cursor | Completions, edits and agent in one polished VS Code-style UI. |
| ⌨️ Terminal-first, want deep configuration shared via Git | Claude Code | Permissions, commands, hooks and memory as committed files. |
| ☁️ You want to hand off tasks and review PRs later | Codex | Cloud tasks from app, IDE or phone are its centre of gravity (Copilot, Cursor and Claude Code offer this too). |
| 🔥 Google Cloud, Firebase or Android shop | Gemini / Antigravity | Closest integration with Google's platform and consoles. |
| 🎓 Learning to code, or on a budget | A free tier | Copilot Free and the free chat apps are enough to learn with. Ask it to explain, not just to write. |
| 🔐 Strict data control, or want any model | Open-source agent + API key | You choose the model, the provider and the data path — and pay per token. |
🧪 Run a one-week bake-off
- Pick two finalists — no more
- Use three real tasks from your backlog: a bug, a small feature, a refactor
- Same prompt, same repo state, clean git status each time
- Score the diffs you'd actually merge, and the time you spent reviewing
🤝 Using two is normal
- A chat app for thinking and explanations
- One agent for the codebase, set up properly
- Keep both
CLAUDE.mdandAGENTS.mdif your team is mixed - Don't run two agents on the same working tree at once
08 · Before you pay
✅ AI tool evaluation checklist
Answer these for each finalist. Most are on the vendor's pricing, trust or docs pages — and every one of them can change, so re-check at renewal.
🧑💻 Fit
- ✅ Works in the editor(s) your team actually uses — including Visual Studio or JetBrains if relevant
- ✅ Integrates with your Git host (GitHub, GitLab, Azure DevOps, Bitbucket) for PRs and review
- ✅ Reads a committed project-instructions file so conventions are shared
- ✅ Connects to the tools you need via MCP (database, tracker, docs)
🔒 Trust
- ✅ Clear answer on training use and retention for your plan
- ✅ SSO, audit logs and admin policies if you're a team
- ✅ Permission / sandbox model you understand before the agent runs commands
- ✅ Way to exclude sensitive files and repositories
💳 Cost
- ✅ What happens at the limit — slowdown, extra charges, or a hard stop?
- ✅ Which features consume credits and which are unlimited
- ✅ Realistic monthly usage estimated with the AI Token Calculator
- ✅ Monthly vs annual commitment, and how easy it is to switch
🎯 Quality — measured on your code
- ✅ Tested on your own repo and language, not a demo project
- ✅ Handles your build and test commands without hand-holding
- ✅ Produces diffs you'd merge after normal review
- ✅ Admits uncertainty instead of inventing APIs
The takeaway: the tool you configure well beats the "best" tool used as autocomplete. Pick one that fits your editor, Git host and data rules, commit a project instructions file, set sensible permissions, and review every diff. The Claude Code vs Codex guide shows what that setup looks like in practice.