GitHub Copilot billing used to feel simple to me. I could keep it on inside VS Code, accept completions, ask Chat a few questions, and treat the subscription as a constant coding companion.
That framing changed after GitHub moved Copilot to GitHub AI Credits-based usage billing on June 1, 2026. Each plan now includes a monthly AI Credits allowance, and heavier model or agent usage can consume more credits.
I use Claude Code and Codex quite a lot for development now, and I keep watching Cursor as a serious alternative. So the question is no longer just “which AI coding tool is smartest?”
The better question is which task should go to which tool so I waste less time and money.
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Summary
- GitHub Copilot now uses AI Credits-based usage billing.
- Copilot still shines for IDE autocomplete and GitHub-centered workflows.
- Claude Code fits large codebase exploration, multi-file changes, and test/debug loops.
- OpenAI Codex works well as a ChatGPT-account-based coding agent across CLI, app, IDE, and web surfaces.
- Cursor makes the most sense when you want an AI-native editor workflow.
In this article
- What changed in Copilot billing?
- Where Copilot still works well from my own use
- When Copilot is still the right choice
- When Claude Code fits better
- When Codex fits better
- When Cursor fits better
- Copilot, Claude Code, Codex, and Cursor compared
- My practical split
- Conclusion
- FAQ
- References
What changed in Copilot billing?
According to GitHub’s official documentation, paid individual Copilot plans include monthly AI Credits.
| Plan | Monthly price | Monthly AI Credits included |
|---|---|---|
| Copilot Pro | $10 | 1,500 |
| Copilot Pro+ | $39 | 7,000 |
| Copilot Max | $100 | 20,000 |
The important point is that this is not simply “one prompt equals one credit.” GitHub explains that usage depends on input tokens, output tokens, cached tokens, and the model being used. A long Copilot cloud agent session over multiple files can consume more AI Credits than a short autocomplete-like interaction.
Copilot code review also consumes GitHub Actions minutes in addition to GitHub AI Credits. For teams, that means Copilot is no longer just a seat-cost decision. Usage monitoring matters too.
Where Copilot still works well from my own use
What I liked about Copilot was practical: it did not interrupt the coding flow.
When I write Java, there are many repetitive patterns: DTOs, test data, null checks, exception messages, small mappers, and method calls with similar names. These are too small to hand off as full tasks to Claude Code or Codex, but they still take attention when written manually.
Copilot is still useful in moments like these:
- inline IDE autocomplete
- short helper functions and test scaffolding
- filling repetitive patterns after I have already decided the direction
- GitHub PR, issue, and review workflows
- team adoption with relatively low onboarding friction
If a team already lives in GitHub, Copilot remains convenient. It fits into the IDE and GitHub surface without asking every developer to change their workflow.
When Copilot is still the right choice
1. IDE autocomplete is the main value
If you still write a lot of code directly and want AI beside you instead of ahead of you, Copilot remains a good fit.
2. Your team is GitHub-centered
For teams using GitHub issues, pull requests, reviews, and Actions, Copilot’s management and integration story is strong.
3. The team is just starting with AI coding tools
Claude Code and Codex are powerful, but they change how developers work. Copilot is easier to introduce because it feels more like an enhancement to the existing IDE.
When Claude Code fits better
Claude Code is different from Copilot autocomplete. Anthropic describes it as an agentic coding system that reads a codebase, edits across files, runs tests, and can deliver committed code.
I usually reach for Claude Code when I need:
- unfamiliar codebase exploration
- multi-file feature work
- refactoring plans
- test failure investigation
- iterative debugging from logs
- terminal, git, and test commands handled together
Claude Code feels strongest when it can think through the structure before changing files. For complex code, I like asking it to explain why something is built that way, plan first, and then modify.
Claude Pro is $20 per month, and Max starts from $100 per month. Claude Code access is included with Pro and Max, but usage limits are shared across Claude apps and Claude Code.
When Codex fits better
OpenAI Codex is a coding agent tied to a ChatGPT account. OpenAI says Codex is included across eligible ChatGPT plans, including Free, Go, Plus, Pro, Business, Edu, and Enterprise, with plan-specific usage limits and credit options.
To me, Codex feels close to Claude Code, but with a stronger “task agent across surfaces” flavor. OpenAI provides Codex through the app, CLI, IDE extension, and web.
I use Codex well for:
- throwing tasks from the CLI
- using a coding agent within an existing ChatGPT subscription
- code review, refactoring, and test generation
- splitting work into separate task-sized chunks
- quick implementation spikes before deciding the final direction
If you already pay for ChatGPT Plus or Pro, Codex is worth trying before adding another coding-tool subscription.
When Cursor fits better
Cursor started as an AI-powered editor, but now it is closer to a full development environment with Agent, Cloud Agents, CLI, Bugbot, rules, skills, and MCP.
Copilot feels like AI added onto an existing IDE. Cursor feels like an editor designed around AI from the start.
Cursor’s current individual pricing is:
| Plan | Price | API usage included |
|---|---|---|
| Pro | $20/mo | $20 |
| Pro Plus | $60/mo | $70 |
| Ultra | $200/mo | $400 |
Cursor separates the Auto + Composer pool from the API pool. Auto is meant for everyday agentic coding at lower cost, while selecting specific frontier models draws from API usage.
Cursor fits when you want:
- a VS Code-like editor built around AI workflows
- planning, editing, review, and agent work in one surface
- model switching inside the editor
- rules, MCP, and skills customization
- Cloud Agents or mobile agent workflows
The catch is that Cursor is an editor choice. If your IntelliJ, VS Code, or terminal workflow is already very solid, switching has a cost.
Copilot, Claude Code, Codex, and Cursor compared
| Tool | Strongest area | Cost watch point | Best fit |
|---|---|---|---|
| GitHub Copilot | IDE autocomplete, GitHub integration, team management | AI Credits usage, Actions minutes for code review | GitHub-centered teams and autocomplete-heavy developers |
| Claude Code | Codebase understanding, multi-file edits, test/debug loops | Pro/Max usage shared across Claude and Claude Code | Developers who delegate larger terminal-based tasks |
| OpenAI Codex | ChatGPT-based access, CLI/app/IDE/web surfaces, task agents | Agentic usage limits vary by task size | ChatGPT users doing reviews, spikes, and implementation tasks |
| Cursor | AI-native editor, Agent, rules, MCP, Cloud Agents | API usage when choosing specific models | Developers willing to move into an AI-first editor |
My practical split
I would not force one winner. I would split the work.
- Small inline help and GitHub workflow: Copilot
- Large refactors, codebase exploration, test failures: Claude Code
- Task-sized implementations, reviews, quick spikes: Codex
- AI-native editor workflow: Cursor
The question used to be “is one Copilot subscription enough?” Now it is closer to this:
Am I asking AI to help while I code, or am I delegating part of the development work?
If autocomplete is the center, Copilot is still strong. If I want an agent to read issues, plan, edit multiple files, run tests, and iterate on failures, Claude Code or Codex feels more natural. If I want the whole editor to become AI-first, Cursor is the serious candidate.
Conclusion: Copilot is not dead, but its role is narrower
GitHub Copilot is not suddenly a bad tool. It remains one of the most natural coding assistants for inline IDE help, especially in GitHub-centered teams.
But after the billing change, I no longer want to treat Copilot as the default for every AI development task. AI Credits, model cost, long context, agent sessions, and code review costs all matter.
For me, Copilot is now the always-on lightweight assistant. Bigger work goes to Claude Code or Codex. Cursor is the option to consider when I want to move the editor itself into an AI-native workflow.
Choosing an AI coding tool is becoming less about taste and more about task allocation.
FAQ
Did GitHub Copilot simply become more expensive?
Not exactly. The bigger change is that usage is now more explicitly measured through AI Credits. Light autocomplete-heavy usage may still fit comfortably, but long agent sessions and frontier-model work need monitoring.
Can Claude Code replace Copilot?
Not completely. Claude Code is better for larger delegated tasks, while Copilot is still more natural for line-by-line help while coding.
Should I pick Claude Code or Codex?
Claude Code fits deep terminal-based work over a codebase. Codex fits well if you already use ChatGPT and want a coding agent across CLI, app, IDE, and web surfaces.
Is Cursor better than Copilot?
It is more different than better. Copilot extends your existing IDE. Cursor asks you to work inside an AI-native editor.
References
- 🔗 GitHub Blog, “Updates to GitHub Copilot billing and plans”, 2026-06-01
- 🔗 GitHub Docs, “Usage-based billing for individuals”
- 🔗 GitHub Docs, “Models and pricing for GitHub Copilot”
- 🔗 Anthropic, “Claude Code” product page
- 🔗 Anthropic Support, “Use Claude Code with your Pro or Max plan”
- 🔗 Anthropic Support, “What is the Pro plan?” / “What is the Max plan?”
- 🔗 OpenAI Help Center, “Using Codex with your ChatGPT plan”
- 🔗 OpenAI, “Codex | AI Coding Partner from OpenAI”
- 🔗 Cursor Docs, “Models & Pricing”
- 🔗 Cursor Pricing page
<div class=”original-post-note”><strong>Original Korean version:</strong> This article is based on the Korean version and lightly adapted for English readers. <a href=”https://daily-it.com/2026/06/30/github-copilot-billing-claude-code-codex-cursor/”>Read the original Korean post</a>. <strong>Please show some love to Korean, too.</strong></div>