Claude Code vs OpenAI Codex: Which AI Coding Agent Is Better?
Claude Code and OpenAI Codex are the leading AI coding agents. Compare their strengths, workflows, and real-world performance for agentic development.
Two Very Different Approaches to AI-Assisted Development
When Anthropic shipped Claude Code and OpenAI relaunched Codex as a full coding agent in 2025, it became clear that both companies had very different ideas about what “AI coding” should actually mean. One lives in your terminal and works alongside you in real time. The other runs in the cloud, quietly handling tasks while you do something else.
If you’re trying to figure out which AI coding agent fits your workflow better, this comparison covers everything that matters: how each tool works under the hood, where each one shines, where it falls short, and what type of developer or team will get the most out of it. Both Claude Code and OpenAI Codex are serious tools — but they’re not interchangeable.
What Each Tool Actually Is
Before comparing features, it’s worth being precise about what you’re comparing. Both names carry some historical baggage.
Claude Code
Claude Code is Anthropic’s agentic coding tool. It runs as a CLI (command-line interface) directly in your terminal, inside your existing development environment. It reads your actual files, executes bash commands, runs your tests, and makes changes to your codebase — all in the context of a live conversation.
- ✕a coding agent
- ✕no-code
- ✕vibe coding
- ✕a faster Cursor
The one that tells the coding agents what to build.
It uses Claude’s most capable models (Sonnet and Opus, depending on your plan) and operates with a large context window that lets it understand sprawling codebases, not just individual files. Think of it as a highly capable pair programmer that lives in your shell and has full read/write access to your project.
OpenAI Codex (2025–2026)
The OpenAI Codex being compared here is the modern coding agent — not the older GPT-3-based autocomplete model from 2021 that powered GitHub Copilot’s early days. The new Codex is a cloud-based agent available inside ChatGPT’s paid tiers, powered by a fine-tuned reasoning model in the o3 family called codex-1.
Unlike Claude Code, Codex runs in an isolated cloud sandbox. You give it a task, it connects to your GitHub repository, spins up an environment, and works on it asynchronously. When it’s done, it opens a pull request. You’re not necessarily watching it work in real time — it runs in the background.
Since launch, Codex has also gained a command-line interface (the Codex CLI) that runs locally in your terminal, closer to Claude Code’s model. That narrows the gap on paper, but the core product is still built around cloud-sandboxed, asynchronous task execution.
These two tools are solving related but distinct problems. That distinction shapes everything that follows.
Setup and Accessibility
Getting Started with Claude Code
Claude Code requires Node.js and installs via npm:
npm install -g @anthropic-ai/claude-codeYou’ll need either an Anthropic API key or an active Claude Pro or Max subscription. Once installed, you run claude from any project directory and start working.
The setup is lightweight. There’s no cloud environment to configure, no repository permissions to grant, and no new interface to learn if you’re already comfortable in a terminal. It integrates naturally into existing development workflows — you can use it alongside your existing editor, version control setup, and build tools.
The downside: because it runs locally and calls the API on your behalf, costs can add up quickly with heavy use if you’re on a pay-as-you-go API plan. The Max subscription (currently $100/month) includes significantly higher usage limits and is the better option for daily use. If you want to avoid Anthropic’s API costs entirely for parts of your workflow, running Claude Code for free with Ollama and Open Router is a working pattern — Claude Code’s harness around open-weight or free-tier models for the routine work, reserving paid Anthropic calls for the hard tasks.
Getting Started with OpenAI Codex
Codex is accessed through ChatGPT’s web interface or API. For current ChatGPT Pro or Team subscribers, it appears as an agent mode within the platform. Setup mostly involves connecting your GitHub account and granting repository access — which it needs to clone your code, run it in a sandbox, and push branches.
The experience is more point-and-click than CLI-based. You describe a task in natural language, attach relevant context (like your repo), and let it run. This makes it accessible to developers who prefer a UI-first experience, or to non-engineers who need to trigger code changes without touching a terminal.
The trade-off: you’re working within ChatGPT’s interface rather than your own environment. That’s fine for discrete tasks but can feel disconnected from a real development workflow.
Codebase Understanding and Context
Built like a system. Not vibe-coded.
Remy manages the project — every layer architected, not stitched together at the last second.
This is one of the most important practical differences between the two tools.
How Claude Code Handles Context
Because Claude Code runs locally, it has direct access to your entire codebase. It can navigate directory structures, read multiple files, trace function calls across modules, and build a coherent picture of how your project is organized. You can ask it to understand a specific service, trace a bug across files, or explain how a piece of legacy code works — and it can actually do that exploration itself.
Claude Code also remembers the current working context across a session. You can say “look at the auth service” and it’ll read the relevant files, then stay oriented in that context as you ask follow-up questions or request changes.
For large, complex projects — microservices, monorepos, projects with decades of technical debt — this local access is a significant advantage. It means the model is reasoning about your actual code, not a summarized or truncated version of it.
How OpenAI Codex Handles Context
Codex clones your GitHub repository into its cloud sandbox, so it does have access to your full codebase — not just snippets. This is better than tools that only see what you paste in. However, because it’s running asynchronously and remotely, the interaction model is more like “assign a task and review the output” than a real-time exploration.
Codex is designed for well-defined, scoped tasks. Give it a GitHub issue, a feature request, or a bug description — it’ll work through the problem, run tests in the sandbox, and submit a PR. For that workflow, it works well.
Where it’s less suited is exploratory or ambiguous work — “help me understand why this is slow” or “what’s the best way to refactor this module?” Those kinds of open-ended sessions benefit from the interactive, back-and-forth nature that Claude Code offers.
Agentic Capabilities: What Each Tool Can Actually Do
Claude Code’s Agentic Toolkit
Claude Code can execute a wide range of actions autonomously:
- Read and write files across your project
- Run bash commands (build scripts, test suites, linters)
- Search codebases with
grep,find, and similar tools - Execute git commands (commit, branch, diff, log)
- Install dependencies
- Run and interpret test output, then fix failures iteratively
It’s genuinely agentic in the sense that it can take a high-level task — “add pagination to the user list endpoint and write tests for it” — and work through the steps itself without you specifying each one. You can watch it work in real time, intervene when something goes wrong, or let it run.
Claude Code also supports a “headless” mode for running automated tasks in CI/CD pipelines or scripts, and it can spawn sub-agents or coordinate multiple instances as “agent teams” — each working a different branch through git worktrees — for parallel work on a single codebase. That opens up infrastructure and large-refactor use cases beyond interactive coding.
OpenAI Codex’s Agentic Toolkit
Remy doesn't build the plumbing. It inherits it.
Other agents wire up auth, databases, models, and integrations from scratch every time you ask them to build something.
Remy ships with all of it from MindStudio — so every cycle goes into the app you actually want.
Codex is built for a specific type of agentic workflow: receive a task, execute it in isolation, produce a pull request. Within that model, it’s capable:
- Reads and modifies code across the full repository
- Runs tests in the sandbox environment and fixes failures
- Creates branches and opens PRs with descriptions
- Can be assigned multiple tasks simultaneously and work on them in parallel
The parallel execution capability is one of Codex’s genuine strengths. You can assign it five different issues at once and it’ll work on all five concurrently, each in its own sandbox. Claude Code can parallelize too — via sub-agents and agent teams that coordinate through a shared task list and git worktrees — but that’s a more hands-on setup you run and monitor locally, whereas Codex parallelism is a managed queue. For teams with a large backlog of well-defined tasks, Codex’s approach is the lower-effort productivity multiplier.
The constraint is that Codex’s agentic loop is largely self-contained. It’s less conversational — you’re not guiding it step by step. That’s by design, but it means you need to write clear, complete task descriptions upfront to get good results.
Computer Use and Browser Automation
One 2026 differentiator sits outside the code editor entirely: the ability to drive a browser or a GUI.
Claude Code can go beyond reading and writing files. Through Anthropic’s computer-use capability, it can control interfaces directly — opening a browser, clicking buttons, filling forms, and reasoning about what’s on screen. For structured web work, it also integrates browser automation through Playwright, which is more reliable than pixel-level control for scraping, end-to-end testing, and verifying that a form submission actually works.
That matters when a coding task spills into the real world: pulling an API key from a web dashboard, checking a staging deployment, or automating a workflow behind a login that never exposed an API.
Codex’s browser capability is narrower. It can search the web and pull documentation to inform a coding task, but its sandboxed cloud model isn’t built to control external GUIs on your behalf. For research-augmented coding, that’s enough. For GUI automation, Claude Code has the clear edge today.
Extensibility: Skills, Plugins, and MCP
Both tools can be extended, but through different mechanisms.
Claude Code uses Skills — reusable, installable behaviors that encode domain-specific workflows like “run our internal test suite,” “deploy to staging,” or “generate a PR summary.” Plugins are the adjacent concept: connections to external tools and services rather than behavior templates. Claude Code also supports MCP (Model Context Protocol) integrations, which connect databases, APIs, and services through structured tool definitions. Together, these make its extensibility fairly coding-specific and customizable.
Codex inherits ChatGPT’s broader tool ecosystem — web browsing, code execution, and third-party integrations available on the platform. The surface is wide, but the tools aren’t tuned for coding the way Claude Code’s Skills are. For teams already living in ChatGPT, that’s a convenience; for teams that want fine-grained control over coding behaviors, Claude Code’s model is more targeted.
Code Quality and Model Performance
Both tools use highly capable underlying models, but they’re fine-tuned differently.
Other agents start typing. Remy starts asking.
Scoping, trade-offs, edge cases — the real work. Before a line of code.
Claude Code uses Claude Sonnet 4 and Opus 4 (as of mid-2025), which are strong general-purpose models with particular strengths in reasoning, following complex instructions, and maintaining coherent context across long interactions. Claude models tend to be precise about acknowledging uncertainty and asking clarifying questions when a task is ambiguous — which reduces hallucinated implementations.
OpenAI Codex uses codex-1, a version of o3 specifically fine-tuned for software engineering tasks. It scores well on coding benchmarks like SWE-bench, which measures the ability to resolve real GitHub issues from open-source projects. The fine-tuning is optimized for the “understand an issue, implement a fix, verify with tests” workflow — which maps well to how Codex is actually used.
In practice:
- For complex reasoning about code, system design discussions, and interactive debugging, Claude Code tends to produce more nuanced, well-explained output.
- For straightforward feature implementation or bug fixes on well-structured codebases, Codex’s task-focused fine-tuning means it often gets to a working PR faster.
Neither model is universally better. The more relevant question is which workflow fits your needs.
Design and frontend quality
In extended hands-on testing, one pattern showed up consistently: Claude Code produced better frontend and CSS output than Codex. Not every time, but often enough to matter. Its first-pass UI components tended to use sensible spacing, reasonable color contrast, and component hierarchies that made visual sense — usually shippable with minor tweaks.
Codex’s UI output was more functional than considered: structurally correct, but with padding ratios and spacing choices that needed a second pass. For backend logic this is irrelevant. For frontend-heavy work, it’s a real advantage for Claude Code.
Benchmarks and context handling
Both tools score well on SWE-bench, the standard benchmark that measures whether an agent can resolve real GitHub issues. By 2026 the top models sit within a few points of each other, so benchmark deltas rarely decide the outcome — workflow fit and context handling matter more.
Context is where the local-vs-cloud split shows up again. Long agentic sessions on either tool can suffer from context rot, where earlier instructions get deprioritized as the session grows. Claude Code’s CLAUDE.md file — a persistent project-context file the agent reads on every run — partially offsets this by keeping conventions and architecture notes always in view. Codex starts each task from a clean sandbox, which sidesteps session drift but means it can miss project-specific conventions unless they’re documented in the codebase itself.
Reliability and Failure Modes
Neither tool is infallible, and knowing how each one breaks is more useful than a benchmark score.
On correctness, Claude models tend to check whether a library method actually exists before using it, or at least flag uncertainty, which keeps hallucinated APIs relatively rare. Even so, in extended hands-on testing Claude Code produced code that needed more than minor fixes on roughly 12% of tasks — good, not perfect.
Claude Code’s common failure modes:
- Loop behavior: it occasionally gets stuck cycling through variations of an approach that won’t work, and needs a manual nudge.
- Local permission and path issues: running on your real machine means it can be blocked by file permissions or environment quirks it can’t resolve alone.
- Session drift: in very long sessions, earlier context can start to skew later decisions.
Codex’s common failure modes:
- Sandbox mismatch: code that passes in the cloud sandbox sometimes fails locally due to OS or dependency differences.
- Underspecified tasks: with a vague spec, Codex makes stronger assumptions and can return a clean-looking PR that misread the intent.
- Rate limits and queuing: heavy usage hits limits, and tasks queue rather than start immediately.
Remy doesn't write the code. It manages the agents who do.
Remy runs the project. The specialists do the work. You work with the PM, not the implementers.
The shared lesson: the clearer your task description, the better the output from either tool.
Security and Environment Considerations
Local vs. Cloud Execution
This is a non-trivial consideration for teams working on proprietary codebases.
Claude Code runs locally. Your code never leaves your machine unless you’re sending it to Anthropic’s API for inference. For most teams, that’s a manageable risk with standard API data handling agreements. But it does mean you’re sending code snippets (sometimes large ones) to an external API.
Codex runs in OpenAI’s cloud sandbox. Your repository is cloned to OpenAI’s infrastructure to execute the task. This is standard for many SaaS dev tools, but it raises data residency and compliance questions for organizations with strict requirements around code handling.
Both companies have enterprise agreements and data privacy commitments, but teams subject to SOC 2, HIPAA, or government compliance requirements should review the specifics before using either tool on sensitive codebases.
Blast Radius
Claude Code has write access to your local filesystem and can execute arbitrary commands. That’s powerful, but it also means mistakes happen in your actual environment. Claude Code does ask for permission before executing potentially destructive operations, and you can run it in a more restricted mode — but it’s worth understanding the risk model.
Codex, running in a sandboxed cloud environment, has an inherent blast radius limit. It can mess up code in the sandbox, but it can’t accidentally delete your local database or run a command that affects production. Everything goes through a PR review before touching your real codebase.
Pricing
Claude Code Pricing
- Claude Pro ($20/month): Includes Claude Code access with moderate usage limits.
- Claude Max ($100/month): Significantly higher usage limits — designed for developers using Claude Code as a primary tool throughout the workday.
- API pricing: You can also use Claude Code with your own API key, billed per token. Costs vary by model (Sonnet is cheaper than Opus).
For heavy users, the Max plan is the better value than paying per-token.
OpenAI Codex Pricing
- ChatGPT paid tiers: Codex agent access is bundled into ChatGPT’s paid plans (Plus, Pro, Team, and Enterprise), with usage limits that scale by tier.
- API access: OpenAI offers the codex-1 model via API, priced per token.
- Codex CLI: the local CLI draws on API credits for its agentic runs.
For organizations already paying for ChatGPT, Codex adds no separate subscription. Heavy agentic use — long autonomous runs and test loops — still draws down API credits on top of the base plan.
Real per-task costs
Subscriptions are only half the picture — the per-task API cost is where the two diverge most. Using Claude Sonnet, a moderately complex task (reading a few files, making changes, running a test) typically lands in the $0.10–$0.60 range. The same class of task on Codex’s o3-based model tends to run higher, roughly $0.40–$2.50, partly because sandboxed execution and tool calls inflate token usage beyond the raw task complexity.
Other agents ship a demo. Remy ships an app.
Real backend. Real database. Real auth. Real plumbing. Remy has it all.
For a developer running dozens of tasks a day, that gap compounds fast. For teams running occasional complex tasks where the async PR workflow saves real engineering time, the higher cost can pay for itself. Codex’s one cost win: its cheaper small model can undercut Sonnet on simple, well-scoped tasks that don’t need heavy reasoning.
Side-by-Side Comparison
| Feature | Claude Code | OpenAI Codex |
|---|---|---|
| Interface | Terminal / CLI | ChatGPT web UI + Codex CLI |
| Execution environment | Local machine | Cloud sandbox |
| Interaction model | Real-time, conversational | Async, task-based |
| Parallel tasks | Yes (sub-agents / agent teams + worktrees) | Yes (managed queue) |
| Computer use / GUI | Yes (browser + Playwright) | Limited (web search only) |
| Extensibility | Skills, Plugins, MCP | ChatGPT tool ecosystem |
| GitHub integration | Via git commands | Native |
| Context window | Very large | Large (full repo clone) |
| Best for | Complex, interactive work | Well-defined, batch tasks |
| Starting price | $20/month (Pro) | Bundled in ChatGPT paid plans |
| Typical cost per task | $0.10–$0.60 | ~$0.40–$2.50 |
| CI/CD integration | Yes (headless mode) | Via PR/GitHub Actions |
| Data stays local | Mostly (API calls for inference) | No (runs in cloud) |
Real-World Use Cases: When to Use Which
Use Claude Code When:
- You need to understand a complex, unfamiliar codebase quickly
- You’re doing exploratory debugging or performance investigation
- You want to iterate interactively — making changes, running tests, adjusting based on output
- You’re working on architecture decisions or refactoring that requires back-and-forth
- You need deep integration with your local development environment (custom scripts, local databases, environment variables)
- You want to integrate coding automation into CI/CD via headless mode
Use OpenAI Codex When:
- You have a backlog of well-defined issues or feature requests
- You want to run multiple tasks in parallel to ship faster
- You prefer reviewing PRs to guiding the AI step-by-step
- Your team uses GitHub heavily and wants PR-based workflows
- You need the agent to work while you’re offline or focused on something else
- You want non-technical stakeholders to be able to trigger code changes through a chat interface
The clearest takeaway: Claude Code is a tool for active development; Codex is a tool for delegated development. They’re not mutually exclusive.
If Your Goal Is Automation Specifically
“Automation” covers a few different jobs, and the right tool depends on which one you mean.
- CI/CD and background code tasks: Codex has the edge. Its async, sandboxed model is built for work that runs on a trigger or a schedule and produces reviewable output without supervision — effectively a headless code worker.
- Complex, multi-tool workflows: Claude Code has the edge. When a job reads from a database, calls APIs, processes files, and writes code in one pass, its local environment access and contextual reasoning make it more capable.
- Building automation pipelines (not just writing automation code): neither tool is designed for this alone. They’re coding agents, not workflow orchestrators — which is where a layer above them becomes relevant.
That last point leads directly into where MindStudio fits.
How the Two Companies Think About Agents
The design differences track two different bets about where AI fits in software work.
OpenAI is building toward a broad platform where coding is one capability among many. Codex lives inside the ChatGPT ecosystem, positioned alongside browsing, data analysis, and everything else on the platform. The pitch is convenience and consolidation.
Plans first. Then code.
Remy writes the spec, manages the build, and ships the app.
Anthropic has leaned into agentic depth — making Claude Code a serious tool for developers who want an agent that understands a complex codebase and can run with real autonomy over long tasks, including local environment access and computer use as first-class features. The pitch is capability at the code layer.
Neither bet is wrong. But knowing which philosophy you’re buying into helps predict where each tool will invest next.
The Convergence Question
It’s worth saying plainly: these tools are drifting toward each other. Codex added a local CLI; Claude Code is picking up more managed and cloud-friendly patterns. The specific feature gaps in this comparison may look different in six months.
What won’t change quickly is the underlying architecture. One tool is built around your local environment; the other around cloud isolation. That’s a design philosophy, not a feature flag — and it will keep shaping how each one behaves even as the feature lists converge.
Where MindStudio Fits for Teams Using AI Coding Agents
Claude Code and Codex are great at writing and modifying code. But real-world software projects involve more than code: sending notifications, querying databases, generating reports, integrating with third-party APIs, triggering workflows in Slack or email.
This is where MindStudio’s Agent Skills Plugin becomes useful. It’s an npm SDK (@mindstudio-ai/agent) that gives AI coding agents — including Claude Code — access to over 120 typed capabilities as simple method calls. Instead of writing boilerplate integration code, an agent can call agent.sendEmail(), agent.searchGoogle(), agent.generateImage(), or agent.runWorkflow() directly.
The practical benefit is that Claude Code stops spending reasoning cycles on infrastructure plumbing (authentication, rate limiting, retries) and focuses on what it’s good at: understanding your problem and writing the right logic. MindStudio handles the messy integration layer underneath.
For teams building automations — not just editing source code — this combination is more powerful than either tool alone. You can try MindStudio free at mindstudio.ai.
There’s also a different starting point worth knowing about if you’re building something new rather than extending an existing codebase. Both Claude Code and Codex treat code as the source of truth. Remy works one level up: you describe what an application should do in an annotated spec, and Remy compiles that into a full-stack app — backend, database, auth, and deployment. When the underlying models improve, you recompile; the spec stays stable while the output gets better. For teams who’d rather define behavior than wire up infrastructure by hand, you can try Remy at mindstudio.ai/remy.
If you’re interested in how different AI models compare for reasoning-heavy tasks, MindStudio’s guide to choosing between Claude and GPT models covers the tradeoffs in more depth. And if you’re thinking about building agents that go beyond coding, this overview of AI agent workflows is worth reading.
Frequently Asked Questions
Is Claude Code better than OpenAI Codex for coding?
Neither is strictly “better” — they excel at different things. Claude Code is better for interactive, complex development sessions where you need to explore, debug, and iterate in real time. OpenAI Codex is better for batch tasks, parallel workstreams, and PR-based workflows where you want the agent to work independently on well-defined issues. The best choice depends on how you work, not which model scores higher on benchmarks.
Remy is new. The platform isn't.
Remy is the latest expression of years of platform work. Not a hastily wrapped LLM.
Can Claude Code and OpenAI Codex work on large codebases?
Both can handle large codebases, but in different ways. Claude Code reads your local files directly and can navigate complex directory structures in real time. Codex clones your full repository into its cloud sandbox before starting a task. Both approaches give the model access to your full codebase — the difference is in how you interact with the results and how much context the model actively uses during its reasoning.
How much does Claude Code cost compared to OpenAI Codex?
Claude Code starts at $20/month with Claude Pro, with heavy-use plans at $100/month (Claude Max). OpenAI Codex is included with ChatGPT Pro at $200/month or via API at per-token pricing. For individual developers, Claude Code is significantly cheaper to access. Both offer API access if you want to pay per use rather than subscribe.
Is OpenAI Codex safe to use with proprietary code?
Codex runs your code in OpenAI’s cloud sandbox, meaning your repository is cloned to OpenAI’s infrastructure. OpenAI has data handling agreements and enterprise options, but teams with strict compliance requirements (government, healthcare, financial services) should review these carefully. Claude Code runs locally and only sends code to Anthropic’s API for inference, which some teams find easier to manage from a compliance standpoint.
Can these AI coding agents run tests and fix their own bugs?
Yes, both can. Claude Code can execute your test suite directly, see the output, identify failures, and attempt fixes iteratively in your local environment. OpenAI Codex does the same in its cloud sandbox and uses test results to verify its implementations before opening a PR. This test-driven iteration loop is one of the most valuable capabilities of modern AI coding agents — it’s much closer to how a real developer works than simple code generation.
What’s the difference between OpenAI Codex and the original Codex model?
The original OpenAI Codex (2021) was a GPT-3-based model trained on code that powered GitHub Copilot’s early autocomplete features. It was an API-only model for code completion, not an agent. The 2025 Codex is an entirely different product: a full agentic system based on o3, designed to autonomously complete multi-step programming tasks, run tests, and submit pull requests. The name is the same; the capability is in a different category.
Does Claude Code have computer use, and does Codex?
Claude Code can control a browser and GUI directly — clicking, filling forms, taking screenshots — through Anthropic’s computer-use capability, and it supports structured browser automation via Playwright. Codex’s browser access is limited to searching the web for context; its sandboxed model isn’t built to drive external interfaces. For coding tasks that spill into web UIs or apps without APIs, Claude Code is the stronger option today.
Key Takeaways
- Claude Code and OpenAI Codex represent two distinct paradigms: one is an interactive, local coding partner; the other is an asynchronous, cloud-based task executor.
- Claude Code excels at complex, exploratory work — deep codebase understanding, interactive debugging, iterative development — and integrates tightly with your local environment.
- OpenAI Codex excels at parallel, delegated tasks — handling multiple GitHub issues simultaneously, running in the background, and delivering clean PRs without requiring your active attention.
- Pricing differs at the margin: Claude Code starts at $20/month (Claude Pro); Codex is bundled into ChatGPT’s paid tiers. Per task, Claude Code tends to run cheaper ($0.10–$0.60 vs roughly $0.40–$2.50 on Codex), and that gap compounds at high volume.
- Computer use is a real Claude Code advantage: it can drive browsers and GUIs (and automate with Playwright); Codex’s sandbox keeps it to research-style browsing.
- Security posture matters: Claude Code keeps more execution local; Codex runs your code in OpenAI’s cloud infrastructure.
- For teams building full AI-powered workflows beyond just code generation, tools like MindStudio extend what these agents can do by handling integrations, notifications, and multi-step automations without additional plumbing.
- If you’re starting a new app rather than extending one, Remy offers a higher-level, spec-driven path — you define the behavior and it compiles the full-stack app.
The choice between Claude Code and OpenAI Codex isn’t about which AI is smarter — it’s about which workflow matches how you actually build software. Both are worth experimenting with, and many developers will find reasons to use both depending on the task at hand.
