ChatGPT Health Launches With Medical Records and Apple Health

Sol, Terra, and Luna make GPT-5.6 look more like a product family than a single model. The naming also signals what OpenAI is trying to change: users no longer have to choose only between an expensive flagship and a much smaller model. Instead, they get three capability tiers designed for different workloads. The important caveat is that GPT-5.6 is currently in limited preview, and OpenAI says it is not available in ChatGPT during this preview period.On the other side, Anthropic positions Claude Fable 5 as a frontier model for reasoning, software engineering, scientific research, and long horizon agentic work. The useful question is therefore not simply which model is smarter. It is which product architecture helps a team complete real work with predictable quality, latency, and cost.What GPT-5.6 actually isAccording to OpenAI's preview announcement, GPT-5.6 consists of Sol, Terra, and Luna. Sol is the flagship and most capable option, Terra is a strong lower cost model, and Luna is the fastest and most cost efficient member of the family.The important change is how OpenAI divides demand into three tiers. A research team might use Sol for a difficult reasoning problem, a product team might run most daily work on Terra, and a high volume system might use Luna for thousands of short requests. This looks more like an infrastructure strategy than the launch of a single new chatbot.Availability matters: OpenAI says GPT-5.6 is not available in ChatGPT during the preview. An experience in an API, developer tool, or partner platform should not be treated as the final ChatGPT experience.Sol is designed for difficult, extended workSol is positioned as the strongest GPT-5.6 model for deep reasoning, complex coding, and long multi step tasks. A software team might ask it to understand a repository, identify the cause of a bug, propose a minimal patch, and write regression tests. Sol's value is not answering a short question quickly. It is maintaining the objective while working through a longer chain of decisions.OpenAI also highlights stronger cyber capability as reasoning increases. That can be useful for authorized security testing and vulnerability analysis, but it also makes access controls, logging, sandboxing, and human approval more important.Terra aims for the practical middleTerra targets the broadest category of work: document analysis, content production, application development, research synthesis, and operational support. If Sol is the specialist called for the hardest problem, Terra is the strong team member expected to work throughout the day without making every request unnecessarily expensive.A marketing team could use Terra to read market reports, extract insights, build an outline, and draft several content variants. A development team could use it for code review, test generation, and tickets with a clear scope. This tier could become the default if its real world quality remains consistent.Luna prioritizes speed and scaleLuna is designed for low latency and lower cost. Classification, conversation summaries, field extraction, drafting, and ticket routing do not always require the strongest model. In these cases, response time and total operating cost matter more than maximum reasoning capability.Fast does not mean suitable for everything. If a task requires source verification, a long plan, or a code change with a large blast radius, a team should move it to Terra or Sol instead of forcing Luna beyond its intended role.Claude Fable 5 takes a different routeAnthropic presents Claude Fable 5 as a frontier model for reasoning, software engineering, vision, scientific research, and long horizon agentic work. Instead of emphasizing three product tiers in one generation, Anthropic's message focuses on the capability of a powerful model working inside the Claude ecosystem.This difference changes deployment decisions. With GPT-5.6, an engineering team might build a router that sends each request to Sol, Terra, or Luna. With Fable 5, the focus may be on optimizing prompts, tools, context, and reasoning budgets around one primary model. Neither approach is universally better because the answer depends on workload and operational maturity.A fair comparison: Do not run one prompt and declare a winner. Build a test set covering short tasks, long reasoning, coding, extraction, and recovery from errors. Measure accuracy, latency, the number of human corrections, and the total cost of a completed task.Coding and agentic work depend on the surrounding toolsBoth GPT-5.6 Sol and Claude Fable 5 target complex software work, but the practical experience depends heavily on the system around the model. The ability to read a repository, execute commands, observe results, and correct mistakes can matter as much as a benchmark score. For OpenAI workflows, the Codex page is a useful starting point for understanding how a model participates in coding work.Fable 5 may be attractive to teams already invested in Claude and long running agentic workflows. Read our Claude Fable 5 coverage for more context on Anthropic's positioning and the types of work it targets.What early forum experience tells usEarly discussions on Reddit and developer communities focus on how different Sol, Terra, and Luna feel in real work. Some users describe Sol as the better fit for multi step tasks, Terra as the practical option for routine work, and Luna as the interesting choice for speed. These observations match OpenAI's positioning, but they do not establish a precise quality gap.Forum reports are useful because they reveal the questions real users care about. However, they are self selected evidence. People may use different prompts, access levels, integrations, and preview versions. A result from a developer platform does not guarantee the same result when a model eventually appears in ChatGPT.Early positivesThe three tiers make it easier to understand which model belongs to which workload.Luna creates a clear expectation of low latency for high volume systems.Terra could become a default if it delivers stable quality at a practical cost.Sol is expected to be stronger for coding, long reasoning, and tasks with several verification steps.Open questionsHow large the practical quality gap between Sol and Terra will be on common workloads.The total cost after retries, corrections, and human review are included.How Luna behaves with long prompts and many constraints.Whether performance remains stable as GPT-5.6 expands beyond preview access.Forum reports are not benchmarks: Community experience should help you choose test cases, not make a production purchasing decision by itself.Comparing GPT-5.6 and Fable 5 by workloadWriting and document analysisTerra appears positioned for most document work because it balances capability and cost. Fable 5 may be attractive when documents are long, questions are complex, and the model must maintain an argument across a large context. A useful evaluation should score citation accuracy, structural consistency, and how much editing is required before publication.Software development and debuggingSol and Fable 5 are both candidates for difficult coding tasks. A representative test should include reading existing code, identifying the root cause, producing a minimal fix, writing tests, and explaining risk. Asking a model to create an isolated function from scratch does not reflect how well it works in a real repository.High volume processingLuna has the clearest positioning advantage when speed and cost dominate. At thousands of extraction or classification requests per day, a small difference in price and latency can have a large effect. Fable 5 may be unnecessarily expensive for a workload that only needs short, structured outputs.Research and long reasoningSol and Fable 5 should be compared with tasks that have verifiable outcomes rather than open questions that merely sound impressive. Give both models the same research material and ask them to identify assumptions, detect contradictions, propose an experiment, and explain what evidence is missing. The better model is the one that helps users discover errors faster, not the one that writes the longest answer.Should you choose Sol, Terra, Luna, or Fable 5?If you want maximum capability inside the OpenAI ecosystem, Sol is the first model to test. If you need a strong model for regular use, Terra has the more practical position. If your workload contains many short and repetitive tasks, Luna could reduce operating cost. Fable 5 remains relevant for teams invested in Claude or focused on long reasoning and agentic work.Because GPT-5.6 is still in preview, replacing an entire production workload would be premature. Run the models in parallel on real but sanitized data, record failures, and use the same criteria for every candidate.A test plan you can use nowSelect 20 tasks that represent real work, including easy and difficult cases.Run each task on Sol, Terra, Luna, and Fable 5 when access allows.Score accuracy, response time, total cost, and required human correction.Track severe failures separately instead of relying only on averages.Choose a model for each workload category rather than forcing one model to do everything.Is GPT-5.6 worth switching to now?The most important change in GPT-5.6 may not be Sol's raw capability. It is OpenAI's decision to turn one model generation into three operational tiers. That could help organizations control cost, but only if they can classify workloads and route requests intelligently.The practical next step is to build a small benchmark from your own data. If Sol wins difficult tasks, Terra is good enough for routine work, and Luna handles high volume requests reliably, the three tier architecture has real value. If Fable 5 remains more consistent on long reasoning, a multi model strategy may still be better than committing to one provider.

Three million Codex users per week — up six times in just the first three months of 2026. That number tells you something: Codex is the rising star. OpenAI is turning it into an all-in-one tool, which means Codex is no longer just a playground for developers. What is Codex? A tool that's not just for developers Think about this scenario: you want to build a spreadsheet that automatically updates every week, or a small website to let customers book appointments, or simply a tool that summarizes your email reports each morning without opening dozens of tabs. Previously, these things required a developer. With Codex, you just type your request in plain English and wait for the result. Codex is OpenAI's AI agent, launched in May 2025 and deeply integrated into the ChatGPT ecosystem. Its core difference from regular ChatGPT is that Codex doesn't just answer — it actually does the work through a code execution environment. You assign a task, Codex plans it out, executes each step, checks the result, and returns a finished product ready to use. No need to understand what code is, no need to monitor every command. Codex can now run as a standalone desktop app available for both Windows and macOS, and has recently expanded to Android and iOS on mobile as well. You can sign in using your existing ChatGPT account. Codex is currently available on ChatGPT Plus, Pro, Business, and Enterprise plans, though Free and Go users also get limited trial access. What Codex can do for you Build apps or small websites from a description You don't need to know HTML or JavaScript. Just describe what you need: "Create a simple appointment booking page with fields for name, phone number, and date/time selection, and send an email notification whenever someone books." Codex will build the entire interface, handle the logic, and guide you through publishing it online. A startup team in the US once shared that they completed in one weekend what would have previously taken an entire quarter — and it wasn't a team full of developers. Automate repetitive tasks This is where non-developer users will find the most value. For example: every week you have to consolidate revenue data from three different Excel files, merge them, and send a report to your manager. Codex can build an automated workflow that does this for you on a schedule and delivers the result without you ever opening your laptop. With the Automations feature launched in the April 2026 update, Codex can take on long-horizon tasks, pause, resume, and complete them over multiple days without needing to be reminded. Generate images and prototypes directly in the app Codex integrates image generation powered by GPT Image 2.0 directly inside the app. You can ask Codex to create interface mockups, product banners, or illustration assets for a document — all within the same workflow, without switching to another tool. For content creators, marketers, and solo founders, this is a genuine advantage: the entire journey from idea to finished output can happen in a single window. Control your computer to work in the background Since April 2026, Codex can operate Mac applications using its own cursor, viewing the screen and clicking and typing to complete tasks while you continue using the machine normally. A simpler way to picture it: you're in an online meeting while Codex has Figma open, editing a design and saving the file according to instructions you set earlier. Two things happening in parallel, neither getting in the other's way. The computer use feature is currently only available on macOS and is not yet available in the EU or UK. You will need to grant Codex Accessibility and Screenshot permissions during initial setup. How to get started with Codex Codex requires installing the desktop app on Windows or macOS — it does not run directly in a web browser. The setup process is straightforward and takes just a few minutes. Step 1: Go to openai.com/codex and download the version for your operating system. On macOS, there are two separate builds for Apple Silicon (M1 and later) and Intel chips. On Windows, there is a single universal build. Step 2: Install the app and sign in with your existing ChatGPT account or OpenAI API key. Step 3: Choose a project folder you want Codex to work within — you can also link it to a github repository — or skip this step if you only want to assign standalone tasks like creating files, generating images, or automating a workflow. Step 4: Type your request in natural language, and be as specific as possible. Instead of "make me something about a report," try: "Create an Excel file summarizing monthly revenue from the data I provide, add a bar chart comparing each month, and highlight the month with the highest revenue." The more specific your request, the better the result. Codex works best when you clearly describe the input, the desired output, and any constraints you need — such as file format, display language, or calculation rules. Codex vs Claude Code, Antigravity, and Cursor from a non-technical user's perspective If you're not a developer, the real question isn't "which tool is technically more powerful" — it's "which tool can I use right now without learning anything new." From that angle, these four tools are clearly different from one another. Codex and Claude Code Claude Code from Anthropic is Codex's most direct and formidable competitor. In terms of raw technical output quality, Claude Code currently leads the pack — producing cleaner code, tighter logic, and handling large, complex codebases more effectively. However, Claude Code is explicitly designed for developers: it runs in the terminal, requires command-line installation, and notably has no image generation capability. If you're not comfortable in a terminal, Claude Code is a barrier from the very first step. Codex, by contrast, offers a more user-friendly desktop interface, integrates image generation within the same workflow, and is noticeably more accessible to non-technical users. Codex and Antigravity Both require a desktop app, but their underlying philosophies are completely different. Codex is built around a "hand off the task and wait for results" model: you describe what you need, the agent runs in an isolated cloud sandbox, and returns a finished product without affecting your machine at all. It suits people who want to automate workflows, create files, or build something without monitoring every step. Antigravity works in the opposite direction: the agent runs directly on your machine, watches your screen, opens applications, and collaborates with you in real time while you work. If you want an AI colleague working alongside you — observing and reacting to what's happening on your screen — Antigravity is the better fit. Codex and Cursor Cursor is built on VS Code and targets developers who want to keep their familiar working environment intact. For non-coders, Cursor is largely inaccessible because the entire experience revolves around editing code inside an editor. Cursor excels at understanding an entire codebase and offers flexibility in choosing AI models, but those advantages are for developers — not for general users who need to automate workflows or build something from scratch. In summary, from a non-technical user's perspective: Codex: Friendly desktop interface on Windows and macOS, capable of generating images, well-suited for users who want AI as an automated workflow tool. Claude Code: Best technical output quality, but developer-oriented and cannot generate images. Antigravity: Agent works directly on your machine in real time, suited for users who want to collaborate with AI while they work. Cursor: Best for developers keeping their VS Code workflow intact; not suited for general users. Who is Codex best for? If you're a content creator who wants to build a landing page for a campaign without hiring a developer, Codex fits. If you're a marketer who needs to automate weekly reports pulling from multiple data sources, Codex fits. If you're a solo founder who needs to ship a product fast without a technical team, Codex fits. If you're a teacher who wants to build a small quiz app for students without learning to code, Codex fits. On the other hand, if you're a developer who needs granular control over every line of code in a large, complex codebase, Claude Code will deliver better output quality. Codex is the right tool for people who want fast results without needing to understand what's happening under the hood. One practical limitation worth knowing: Codex currently has full support for Python, JavaScript, TypeScript, and Ruby. For tasks that don't involve code — like generating images, automating workflows, or creating documents — this language limitation has no impact on you. The line between "can code" and "can't code" is fading The question "do you know how to program?" is losing its weight as tools like Codex continue to evolve. What matters more now is whether you can describe clearly what you want — because that's exactly the thinking skill required to work effectively with Codex and similar AI agent tools. If you want to try it today, start with something small and specific: ask Codex to create an Excel file consolidating data you currently process manually every week. That's the fastest test to evaluate whether Codex genuinely saves you time or not.

OpenAI has brought GPT-Live to ChatGPT Voice, turning voice conversations from a speak-then-wait exchange into a continuous stream of interaction. The model can listen while speaking, notice when a user wants to interrupt, wait while they think, and delegate difficult work to GPT-5.5 in the background. The result feels closer to a real conversation, although there are important limitations to understand before using it.How is GPT-Live different from earlier ChatGPT Voice?OpenAI introduced GPT-Live on July 8, 2026, as a new generation of voice models consisting of GPT-Live-1 and GPT-Live-1 mini. Both are rolling out inside ChatGPT rather than as standalone products with separate interfaces. Users simply open the familiar Voice button to receive the new experience once their account is updated.[VIDEO:EAN5Cj347PY|OpenAI introduces GPT-Live|Introducing the new ChatGPT Voice powered by GPT-Live]The biggest difference is the full-duplex architecture. Earlier cascaded voice systems had to convert speech into text, send the text to a language model, and then read the response through synthesized speech. That process introduced delay and could lose nuance. Advanced Voice Mode handled audio more directly, but conversations still operated in discrete turns: the AI generally waited for the user to stop completely before responding.GPT-Live processes input continuously while generating output. Many times per second, the model can decide whether to speak, keep listening, pause, accept an interruption, or call a tool. A short silence therefore does not necessarily mean the user has finished speaking.How does listening and speaking at once change a conversation?When both sides can react continuously, users no longer need to package every request into a complete turn. You can add context midway, ask the AI to slow down, or correct an assumption before the response ends. GPT-Live can also offer brief acknowledgements to show it is following along or remain quiet when asked to listen.Full duplex is also useful for live translation, language practice, and fast-moving exchanges. However, more natural interaction does not mean the AI understands every signal like a person. Regional accents, heavy background noise, unstable connectivity, or underspecified requests can still send a conversation in the wrong direction.What does using GPT-Live actually feel like?The clearest difference comes from conversational rhythm rather than one isolated feature. If you hesitate while remembering a number, GPT-Live is designed to wait instead of jumping in. If you change the question while it is explaining something, the model can stop and redirect more quickly. OpenAI also says it is better at focusing on the user’s voice when traffic or nearby conversations create background noise.More natural dialogue still needs a clear goalGPT-Live fits tasks such as planning while walking, practicing interviews, improving pronunciation, asking for help while cooking, or exploring an idea without typing. Users can start with an objective, add constraints while speaking, and ask the model to summarize decisions at the end.For a more reliable session, state the role and desired outcome. Instead of saying only “help me practice English,” ask GPT-Live to act as an interviewer, speak slowly, correct each answer, and provide feedback at the end. Continuous listening makes the exchange flexible, but a specific goal still determines output quality.Difficult work is delegated to GPT-5.5 in the backgroundGPT-Live separates immediate interaction from deeper reasoning. When a request requires web search, complex analysis, or multi-step processing, the voice model can delegate it to GPT-5.5 and bring the result back into the conversation. GPT-Live can keep talking and preserve context while that work runs instead of leaving the user in a long silence.At launch, Instant mode and GPT-Live-1 mini use GPT-5.5 Instant in the background, while Medium and High use GPT-5.5 Thinking with corresponding reasoning effort. Users can choose Instant for everyday questions or Medium and High when they want the model to spend more time on a difficult problem.Important: Delegating work to a stronger model does not eliminate mistakes. For important information, users should still verify sources, calculations, and conclusions rather than treating a spoken answer as the final result.What else does GPT-Live add?OpenAI remastered the nine voices available in ChatGPT for GPT-Live. The goal is not only clear pronunciation but also more natural pacing, expression, and short acknowledgements. Users still choose from predefined voices; GPT-Live is not designed to imitate a real person’s voice.During a conversation, ChatGPT can display rich visual cards for weather, stocks, sports, and other topics. Voice continues to work with search, memory, images, and file uploads. The experience is therefore no longer limited to audio: users can hear an explanation while viewing figures or details that need checking.Everyday work: ask quick questions, plan, create lists, and summarize decisions when typing is inconvenient.Learning: practice languages, simulate interviews, explain concepts, and test knowledge through conversation.Creative work: develop ideas, explore alternatives, and ask the AI to record the final direction.Search: ask follow-up questions while GPT-5.5 processes more complex information in the background.Two versions for two user groupsGPT-Live-1 becomes the default ChatGPT Voice model for Go, Plus, and Pro plans. Free users receive GPT-Live-1 mini. OpenAI is rolling out both versions across iOS, Android, and ChatGPT.com in stages, so some accounts may not see the change immediately.For developers, GPT-Live was not broadly available through the API at announcement time. OpenAI says API access will come later and is accepting notification sign-ups from developers and enterprises. GPT-Live is currently primarily a ChatGPT Voice experience rather than an immediate replacement for every voice agent built on the Realtime API.Which limitations are most noticeable?At launch, GPT-Live does not support voice together with video or screen sharing. Users who need those capabilities can switch to legacy Standard Voice or Advanced Voice Mode. This matters for remote-support workflows that depend on the AI seeing a camera feed or screen content.OpenAI also acknowledges that the model was initially optimized for some of ChatGPT’s most popular languages. In other languages, it may have a non-native accent or gaps in fluency. The Vietnamese experience may therefore vary with voice, speaking speed, environment, and rollout stage.Safety during continuous conversationVoice can feel more personal than text, making emotional reliance a more significant concern. OpenAI added evaluations for self-harm, psychosis and mania, violence, sexual content, and emotional attachment to AI. The system can steer a response, surface appropriate support, or end a conversation in higher-risk situations.The GPT-Live System Card also describes protections for teen users and parental controls. Even so, GPT-Live is not a medical professional or a replacement for human relationships. It should be treated as a support tool, with qualified help sought for sensitive issues.Does GPT-Live really change ChatGPT Voice?GPT-Live addresses the most frustrating parts of AI voice interaction: waiting for turns, being interrupted while thinking, and sitting through silence while the system handles difficult work. Full-duplex interaction combined with delegation to GPT-5.5 makes the experience fast at the conversational layer while retaining stronger intelligence for complex questions.Its greatest value may not be a voice that sounds more human, but the ability to maintain a workflow through conversation. Users can think aloud, revise requests while speaking, and receive both spoken responses and visual information. That opens the door to longer sessions for practice, idea development, and coordinating multiple tasks.GPT-Live is still in its first rollout stage. The API is not broadly available, language support is uneven, and video or screen sharing is temporarily absent. For hands-free conversation, practice, and continuous questions, it is a compelling upgrade. For work that requires screen observation, absolute accuracy, or immediate enterprise integration, users will still need to combine it with other modes and tools. If you need to build a voice application through the API today, consider gpt-realtime while waiting for GPT-Live developer access.

Is anyone else using Codex and Claude Code side by side? I only recently discovered the Codex plugin for Claude Code, published by OpenAI itself. The useful part is not simply having another AI available. It is being able to call Codex from the current Claude Code session for a code review, an adversarial design challenge, or a separate delegated task without constantly switching tabs and sessions. What makes the Codex plugin for Claude Code useful? The openai/codex-plugin-cc plugin is intended for developers who already work in Claude Code and want to add Codex to that workflow. Instead of allowing both agents to edit the same file at the same time, you can assign clear roles: Claude Code implements and Codex reviews, or Claude Code keeps the main thread while Codex investigates an independent problem in the background. The official plugin provides review commands such as /codex:review and /codex:adversarial-review, delegation through /codex:rescue, and job or session management through /codex:transfer, /codex:status, /codex:result, and /codex:cancel. Codex therefore becomes a collaborator inside the Claude Code workflow rather than a separate window. It does not create a separate Codex runtime The plugin uses the Codex CLI and Codex app server installed on the same machine. It also reuses the local authentication state, current repository checkout, and existing config.toml settings. Integration is straightforward, but each request still contributes to the user's Codex usage limits. Requirements before installation You need Node.js 18.18 or later and either a ChatGPT subscription, including Free, or an OpenAI API key. If Codex CLI is missing, /codex:setup can offer installation guidance. You can also install it manually with npm install -g @openai/codex and sign in with !codex login. How to install the Codex plugin in Claude Code Run these commands in Claude Code: /plugin marketplace add openai/codex-plugin-cc /plugin install codex@openai-codex /reload-plugins /codex:setup The last command checks whether Codex is installed and authenticated. Once setup is complete, the Codex slash commands should appear in Claude Code, along with the codex:codex-rescue agent under /agents. Try a background review first A low-risk first run is /codex:review --background. Use /codex:status to monitor it and /codex:result to retrieve the final review. Multi-file reviews can take time, so background mode keeps Claude Code available for other work. Three effective Codex and Claude Code workflows The value of the plugin comes from role design. If both agents modify the same area without boundaries, the result may be conflicting edits, repeated analysis, and wasted context. The following workflows make ownership clearer. Let Claude implement and Codex review After Claude Code completes a feature, run /codex:review for a read-only review. It can inspect current uncommitted changes or compare the branch against a base with /codex:review --base main. Because Codex does not edit files in this mode, the developer keeps control of what is accepted. For example, after Claude adds a payment flow across several modules, Codex can inspect logic errors, edge cases, and cross-file side effects. Claude Code can then evaluate the findings and apply only the changes that make sense. Delegate an entire task to Codex Use /codex:rescue for a problem that can be isolated, such as /codex:rescue --background investigate why the integration test is flaky. Claude Code can continue working on the interface or documentation while Codex investigates in the background. Rescue supports --background, --wait, --resume, and --fresh. Define the expected output and file scope before delegating. A vague instruction to fix everything while Claude Code is also editing the repository can still create collisions. A good task has a specific goal, completion criteria, and a clearly owned part of the codebase. Use adversarial review to challenge the project direction /codex:adversarial-review is designed to question implementation and design decisions rather than merely find bugs. For example, /codex:adversarial-review --base main challenge the caching and retry design asks Codex to inspect assumptions, trade-offs, alternatives, and risks such as data loss, race conditions, rollback, or reliability. This is where the two agents may appear to argue, but the debate only helps when a human sets a narrow question, requests evidence, and defines a decision rule. Otherwise, the review can become a chain of opinions with no practical outcome. Transfer sessions and manage background jobs /codex:transfer creates a persistent Codex thread from the current Claude Code session and prints a codex resume <session-id> command. It is useful when a discussion has grown beyond a short review and you want to continue directly in the Codex App or TUI without manually rewriting the context. Monitor, retrieve, and cancel work For background tasks, /codex:status shows progress, /codex:result returns the stored output and session ID, and /codex:cancel stops an active job. These commands prevent multi-agent work from becoming a black box. When a task drifts from its goal, canceling early is usually cheaper than waiting and starting over. Watch for review loops and usage limits Important: OpenAI explicitly warns that the optional review gate can create a long-running Claude/Codex loop and drain usage limits quickly. When enabled with /codex:setup --enable-review-gate, the plugin uses a Stop hook, which is an automated trigger that runs when Claude is about to finish its response, to start a targeted Codex review. If it finds an issue, Claude's response is blocked so Claude can address it first. This can be valuable before shipping, but it should not be left unattended. A practical safety checklist Assign roles before running: one agent implements while the other reviews, or each owns a separate task. Limit the scope by naming the branch, files, risk area, and completion criteria. Use background mode for large reviews and check progress periodically. Enable the review gate only while actively monitoring it, then disable it with /codex:setup --disable-review-gate. Do not let Claude review all Codex output and then ask Codex to review every Claude revision without a clear stopping rule. Use /codex:cancel when a task moves in the wrong direction. How can Codex and Claude Code work well together? The official OpenAI plugin offers a cleaner alternative to keeping Codex and Claude Code open in separate tabs or letting both agents edit the same file. Claude Code can remain the coordinator while Codex reviews, challenges a design, or owns a separate task. A sensible starting point is one small /codex:review --background run, followed by status, result, and cancel. Try rescue, transfer, and the review gate only after the basic workflow is familiar. The two systems can complement each other well, provided a person still sets the boundaries, budget, and stopping point.