xianyu110/gpt-codex: a Chinese-language Codex tutorial site, read critically

What xianyu110/gpt-codex actually is, and who it is written for

The repository is a tutorial website, not a Codex distribution. Its top level contains index.html, a docs/ directory, an en/ directory, recipes/, reference/, light/, _config.yml, CNAME and a favicon, which is the shape of a statically generated site with a custom domain rather than an application. The README opens by describing itself as the most detailed Codex tutorial on the web for beginners, moving from zero to one, and it is written in Simplified Chinese for readers who have seen Codex mentioned but have not used it.

The audience is explicit. The README asks the reader whether they cannot write code and whether Codex is still usable for them, and answers that for beginners the value may be larger than for programmers. That framing matters when you decide whether to clone this. It is an orientation document, and the primary language listed for the repository is HTML, which confirms where the effort went. The homepage is codex.maynorai.top. The topics list includes claude, claudecode, codex, gemin, gpt and grok, so the site is broader than one product, but Codex is the subject of the README.

The two claims the README builds everything on

The first claim is that Codex is not merely an AI that writes code but an agent that keeps working around a task. The README states that Codex is OpenAI's answer to Claude Code, and its comparison table sets OpenAI against Anthropic, GPT-5.6 Sol / Terra / Luna against Claude Opus 4.6, and describes both as coding agents, with Codex trending toward a general agent. The second claim is that the underlying model changed the character of the tool: the README says GPT-5.6 Sol / Terra / Luna is the current flagship, that it entered ChatGPT and Codex, and that the earlier GPT-5.3-codex was more narrowly programming-focused. It describes the newer model as planning, executing and checking, and it uses the metaphor of a code worker becoming an engineering lead.

There is a concrete detail worth extracting from that section. The README states that the Codex context window extends to 400K tokens, and that a Fast mode runs at roughly 1.5 times the speed at roughly 2.5 times the token cost. Those two numbers are the most operational facts in the whole document, because they describe a trade-off a user can actually feel. The README cites OpenAI's announcement page and API documentation as sources for the model claims, and it dates the release to 2026-04-23. Treat the dates and model names as claims from this repository, not as something confirmed here.

How the site is put together and how a reader moves through it

The repository layout suggests a documentation pipeline rather than a hand-written page. There is a url_mapping.json and an upload_images.py at the top level, plus an en/ directory alongside the Chinese content. The README states that all images from the original are preserved rather than omitted, that key information is bolded, and that content is converted into tables where possible. That is a deliberate editorial policy, and it explains why the README reads as a long structured article with many comparison tables.

The site also functions as a hub. The README lists sibling repositories by the same author: awesome-openclaw-tutorial, clawbot, gemini-nanobanana-pro, awesome-chatgpt-project, awesome-claudcode-tutorial, awesome-gptimage2 and CodexPlusPlus. One of those, CodexPlusPlus, is described as an external launcher for the Codex app that injects enhancements through the DevTools protocol without modifying the original installation files. If you came here looking for something to install and run against Codex, that sibling is closer to what you want than this repository is. This repository points at it.

Reading the tutorial without a build step

There is no package manager, no build step and no server-side component documented in the README. The repository is static HTML with a Jekyll-style _config.yml, and the README does not document a local preview command, so there is no project-specific command to copy here. What the repository does give you is the prose itself, in plain files: README.md at the top level and codex.md alongside it. Opening README.md in an editor shows the beginner framing and the comparison tables that the site is built from, starting with a heading dated 2026-04-27 and authored by MaynorAI, followed by the four questions the document promises to answer. The README does not document a hosted English build, so the en/ directory is where to look for what an English version contains. If you want the site rather than the text, the README points to the homepage codex.maynorai.top, and the repository ships no instructions beyond that.

Where this repository is the wrong tool

The most important limitation is categorical: this is not Codex. Nothing here installs the agent, configures a model, or runs a task. If you searched for a Codex download and landed on this repository, you have found a page about the download, not the download. The README itself points readers to chatgpt.com/codex and describes clicking a download button, and it mentions a Codex Desktop App, but the repository ships no binary and the README does not document a CLI install procedure.

The second limitation is that a large part of the access guidance is commercial and third-party. The README lists a paid subscription link, a third-party relay route, a one-month bundle, and a cc-switch route that it describes as a third-party relay with a daily quota, explicitly noting that these are not official OpenAI subscriptions. It says the advantage is not needing your own network workaround and the fit is people who want to get running quickly. That is a real trade-off stated plainly, and it is also the part of the document you should trust least, because product links and quotas change without any commit to this repository.

The third limitation is entitlement. The README's own membership table says Plus, Pro, Business and Enterprise can use the model in ChatGPT and Codex, that Edu and Go appear in the official list, and that free users are not clearly included, with a warning that model access varies by region, account, plan and rollout pace. It tells the reader to check the official page and their own account model list. That is an honest hedge, and it means no table in this repository can settle whether you personally have access.

How it differs from the alternatives people compare it to

The comparison the README makes is Codex against Claude Code, and the difference it draws is one of model lineage and emphasis rather than workflow. It assigns OpenAI and Anthropic respectively, GPT-5.6 Sol / Terra / Luna against Claude Opus 4.6, and says Claude Code's general capability and front-end aesthetic are usually stronger while Codex leans toward reasoning plus execution. That is the repository's opinion, presented in a table, and it is the kind of claim you would want to test yourself rather than accept.

The more useful alternative is not a competing agent but a competing document type. Official OpenAI documentation and API model pages are the source the README itself cites, and they carry no relay links and no purchase routes. A Chinese-language community tutorial such as this one wins on readability, on the beginner framing, and on the sibling-repository ecosystem around it, which includes a Claude Code tutorial and an OpenClaw tutorial in the same style. It loses on verifiability. If your question is what the model is called today and what your account can call, the official pages answer it; if your question is how to think about the tool at all, this repository is the faster read.

Maintenance, licence and what upgrading costs you

The last push to the default branch was on 2026-08-20, which is recent enough that the site is not abandoned, but the README's own update stamp is 2026-04-27, so the prose and the commits are four months apart. There are no releases retrieved for this repository, which is consistent with a documentation site that has no versioned artifact to ship. Upgrading is therefore a git pull, and the cost of an upgrade is re-reading the sections that changed rather than resolving dependency conflicts.

The maintenance risk is content drift, not code rot. The README makes model claims tied to a specific announcement date and routes access through third-party shops and relay quotas. Those are the parts most likely to go stale between commits, and the README acknowledges this by telling readers to defer to official pages and their account model list. A repository that must instruct readers not to trust its own tables is telling you how to use it.

The licence is not stated in the repository metadata. That has practical consequences: you cannot assume you may republish the Chinese text, the images the README says it preserves in full, or the tables. If you intend to reuse any of it, establish the licence from the repository itself before you do, and treat the image hosting domain referenced throughout the README as a separate rights question from the text. This is a description of the gap, not legal advice.

Editorial conclusion

Adopt this repository as a reading list, not as software. It fits a developer in mainland China who wants a Chinese-language orientation to Codex, plus the repository's own positioning of the tool against Claude Code and the model naming it uses. It does not fit anyone who wants the Codex client itself, a reproducible install, or a vetted answer on account entitlements, because the README routes several of those questions to third-party shops and says entitlement varies by region, account, plan and rollout. Before relying on anything, verify the model names against OpenAI's own model pages and your account's model list, and confirm the licence, which the repository does not state.

Frequently asked questions

Official sources

1. Issues
2. Project website
3. README
4. xianyu110/gpt-codex on GitHub