Creators have compared Codex and GLM directly in 3 videos. Codex leans positive across 33 videos; GLM is more positive across 12 videos.
| Date | Channel | Video |
|---|---|---|
| 23 Jun 2026 | Greg Isenberg | GLM 5.2 Clearly Explained (and how to set it up) |
| 21 Jun 2026 | Riley Brown | AI Agents Just Changed Forever: GLM 5.2, Codex Skills, Claude & Cursor |
| 19 Jun 2026 | Matt Wolfe | AI News: Fable Banned, New Open-Source Leader, Midjourney Shocker |
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Try it freeCreators consistently frame Codex as a purpose-built agentic platform capable of running unattended for days at a stretch. Matthew Berman documented six- and seven-day autonomous Codex loops that produced a functional Minecraft clone and an Excel clone, noting that GPT-5.6 inside Codex takes "the most direct path to a solution" and uses fewer tokens per task than competing models. Riley Brown similarly highlighted Codex's computer-use capability running silently in the background — unlike Claude Code, which takes over the user's screen — making it, in his view, far more practical for automated QA and long-running loops. AI Jason's month-long loop experiments confirmed that both Claude Code and Codex support a continuous "for/goal" trigger mode, but his practical blueprint focused heavily on Codex's ability to participate in orchestrator-executor-verifier pipelines with isolated worktrees.
GLM 5.2, by contrast, is not itself an agentic platform but a model that creators plug into existing agentic harnesses. Jack Roberts tested GLM 5.2 inside his Hermes agent system and found it competitive with Claude Opus 4.8 on website creation and code improvement tasks, though it required a third retry on a tool-calling task (retrieving an Outlook email via Zapier MCP), raising what he called "robustness concerns". IndyDevDan noted that GLM 5.2 spends most of its output tokens on reasoning, meaning raw tokens-per-second figures are misleading and total wall-clock response time is what matters for agent pipelines. The consensus across co-mention sources is that Codex provides the agentic scaffolding while GLM 5.2 is best understood as a cost-effective execution model dropped into that scaffolding.
The pricing conversation in the corpus is sharply asymmetric: Codex is discussed in terms of the cost of running its host models (GPT-5.6 at $5 per million input tokens and $30 per million output tokens), whereas GLM 5.2 is almost universally framed as a cost-cutting substitute for frontier models. Greg Isenberg and guest Amir calculated that a typical 50k input / 85k output token job costs roughly 44 cents via GLM 5.2 on OpenRouter versus $2.38 for Claude Opus 4.8 — approximately a fivefold saving. Jack Roberts put the comparison even more starkly, noting that Claude Fable 5 costs around $45 per 2 million tokens in and 500k out, versus roughly $3.36 for GLM 5.2, while Matt Wolfe reported that major companies including Coinbase have already switched to GLM 5.2 primarily because of cost and controllability.
Creators who discuss both tools directly tend to recommend a hybrid workflow rather than choosing one over the other on price alone. Greg Isenberg's recommended pattern — plan with a frontier model, execute with GLM 5.2, then review with Codex or Gemini 2.5 — treats Codex as the quality-review layer and GLM 5.2 as the high-volume execution layer. Matthew Berman reinforced this, noting that Codex (unlike third-party tools such as Cursor) has no built-in incentive to auto-route cheaper sub-tasks, which means users must manually adopt a routing strategy to capture GLM 5.2's cost advantages within a Codex workflow. The practical upshot, several creators suggest, is that GLM 5.2's pricing advantage is most realised when it is wired into a Codex or similar orchestration loop rather than used as a standalone replacement.
Codex occupies a distinct position as a first-party OpenAI platform with its own hosted interface, browser control, and a Sites feature (previously teams-only, now available to all users) that Riley Brown described as turning Codex into a "full vibe coding platform with in-app browser support". Matthew Berman demonstrated Codex's browser control being used to automate DNS migrations across Vercel, DigitalOcean, and GoDaddy, and to auto-scale a Supabase instance — tasks that go well beyond code editing. The recently announced merger of ChatGPT and Codex into one platform further deepens this integration, with Ross Mike noting in Riley Brown's breakdown that the combined app supports multiple parallel threads and computer-use that runs without interrupting the user's screen.
GLM 5.2 has no native IDE or platform of its own; integration is achieved entirely through third-party tooling. Both Riley Brown and Greg Isenberg walked through near-identical three-to-five-minute setup processes for adding GLM 5.2 to Cursor via OpenRouter, and Greg Isenberg's guest Amir explained that in Codex itself GLM 5.2 can be used by supplying an OpenRouter key and creating a custom profile. Matt Wolfe reported using GLM 5.2 inside Cursor as an agent harness to build a functional 3D game clone and a Chrome extension in very few prompts. Creators do not report any friction-free native integration for GLM 5.2 — every workflow requires an API-key override or a custom model entry — whereas Codex's own models are available out of the box with no configuration overhead.
Reliability assessments of Codex in the corpus are generally positive, with creators reporting successful multi-day autonomous runs and consistent computer-use execution. Dan Shipper described using Codex with GPT-5.6 as his "primary operating system for all knowledge work" and found it more practical than Claude Opus 4 for everyday use. The Creator Magic channel's Tank framework, which orchestrates Codex alongside Claude Code and Grok, treats Codex as a dependably stable agent session that can be scheduled and queued without manual babysitting. The record-and-replay feature — converting a screen recording of up to 30 minutes into a reusable slash-command skill — was highlighted by both Riley Brown and Matt Wolfe as a reliability-enhancing innovation, since it codifies human-demonstrated workflows rather than relying on the model to infer intent.
GLM 5.2's reliability picture is more mixed across the corpus. The Creator Magic channel ran a direct head-to-head comparison of GLM 5.2 against Claude Opus 4.8 across three browser game builds: GLM 5.2 failed the first task entirely due to an "image input not supported" API error, tied on the second, and lost on the third. Jack Roberts noted that in a tool-calling test GLM 5.2 failed on initial attempts and required a third retry, which he flagged as a robustness issue. Greg Isenberg's guest acknowledged that GLM 5.2 currently lacks vision and image capabilities, requiring a workaround where a frontier model describes screenshots in text before GLM 5.2 can act on them. Creators who are enthusiastic about GLM 5.2 tend to scope its use to tasks where its limitations do not surface — high-volume text and code execution — rather than presenting it as a universally reliable drop-in.
Creators discussing Codex in the context of GPT-5.6 note that the underlying models carry a 1.5 million token context window, which WorldofAI reported as part of the GPT-5.6 Soul, Terra, and Luna specifications. This extended context is presented as enabling the kind of long-horizon, week-long agentic runs that Matthew Berman and Dan Shipper documented — where the model needs to hold a large codebase in context without losing coherence across days of unattended operation. The Codex platform itself adds Codex Chronicle (a local screenshot feed described by Dan Shipper), which gives the model continuous context about what the user is doing on-screen, further augmenting effective context beyond raw token limits.
GLM 5.2 is also noted by multiple creators for its large context window — Matt Wolfe and Greg Isenberg both cite one million tokens with a 128k maximum output. IndyDevDan highlighted that GLM 5.2 is a 753-billion-parameter model whose reasoning-heavy output behaviour means that context is consumed quickly during complex tasks, and he cautioned that wall-clock response time rather than raw speed is the meaningful metric. WorldofAI's benchmark comparison placed GLM 5.2 as the current state-of-the-art among open-weight models on SWE-bench Pro, ahead of Tencent HY3 and DeepSeek V4 Pro, suggesting its architecture punches above its open-source weight class. The key distinction creators draw is that GLM 5.2's context capabilities are delivered through an open-weight model accessible via API or (with significant hardware) locally, whereas Codex's context advantages are tied to a closed, hosted platform that cannot be self-hosted or operated behind a corporate firewall.
Several creators suggest the two tools serve different roles rather than competing directly. Codex is described by Riley Brown and Matthew Berman as a full agentic platform with background computer-use, multi-day autonomous loops, and built-in browser control, while GLM 5.2 is an open-weight model that must be plugged into an existing harness such as Cursor or Codex itself via OpenRouter. For long-running, unattended agentic coding tasks, creators like Dan Shipper and Matthew Berman report Codex as their primary environment; GLM 5.2 is more often discussed as the cost-efficient execution model inside that environment rather than a competing platform.
Yes, and creators from two co-mention sources explain precisely how. Greg Isenberg's guest Amir described supplying an OpenRouter API key and creating a custom model profile inside Codex to point it at GLM 5.2, while Riley Brown walked through a similar process. The recommended workflow in both videos is to use a more powerful frontier model for planning and then route execution tasks to GLM 5.2 to reduce token costs, with Codex or Gemini used for final review.
Creators frame the comparison as roughly five- to sixfold cheaper per token for GLM 5.2 versus frontier models. Greg Isenberg and guest Amir calculated that a typical job costs around 44 cents via GLM 5.2 on OpenRouter versus $2.38 for Claude Opus, and Riley Brown described GLM 5.2 as roughly five to six times cheaper than GPT-5.5. GPT-5.6 inside Codex is priced at $5 per million input and $30 per million output tokens, though Matthew Berman noted that Soul uses fewer tokens per task than previous models, partially narrowing the real cost gap.
Creators note a significant gap here. Greg Isenberg's guest Amir confirmed that GLM 5.2 currently lacks vision and image input capabilities, and the Creator Magic channel documented a task failure caused by an "image input not supported" API error when GLM 5.2 was asked to process visual input. The suggested workaround is to use a frontier model to convert screenshots into text descriptions, then pass those descriptions to GLM 5.2. Codex, running on GPT-5.6, does not face this limitation and was demonstrated by Matthew Berman using computer-use to visually reference and interact with applications such as Excel and browser interfaces.
Creators report diverging enterprise patterns. Matt Wolfe noted that Coinbase has adopted GLM 5.2 as part of a cost-reduction strategy, citing cheaper pricing, greater controllability, and immunity to US government model bans as drivers. Greg Isenberg observed that companies are hitting token budget limits and beginning governance conversations about which models employees should use for which tasks, making GLM 5.2 an attractive execution-layer option. Codex, however, is described by Dan Shipper and Riley Brown as the platform of choice for engineering teams building agentic workflows natively on OpenAI infrastructure, particularly where browser control, the Sites feature, and tight GPT-5.6 integration matter more than raw token cost.
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