In this episode I sit down with Amir to get tactical about running local AI models as part of a daily workflow. We center on GLM 5.2 from ZAI, how it stacks up against frontier models like Opus 4.8, and how a fusion approach lets you sequence a heavy thinking model with a lighter execution model for the best output at the lowest cost. Amir walks through setup in Cursor and Codex via OpenRouter, shares real token-cost math, and demos GLM 5.2 refining a live app. By the end you will know how to start today, where local models shine, and how model chaining keeps spend in check.

Timestamps

00:00 – Intro

02:09 – GLM 5.2 and Z AI

04:01 – Specs: 1M context and Terminal Bench 2.1

05:22 – Making sense of benchmark scores

06:42 – Setup in Cursor or Codex with OpenRouter

10:18 – Local model upside: buy a machine, run tasks

11:42 – Token cost: 44 cents versus $2.38

13:36 – Future-proofing with an upfront hardware bet & The Uber subsidy analogy

16:49 – Model chaining and the vision workaround

19:23 – Token maxing vs routing tasks to the right model

20:54 – Answering the "cost is irrelevant" crowd

21:59 – Closing thoughts

Key Points

• GLM 5.2 ships with a 1M-token context window and scores 81 on Terminal Bench 2.1, landing about four points behind Opus 4.8.
• A fusion approach (a term OpenRouter coined) sequences models: plan with Opus, execute with GLM 5.2, review with Composer 2.5 or Codex 5.5.
• Running GLM 5.2 in the cloud through OpenRouter costs roughly 44 cents for a task that runs about $2.38 on Opus 4.8 — close to a 5X saving.
• You can start today with credit-based access: load $20 in OpenRouter and route tasks to the right model.
• For images, Amir uses Opus 4.8 to read screenshots and describe them, then hands the layout to GLM 5.2 to act on.
• Teams are shifting from token-maxing to output-maxing, making model governance and chaining the smart play

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