Let's Run Local AI GLM 4.7 - #1 Open Coding Model | Developer Review

Let's Run Local AI GLM 4.7 - #1 Open Coding Model | Developer Review

by xCreate

Formal & Physical Sciences AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence
🎙 xCreate 👥 26K 📅 December 23, 2025 ⏱ 16 min 👁 9K 📄 news review 🧭 2026-09-09
Available in: English (current) Français

Keywords

GLM 4.7local AIcoding performancetool callingreasoning

Summary

xCreate reviews GLM 4.7, an MIT-licensed open-weight coding model from Zhipu, running it locally on a Mac Studio with 512GB RAM. The video begins with an overview of benchmark improvements, showing that GLM 4.7 outperforms Claude and GPT on certain coding and tool-calling tests. The host then runs the model using the Inferencer app, testing both thinking-enabled and disabled modes. He observes that with thinking enabled, the model initially responds in Chinese but can be forced to English, and even token negation works. For a reax-specific coding challenge, thinking mode takes 6,000 tokens and 480 seconds but gets the correct answer, while a two-shot prompt with thinking disabled achieves the result in only 1,000 tokens. A classic surgeon riddle is solved correctly with thinking, but fails without it (answers ‘mother’), highlighting limitations in reasoning-only mode. Batching allows running multiple prompts simultaneously, reducing per-generation speed but increasing throughput. Both thinking and non-thinking modes generate functional Photoshop-like clones, though the thinking version includes more menu options. Tool calling is tested by fetching web pages; the model successfully retrieves and summarizes content, even calling the tool twice to find ant lifespan. The host concludes that thinking mode is not always beneficial for coding tasks, and notes the model’s MIT license and potential for commercial use. The video is informative for developers considering local deployment of GLM 4.7, though subjective in nature.

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Critical Evaluation

Value of the Information & Strength of the Argument

The value lies in the hands-on testing of a newly released model, providing concrete performance metrics (tokens, speed, memory) across different modes and tasks. The argumentation is based on direct observations rather than abstract claims, which is commendable. However, the tests are not rigorously controlled: no repeated runs to establish variance, no comparison with other models under identical conditions beyond referencing previous videos, and the conclusions about ’thinking’ quality are drawn from a single example of reasoning. The author does present a balanced view, noting that thinking disabled can be more efficient for coding, but the evidence is anecdotal. The reasoning puzzle demonstrates a clear failure mode, but the interpretation that ’these databases aren’t actually thinking’ is a simplification of LLM mechanics.

Scientific Rigor, Source Quality, Title Accuracy

Scientific rigor is moderate: the video uses real-time testing with visible outputs, but lacks formal benchmarking protocols and reproducibility details. Sources cited include the Hugging Face model page (inferential, with MLX quantized weights), the Inferencer app, and related videos. The title accurately reflects the content, though the ‘#1 Open Coding Model’ claim is based on benchmarks that are not deeply examined. No formal citations or academic references are provided; the video relies on empirical experience. The adequacy between title and content is high, with no misleading elements. There is no discussion of audience comments, as none were provided.

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Title / Content Match

The title promises running local AI GLM 4.7 with a developer review; the video delivers exactly that, focusing on coding performance and model behavior.

Quality & Reliability

6/10

Subjective hands-on testing without rigorous methodology, but provides concrete metrics (tokens, time, memory usage) and transparent comparisons across modes.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

  • GLM-4.7-MLX-6.5bit on Hugging Face — Model file referenced for local run and quantized weights
  • Inferencer App — The application used for running and controlling the model locally
  • Mac Studio Review video — Companion video reviewing the hardware used in this test

External References

Contribution & Novelties

The video contributes a practical, developer-oriented evaluation of GLM 4.7 in a local environment, highlighting its coding strengths and reasoning weaknesses. It offers insights into the impact of thinking mode on different tasks, showing that disabling thinking can be more efficient for straightforward coding challenges. The demonstration of token negation and batching is a novel feature for the Inferencer application. However, the findings are based on single runs, and no comparative analysis with other models is performed in the video, limiting its generalizability.

Pour aller plus loin :

  • Large Language Model (Wikipedia) — Foundational concept for understanding how models like GLM 4.7 work.
  • Inference in Machine Learning (Wikipedia) — Background on inference methods, relevant to token generation and batching.
  • GLM Models (Zhipu AI) — Official page for the GLM model family, providing technical details and updates.
  • Ollama — A popular tool for running local LLMs, useful for comparing deployments.

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Radar Profile

The profile shows high scores in technical level and information quantity, reflecting a hands-on demonstration with realistic metrics. Quality of information is adequate but limited by single-run subjective tests. Reliability is lower, as the video relies on anecdotal evidence and lacks statistical rigor. The overall shape suggests a useful but not definitive evaluation.

Reliability 6/10