zai-org / GLM-5

What is GLM-5?

GLM-5 delivers a 744B-parameter open-source LLM tuned for agentic engineering, shifting from vibe coding to structured long-horizon tasks like systems building and resource planning. It solves scaling pains in complex coding and reasoning by packing 28.5T tokens of training plus RL boosts via slime infrastructure, deployable locally with vLLM or SGLang on multi-GPU setups. Users grab BF16/FP8 weights from Hugging Face or ModelScope and serve via simple Docker or pip commands for API access.

Why is it gaining traction?

GLM-5 crushes open-source benchmarks in coding, reasoning, and agentics—topping Vending Bench 2 with $4,432 simulated profits over a year, outpacing GLM-50-22, GLM-50-23G, GLM-50-25G, and even nearing Claude. DeepSeek Sparse Attention slashes inference costs without losing context, and tool-calling/reasoning parsers hook right into vLLM for agent workflows. Devs chase its edge over GLM-50-27, GLM-50-27C, GLM-50-27CG, GLM-50C, and GLM-592LI in real-world engineering benches like CC-Bench-V2.

Who should use this?

Backend engineers tackling multi-step pipelines or frontend devs automating form-heavy systems. Agent builders running long-term sims, like vending ops or planning horizons. Teams upgrading from GLM-4.x for production-grade coding assistance.

Verdict

At 27 stars and 1.0% credibility, GLM-5 is raw—strong deployment docs but zero tests or examples beyond README recipes signal early immaturity. Grab it if you pack Hopper/Blackwell GPUs for agentic prototypes; skip for now if stability matters.

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