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GLM-5.1 pushes open weights toward eight-hour autonomous runs

Source
x.com
Date
Z.ai@Zai_org
Thread · 5 parts

Introducing GLM-5.1: The Next Level of Open Source - Top-Tier Performance: #1 in open source and #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo. - Built for Long-Horizon Tasks: Runs autonomously for 8 hours, refining strategies through thousands of iterations. Blog: https://t.co/hmyDe4Nel3 Weights: https://t.co/CuUjXcPKJD API: https://t.co/fz6reja4fb Coding Plan: https://t.co/Nk8Y98HNhU Coming to https://t.co/WCqWT0qCQb in the next few days.

SOTA on SWE-Bench Pro (58.4): GLM-5.1 delivers significant leaps in coding and agentic performance.

Building a Linux Desktop from Scratch Using a self-review loop, GLM-5.1 spent 8 hours autonomously refining features, styling, and interactions to build a functional desktop environment.

Vector-DB-Bench: 6x Performance Boost In high-performance database optimization, GLM-5.1 reached 21.5k QPS over 600+ iterations and 6,000+ tool calls. This is 6x the performance of a standard 50-turn session.

Read the full thread on X

Context

Z.ai's GLM-5.1 release highlights a coding model that can keep revising its approach over extended runs. In a reported database-optimization experiment, the model completed more than 600 iterations and 6,000 tool calls, producing a system that handled 21,500 queries per second. The release thread says that was six times the performance reached in a standard 50-turn session.

The comparison concerns the speed of the database being optimized, rather than the model's own response speed. The model card describes the working loop as running experiments, reading results and revising strategies. The reported benefit comes from using additional runtime for repeated optimization of the same task.

Terms in this piece · Glossary
  • SWE-bench — The standard benchmark for AI coding agents: real GitHub issues from real repositories, scored by whether the agent's patch passes the project's own tests.
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