Model details
GLM-5.1 is a flagship large language model designed with agentic engineering as its core purpose. Built on a 754-billion parameter architecture, it represents a deliberate shift away from models that burn bright on first attempts but then plateau. The design philosophy centers on sustained effectiveness across extended reasoning sessions, enabling the model to decompose complex software engineering problems, run iterative experiments, read and interpret results, and identify blockers with growing precision. Unlike its predecessors that tend to exhaust their initial repertoire quickly, GLM-5.1 is engineered to keep revising its approach and maintaining productive momentum over hundreds of reasoning rounds and thousands of tool invocations.
The model carries forward the GLM family lineage while marking a substantial leap in coding capability. It achieves state-of-the-art performance on SWE-Bench Pro, a benchmark for complex software engineering tasks, surpassing the results of GLM-5, GPT-5.4, and Gemini 3.1 Pro on this measure. On repository generation and real-world terminal tasks, GLM-5.1 leads its predecessor GLM-5 by a wide margin. The model is distributed under an MIT license as an open-weight release, making it accessible for both commercial and non-commercial applications. Its architecture supports adaptive problem-solving that can sustain autonomous execution for up to eight hours, positioning it as a practical choice for developers and enterprises seeking to automate extended engineering workflows without sacrificing quality or effectiveness.
Transparent token rates
Rates are shown per one million tokens. Combined means one million input plus one million output tokens.
Z.AI
2026 04 07 · Research GLM 5.1: Towards Long Horizon Tasks GLM 5.1 is our next generation flagship model for agentic engineering, with significantly stronger coding capabilities than its predecessor. It achieves state of the art performance on SWE Bench Pro and leads GLM 5 by a wide margin on NL2Repo (repo generation) a
Z.AI
Z.AI Releases GLM-5.1: A Next-Generation Agentic Model Built for Long-Horizon Engineering Tasks and Rewrites CUDA Kernels
Z.AI
Z.ai raises prices for its most advanced AI model, GLM-5.1, by at least 8% compared to GLM-5 Turbo, joining Alibaba and Tencent as demand for agentic AI surges. This signals vendor-level price pressure for advanced models and could increase deployment and inference costs for developers and enterprises.
Z.AI
Z.ai releases GLM-5.1, a 754B-parameter model (1.51TB on Hugging Face) distributed under an MIT license; it matches the parameter count of GLM-5 and is presented as aimed at long-horizon tasks.
Z.AI
According to a recent LinkedIn post from GMI Cloud, the company is highlighting what it describes as Day 0 support for the newly released GLM-5.1 model from Z.ai. T...
Z.AI
OpenRouter's GLM 5.1 listing gives developers concrete, current deployment data: release date April 7, 2026, 205K context, and a published list price of $0.9086/$2.856 per 1M tokens (a 35% discount shown alongside the standard $1.40/$4.40 Z.ai rate). The page confirms the model's core positioning—"can work independentl The same listing provides a developer-actionable snapshot of 16 hosting providers with input/output/cache-read pricing, P50 latency, throughput (tok/s), and uptime—e.g., Chutes at $0.98/$3.08 with 35 tok/s, DeepInfra at $1.05/$3.50, Crusoe with 0.55s latency at 40 tok/s, and Friendli at 0.64s/65 tok/s. Average throughp
Z.AI
Z.AI's official "New Released" developer documentation page establishes GLM-5.1's current status within the provider's release lineup as of late August 2026. The page lists GLM-5.1 (2026-04-07) as the foundation for long-horizon autonomous work—"designed for long-horizon tasks, GLM-5.1 can work independently for up to The same release-notes index is also the authoritative source for framing GLM-5.1 relative to its successors: GLM-5.2 (2026-06-16, 1M context), GLM-5.3 (2026-08-18, +50% on Z.ai Code Bench, SOTA on Terminal Bench 3.0), and GLM-5.3-Flash (2026-08-26, native vision, 320B/18B hybrid attention). For a developer choosing or
