AI Frameworks · zai-org

GLM-4.5

GLM-4.5 is a 355B-parameter foundation model series (with compact 106B Air variant) designed for agentic AI applications, featuring hybrid reasoning modes for complex tasks and tool use. The repository provides open-source base, reasoning, and quantized FP8 variants under MIT license, with newer GLM-4.6 and GLM-4.7 releases adding improved coding, reasoning, and extended context windows.

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Source: GitHub — github.com/zai-org/GLM-4.5

4.4k

GitHub stars

465

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	zai-org/GLM-4.5
Owner	zai-org
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	4.4k
Forks	465
Open issues	27
Latest release	Unknown
Last updated	2026-02-01
Source	https://github.com/zai-org/GLM-4.5

What GLM-4.5 is

Mixture-of-Experts (MoE) language model with 32B active parameters (GLM-4.5) or 12B (Air), supporting interleaved and preserved thinking modes for multi-turn reasoning and agent orchestration. Supports BF16 and FP8 precision, context windows of 128K–200K tokens (depending on version), and integrates with transformers, vLLM, and SGLang inference frameworks.

Quickstart

Get the GLM-4.5 source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/zai-org/GLM-4.5.git2cd GLM-4.53# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Agentic Coding & Software Engineering

GLM-4.7 scores 73.8% on SWE-bench and 66.7% on SWE-bench Multilingual. Suitable for code generation, debugging, and integration with IDE-based agent frameworks (Claude Code, Cline, Roo Code, Kilo Code).

Complex Reasoning & Mathematical Problem-Solving

GLM-4.7 achieves 42.8% on Humanity's Last Exam. Interleaved and preserved thinking modes enable step-by-step reasoning for long-horizon tasks with reduced re-derivation overhead.

Multi-Turn Tool-Using Agents

Extended context (200K tokens in GLM-4.6+) and hybrid reasoning support multi-step agentic workflows. Turn-level thinking control allows per-request latency/accuracy trade-offs.

Implementation considerations

Quantization (FP8) reduces memory footprint (~50%) but introduces precision trade-off; benchmark on target hardware before deployment.
Thinking modes (interleaved, preserved, turn-level) must be explicitly managed in application logic; API and integration frameworks differ in support.
Context window (128K–200K) requires attention to prompt engineering; longer contexts reduce per-token inference throughput.
MoE routing and sparse activation introduce variable latency; batch processing and infrastructure tuning are critical for stable SLAs.
Model code is distributed across transformers, vLLM, and SGLang; use version-compatible inference stacks to avoid compatibility drift.

When to avoid it — and what to weigh

Real-time, ultra-low-latency requirements — Thinking modes incur computational overhead. High-latency inference is inherent to interleaved reasoning. Non-thinking mode available but may reduce accuracy on complex tasks.
Proprietary deployment without internet access — While base models are open-source, inference requires substantial GPU resources (355B model needs multiple high-end GPUs or quantization). Operational complexity is high for on-premise setups.
Production use without capability benchmarking — Benchmarks reflect third-party datasets (SWE-bench, HLE). Real-world performance on custom tasks, edge cases, or domain-specific code patterns requires validation before production deployment.
Strict license enforcement in derivative commercial products — MIT license permits commercial use and modification, but GPL-incompatible dependencies or downstream legal requirements should be reviewed per your use case.

License & commercial use

GLM-4.5, GLM-4.5-Air, and base model variants released under MIT license. GLM-4.6 and GLM-4.7 license not explicitly stated in provided data; requires review of respective model cards on Hugging Face / ModelScope.

MIT license (confirmed for GLM-4.5 series) permits commercial use, modification, and redistribution with attribution. GLM-4.6 and GLM-4.7 license terms must be verified on official model repositories. Consult legal counsel if bundling with GPL or AGPL dependencies, or if repackaging as proprietary service.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Strong
Assessment confidence	High

Security considerations

No security audit, CVE history, or adversarial robustness data provided. Open-source weights are publicly available; supply-chain trust depends on Hugging Face / ModelScope repository integrity. Inference infrastructure security is operator's responsibility. Tool-calling in agentic deployments introduces risk surface (prompt injection, unauthorized function invocation); validate and sandbox tool definitions.

Alternatives to consider

DeepSeek-V3.1-Terminus

Competing domestic LLM with reasoning capabilities; GLM-4.6 claims competitive advantage but direct head-to-head benchmarks not provided in DATA.

Claude Sonnet 4 (Anthropic)

Proprietary agentic model; GLM-4.6 claims parity on benchmarks. Offers managed API with native thinking mode but higher per-token cost and closed-source.

Llama 3.1 405B (Meta)

Open-source, larger model; less specialized for reasoning/agentic tasks but broader community support and lower operational friction on standard LLM workloads.

Software development agency

Build on GLM-4.5 with DEV.co software developers

Evaluate GLM-4.5, GLM-4.6, or GLM-4.7 for your agentic AI use case. Start with the Hugging Face model cards, benchmark on your workloads, and consult our team on deployment strategy (self-hosted vs. managed API).

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Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

GLM-4.5 GLM-4.5-Air-FP8 GLM-4.5-Air

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GLM-4.5 FAQ

What is the difference between GLM-4.5, GLM-4.6, and GLM-4.7?

GLM-4.5 is the base release (355B params, 128K context). GLM-4.6 expands context to 200K and improves coding/reasoning. GLM-4.7 adds turn-level thinking control, further boosts SWE-bench performance (+5.8%), and introduces GLM-4.7-Flash (30B lightweight variant). All share the same architecture but with incremental capability gains.

Can I use these models commercially?

GLM-4.5 series are released under MIT license, permitting commercial use and modification. GLM-4.6 and GLM-4.7 license terms must be confirmed on their official model repository pages. Always review dependent libraries for license compatibility.

What is 'thinking mode' and when should I use it?

Thinking mode enables interleaved reasoning (model thinks before each response), preserved thinking (reasoning retained across turns), and turn-level control. Use for complex reasoning, multi-step coding, or math; disable for lightweight requests to reduce latency and cost. Non-thinking mode remains available for immediate responses.

Do I need GPU to run these models?

Yes. GLM-4.5 (355B) requires multiple high-end GPUs or quantization (FP8). GLM-4.7-Flash (30B) has lower requirements. Alternatively, use Z.ai's managed API to avoid infrastructure setup.

Custom software development services

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