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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.
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.
Get the GLM-4.5 source
Clone the repository and explore it locally.
git clone https://github.com/zai-org/GLM-4.5.gitcd GLM-4.5# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
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 |
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.
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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GLM-4.5 FAQ
What is the difference between GLM-4.5, GLM-4.6, and GLM-4.7?
Can I use these models commercially?
What is 'thinking mode' and when should I use it?
Do I need GPU to run these models?
Custom software development services
Need help beyond evaluating GLM-4.5? DEV.co is a software development agency building custom software, web applications, APIs, and ai frameworks integrations — and maintaining them long-term.
Ready to Build with GLM-4.5?
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).