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What Is a GLM Model? The Open GLM LLM Family Explained

A GLM model is a member of an open-weight large language model family whose current flagship is GLM-5.2. If you have arrived here from a search for "what is GLM", the short answer is this: GLM is a line of general-purpose language models released with published weights under an MIT license, spanning lightweight 30B releases through to a 1M-token-context flagship built for long-horizon agentic engineering.

What distinguishes a GLM LLM from most models you can name is the combination of two things that rarely appear together. It competes on the same benchmarks as the closed frontier — 81.0 on Terminal-Bench 2.1, 74.4 on FrontierSWE Dominance, 99.2 on AIME 2026, all as published by the model authors — and yet the weights are downloadable, self-hostable and permissively licensed. That combination is what this site exists to document.

What a GLM AI Model Actually Is

A GLM model is a transformer-based large language model: it takes text, predicts text, and has been post-trained to follow instructions, use tools and work inside agent harnesses. Functionally it occupies the same slot in your architecture as any other general-purpose LLM — you send it a prompt and it streams back a completion. The interesting differences are architectural and legal rather than conceptual.

Architecturally, the current generation uses sparse attention with a shared indexer, a design the model authors call IndexShare, so that per-token computation does not scale with context the way dense attention would. That is what makes the flagship's 1M-token window practical to serve rather than merely declarable. Legally, every core GLM model ships under MIT with no regional restrictions, which means you can download it, fine-tune it, deploy it commercially and keep every prompt inside your own network.

The GLM Model Lineage, Version by Version

The family has moved quickly, and the releases are best understood as a sequence of specific problems being solved rather than as a version number ticking upward. Each entry below links to its own spec sheet on this site.

  • GLM-5.2 — The 1M-token context flagship. It is the open-source state of the art on coding and long-horizon evaluations, with explicit Non-Thinking, High and Max effort levels.
  • GLM-5.1 — The previous flagship. It brought the first large long-horizon gains to the family and remains the baseline every GLM-5.2 improvement is measured against.
  • GLM-5 — The release that opened the 5 series: stronger coding fundamentals, more reliable multi-step execution and noticeably better behaviour on complex agent tasks.
  • GLM-5-Turbo — A 5-series base model tuned for dynamic, long-chain agent scenarios where the plan changes repeatedly and the trajectory has to stay executable.
  • GLM-4.7 — A balanced reasoning model with enhanced programming capability, stable multi-step reasoning and markedly improved front-end generation.
  • GLM-4.7-Flash — The lightweight 30B tier. Efficient and fast enough for local inference, and reported to outrank open models of a similar scale.
  • GLM-4.5-Air — The original efficiency milestone of the family: a small-footprint GLM model with strong performance for every unit of compute it consumes.
  • GLM-ASR — The dedicated speech model. Real-time speech-to-text recognition at a published character error rate of 0.0717.

MIT Licensing and True Open Weights

"Open" is used loosely in this industry, so it is worth being precise about what is claimed for the GLM model family. The core weights are published on HuggingFace and ModelScope under an MIT license, with no regional restrictions and no acceptable-use rider that narrows what the licence otherwise grants. In practice that means the weights behave like any other MIT-licensed artefact in your stack.

The operational consequences are the ones that actually matter. Your prompts never leave infrastructure you control, which resolves an entire class of data-governance objection before it is raised. A model version cannot be deprecated out from under you, because you already hold the weights. And you can fine-tune on proprietary data without shipping that data anywhere. Those properties are why an open GLM LLM is evaluated differently from a hosted API even when the benchmark scores are similar.

Which GLM Model Should I Use?

Start with context length, because it is the constraint that cannot be worked around. If a task needs to see an entire repository, a full API surface or days of logs in one prompt, only GLM-5.2 has a published 1,000,000-token window; everything else in the family will require a retrieval layer that decides for you which fragments the model sees. If your tasks are well scoped, that constraint disappears and hardware becomes the deciding factor instead.

Then choose by footprint. GLM-4.7-Flash at 30B and GLM-4.5-Air are the lightweight tiers built for local inference and high-volume work; GLM-5.1 offers a 200,000-token window with a much smaller KV-cache cost than the flagship; GLM-ASR handles speech-to-text rather than text generation. The sensible production pattern is to run two of them — a small model for volume and the flagship for the problems that genuinely justify the compute.

All figures on this page are results as published by the model authors. glmmodel.com reports them; it does not run these evaluations.

GLM LLM FAQ

What is a GLM model in simple terms?
A GLM model is an open-weight large language model from a family whose current flagship is GLM-5.2. It works like any other general-purpose LLM — text in, text out, with tool use and agent support — but its weights are published under an MIT license so you can download and run it yourself.
Is the GLM model open source?
Yes. Core GLM model weights are MIT licensed with no regional restrictions and are published on HuggingFace and ModelScope. Local inference is supported through transformers, vLLM, SGLang, xLLM and ktransformers, so a GLM model can be served entirely on your own hardware.
Which GLM models are there?
The text line runs GLM-4.5-Air and GLM-4.7 through GLM-5, GLM-5-Turbo, GLM-5.1 and the current flagship GLM-5.2, with GLM-4.7-Flash as the 30B lightweight tier. GLM-ASR is the speech recognition model. Each has a spec sheet on this site.
How good is a GLM model compared to closed models?
It depends on the task. As published, GLM-5.2 leads GPT-5.5 on every long-horizon evaluation and leads Gemini 3.1 Pro on every coding row, while Claude Opus 4.8 still leads most standard coding benchmarks. Under the Claude Code harness GLM-5.2 posts 82.7 on Terminal-Bench 2.1 to Opus 4.8's 78.9.
Is the playground on this site a GLM model?
No, and that matters. The free AI playground on the homepage runs a free open-source LLM through OpenRouter. It is not GLM-5.2 and does not use GLM weights — it exists so you can test a live model instantly with no account while you read the published GLM data.

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