What GLM is and who makes it
GLM is the model family from Zhipu AI, a Beijing company that spun out of Tsinghua University and now markets its consumer and developer products under the name Z.ai. GLM stands for General Language Model, and the family has been through several generations, with the GLM-4 series and its successors being the ones people actually use today.
Zhipu occupies a particular position in the Chinese AI landscape. It is the academic spinout, the one with the strongest research pedigree and the most consistent record of publishing open-weight models that other people build on. Where some Chinese labs open-source selectively for attention, Zhipu has released open versions of its flagship models fairly consistently, including lighter variants designed to run on modest hardware.
It is also, and this should be stated plainly rather than buried, a company that has attracted export-control attention from the United States. Zhipu was added to the US entity list in January 2025. For an Indian business that is not a legal prohibition, but it is a signal that Western enterprise customers and partners may have policies that make using the tool awkward. If you supply software to US clients, ask before you build on it.
Key features in depth
Coding. This is where GLM has earned genuine respect. The recent GLM releases are strong at writing, debugging and refactoring code, and Zhipu sells a coding-focused subscription aimed squarely at developers who want that capability without a frontier-model bill. Users report that it holds up well inside agentic coding tools, which is a harder test than answering a coding question in a chat window.
Agent workflows. GLM models are tuned for tool use, meaning the model calls functions, uses a browser, runs code and chains steps rather than just producing text. If you are building an agent that has to complete a multi-step task, this is a more relevant strength than raw conversational quality.
Open weights. Zhipu publishes open versions, including smaller variants intended for self-hosting. For an Indian team that wants agent capability inside its own infrastructure, this is the whole argument.
Chat assistant. There is a normal chat interface with document upload, web search and image generation, reachable from India. It is competent and unremarkable. Nobody should switch their daily assistant to it on the strength of the chat experience alone.
API. Zhipu's API is priced aggressively low, and this is the main commercial hook. For a startup burning money on inference, the difference between a frontier API and GLM can be an order of magnitude.
Pricing for Indian users
There is a free tier on the chat product, sufficient for trying it out and for light personal use.
The subscription that matters is the coding plan, which Zhipu prices in US dollars at a level deliberately set below Western coding assistants. Entry pricing has been in the region of a few dollars a month, which at roughly Rs 85 to the dollar means something like Rs 250 to Rs 600 per month for the lower tiers, with higher tiers costing more. Treat every one of those numbers as indicative and check the current page, because Chinese AI pricing changes frequently and introductory rates expire.
Set that against the alternatives an Indian developer is choosing between. A mainstream Western coding assistant subscription typically runs Rs 1,700 per month and upward, and the premium agentic tiers run several times that. If GLM does eighty per cent of the job at a quarter of the price, that is a real decision for a bootstrapped team in Indore or Coimbatore, not a marginal saving.
API pricing is per million tokens in dollars and sits well below frontier rates. Billing is by card, and Indian cards generally work, though international transaction settings on your card and the RBI mandate rules on recurring payments can cause a first-time failure that has nothing to do with the vendor.
GLM versus DeepSeek and Qwen
| What you care about | GLM (Z.ai) | DeepSeek | Qwen |
|---|---|---|---|
| Maker | Zhipu AI, Tsinghua spinout | DeepSeek, Hangzhou | Alibaba |
| Standout strength | Coding and agent workflows | Reasoning and maths at very low cost | Breadth, multilingual, largest open family |
| Open weights | Yes, including small variants | Yes | Yes, the most extensive |
| API cost | Very low | Very low | Low, tied to Alibaba Cloud |
| Coding subscription | Yes, cheap and aimed at developers | No equivalent consumer plan | Not in the same form |
| Indic language support | Weak | Weak | Best of the three, still behind Western models |
| Access from India | Chat and API reachable, card payment usually works | Reachable | Reachable |
| Policy friction | On the US entity list since January 2025 | Restricted on some government devices | Fewer restrictions, still a Chinese vendor |
Among Chinese options, the rough division is that DeepSeek is the reasoner, Qwen is the generalist with the widest open family, and GLM is the one to try if your work is coding and agents. All three carry the same underlying data residency question.
Real workflows where GLM fits
A bootstrapped startup founder in Indore. You are building a product, you cannot justify Rs 2,000 a month per developer on a coding assistant, and your codebase is your own rather than a client's. GLM's coding plan at a fraction of the price is a rational choice, and you can move later if you outgrow it.
A developer building an internal agent. Something that reads a ticket, queries a database, drafts a reply and files an update. GLM is tuned for exactly this kind of tool-calling loop, and the API cost of an agent that makes many calls per task is where cheap inference compounds.
A data science team that must self-host. A hospital, a bank, a firm with a hard rule against external APIs. The open GLM variants let you run a capable model on your own servers in India, which resolves the residency question completely.
A student learning to code in Bengaluru. The free tier plus a very cheap paid plan gives you a capable assistant at a price that fits a student budget, without needing a parent's international card for a Rs 1,700 subscription.
A freelance developer serving Indian SME clients. Fine, provided the client agrees. If you write software for a US enterprise, raise the entity-list question with them first rather than after.
Limitations to know
Hindi and Indian languages are weak. GLM handles Chinese and English well. Hindi output is serviceable at best and often unnatural, and Tamil, Bengali, Telugu and Marathi are noticeably worse. Nothing about this model family is tuned for India.
Documentation and support are Chinese-first. English documentation exists and is improving, but you will hit Chinese-language pages, Chinese error strings and community discussion you cannot read. Support response times are set to Chinese business hours.
The entity list is a genuine business consideration. Zhipu's presence on the US entity list from January 2025 does not stop an Indian developer using the product, but it does mean US enterprise customers, investors and partners may have procurement policies that exclude it. If your business sells into the US, treat this as a commercial risk to check rather than ignore.
Content restrictions apply. Like all Chinese consumer AI services, GLM operates under Chinese content regulation and will deflect on politically sensitive subjects. For coding this rarely matters. For research or writing on geopolitics, history or Chinese domestic affairs, you are getting a filtered response.
Data residency under Chinese law. Using the hosted chat or API means your prompts, and any code you paste, are processed on infrastructure governed by Chinese law, including national security and intelligence provisions requiring domestic organisations to assist state authorities. For a developer pasting proprietary code, this is the substantive risk, not a theoretical one. Many Indian enterprises restrict Chinese AI tools on work machines for this reason. The open-weight route removes the problem, and if your code genuinely matters, take it.
Reliability and continuity. Pricing, limits and access have all changed with little notice in this segment. Do not architect a product around a single cheap Chinese endpoint without a fallback.
How to get started
- Open the official Z.ai site. Confirm the domain, since cheap-API scam sites imitating Chinese model providers are common.
- Sign up with an email address. An Indian mobile number generally works and a Chinese number is not required for the international product.
- Test the free chat tier on a real coding problem from your own work rather than a puzzle. Ask it to explain a function, then to refactor it, and judge the result as you would a junior developer's.
- If you code daily, look at the coding subscription. Enable international transactions on your card before you try to pay, because that is the usual reason a first payment fails.
- Connect it to the coding tool you already use. GLM is often consumed through an editor or agent framework rather than the chat window, and that is where the value shows.
- For API work, generate a key and run a small batch to measure your real token cost before you assume the headline price applies to your workload.
- Decide your code confidentiality rule now. Client code, credentials, customer data and anything under an NDA should not go into a hosted endpoint under a different legal regime.
- If that rule rules out the hosted service, download an open GLM variant instead and run it on your own or a rented Indian GPU.