What Is GLM AI? Models, Features, Pricing And How It Compares

GLM AI is a family of large language models and multimodal artificial intelligence models developed by Chinese AI company Zhipu AI, which now operates internationally under the Z.ai brand. The latest GLM models can write and analyse text, generate code, reason through complex problems, call external tools, search the web, interpret images and documents, and complete multi-step tasks as AI agents.

The current flagship, GLM-5.2, is aimed particularly at long-running software engineering and agentic workflows. It supports a context window of up to one million tokens, function calling, structured outputs, Model Context Protocol integrations and up to 128,000 output tokens.

GLM is especially interesting because it combines frontier-level capabilities with comparatively low API prices. GLM-5.2 currently costs $1.40 per million input tokens and $4.40 per million output tokens, while lighter GLM models cost considerably less—and some Flash models are available through Z.ai’s API for free.

GLM AI at a glance

Feature	GLM AI
Developer	Zhipu AI / Z.ai
Current flagship	GLM-5.2
Main uses	AI chat, coding, reasoning, agents, research and automation
Maximum context	1 million tokens with GLM-5.2
Maximum output	128,000 tokens
Multimodal models	Yes
Image generation	GLM-Image and CogView-4
Video generation	CogVideoX-3 and Vidu models
Speech recognition	GLM-ASR-2512
Web search	Available as a built-in API tool
Function calling	Yes
Structured JSON output	Yes
MCP support	Yes
OpenAI SDK compatibility	Yes
Lowest text-model price	Free for selected Flash models
Flagship API price	$1.40 input / $4.40 output per million tokens

What is GLM AI?

GLM AI refers to the wider family of AI models created by Zhipu AI. It is not one individual chatbot or model.

The GLM family includes:

• General-purpose language models
• Reasoning and “thinking” models
• Coding models
• Autonomous agent models
• Vision-language models
• Optical character recognition models
• Image generators
• Video generators
• Speech-recognition models

In practical terms, GLM occupies a similar position in Z.ai’s ecosystem to GPT at OpenAI, Claude at Anthropic and Gemini at Google. Different GLM versions are designed for different levels of intelligence, speed, cost and input type.

A developer might use GLM-5.2 to work across a large software repository, GLM-4.7-FlashX to answer high volumes of customer questions cheaply, GLM-5V-Turbo to interpret screenshots, and GLM-Image to generate marketing graphics.

What does GLM stand for in Zhipu AI?

GLM stands for General Language Model.

The name originally referred to an approach described by researchers from Tsinghua University and Zhipu AI in the paper GLM: General Language Model Pretraining with Autoregressive Blank Infilling. The original architecture combined ideas from autoregressive language modelling and masked-language modelling.

The meaning has since expanded. GLM is now the brand name for Z.ai’s complete family of language, reasoning, coding and multimodal models, rather than just the original pretraining method.

Who makes GLM AI?

GLM is developed by Zhipu AI, a Beijing-based artificial intelligence company founded from research connected to Tsinghua University.

Internationally, the company increasingly uses the name Z.ai. As a result, documentation and model listings may refer to Zhipu AI, Z.ai or the organisation name zai-org. These refer to the same wider model ecosystem.

Some earlier models and repositories also use the names ChatGLM and THUDM. ChatGLM was the conversational branch of the model family, while newer releases generally use the simpler GLM name.

What can GLM AI do?

The exact capabilities depend on the selected model, but the GLM ecosystem covers most major generative-AI workloads.

Answer questions and generate content

GLM models can produce articles, reports, summaries, product descriptions, emails, translations and conversational answers.

Thinking can be disabled for quick, straightforward responses or enabled when a question requires deeper reasoning. This gives developers a way to balance response quality, latency and token usage.

and debug code

Coding has become one of GLM’s strongest areas. Current models can:

• Generate front-end and back-end code
• Navigate large codebases
• Refactor multiple related files
• Follow repository-specific instructions
• Run tests and interpret failures
• Investigate bugs from logs
• Work with terminal tools
• Build applications from natural-language requirements
• Use external developer tools through function calls or MCP

These abilities reflect a wider shift from basic code completion towards AI systems that can plan, edit multiple files and test their own work. Our guide examining how well AI can code in 2026 explores what these tools can realistically build—and where human oversight remains essential.

GLM-5.2 is particularly focused on project-scale engineering. Z.ai says it can retain architectural boundaries, API contracts, directory structures and engineering rules over long-running tasks. 

Complete agentic tasks

An AI agent does more than return a block of text. It plans a task, selects tools, takes actions, checks the result and adjusts its approach.

GLM models support function calling, tool streaming, structured outputs and MCP connections. These features can be used to build agents that:

• Search the web
• Query databases
• Analyse company documents
• Update internal systems
• Run code
• Coordinate workflows
• Use business APIs
• Perform scheduled or persistent tasks

The model itself does not automatically receive access to a user’s files, browser or business systems. Developers must connect and authorise the appropriate tools.

Search the web

Z.ai offers web search as a built-in API tool. It currently costs $0.01 per use, in addition to the tokens consumed by the model.

Web search allows a GLM-powered application to retrieve current information rather than relying only on the model’s training data. The application should still show sources and apply appropriate fact-checking where accuracy matters.

Understand images, videos and documents

Vision-language models such as GLM-5V-Turbo and GLM-4.6V can process combinations of text, images, files and video.

Potential uses include:

• Reading charts and tables
• Summarising PDFs
• Extracting details from invoices
• Interpreting interface screenshots
• Converting a design mock-up into code
• Understanding events in a video
• Locating objects inside an image
• Operating graphical interfaces as part of an agent

GLM-OCR offers a cheaper, specialised option when the main requirement is extracting text from scanned material.

Generate images and video

Z.ai’s wider model platform includes:

• GLM-Image: Image generation with an emphasis on instruction following and text rendering
• CogView-4: Lower-cost image generation
• CogVideoX-3: Text-to-video and image-to-video generation
• Vidu: Video generation from text, images, start/end frames or reference material

These are separate models rather than built-in output modes of every GLM language model.

Transcribe audio

GLM-ASR-2512 converts spoken audio into text. Z.ai prices it at approximately $0.0024 per minute, although actual billing is expressed in tokens.

It can support meeting transcription, voice interfaces, subtitles and searchable audio archives.

The main GLM AI models explained

Z.ai offers a large catalogue, but the following are the most relevant models for most users and developers.

GLM-5.2

Best for: Large codebases, demanding agents and long-running engineering projects.

GLM-5.2 is Z.ai’s current flagship foundation model. Its defining upgrade is a one-million-token context window, compared with 200,000 tokens on GLM-5 and GLM-5.1.

It accepts text and produces text, with a maximum output of 128,000 tokens. Supported features include:

• Thinking mode
• Adjustable reasoning effort
• Streaming
• Function calling
• Context caching
• Structured output
• MCP integrations

Z.ai released GLM-5.2 on 16 June 2026 and positions it as its strongest open model for long-horizon software engineering. In Z.ai’s published results, it scored 81.0 on Terminal-Bench 2.1 and 62.1 on SWE-bench Pro. These are vendor-reported benchmark results and should be considered alongside independent testing.

The biggest reason to choose GLM-5.2 is not merely that it can accept more tokens. It is designed to retain requirements and engineering decisions while carrying out a task across many files and stages.

GLM-5.1

Best for: Strong agentic coding without needing a one-million-token context window.

GLM-5.1 is a 200,000-token flagship model designed for sustained autonomous work. Z.ai says it can continue working on a single task for up to eight hours, moving through planning, implementation, testing and refinement.

It supports the same core developer features as GLM-5.2, including thinking, tool calling, caching, structured outputs and MCP.

GLM-5.1 remains capable, but its API price is currently identical to GLM-5.2. New implementations should generally start with GLM-5.2 unless testing shows that GLM-5.1 is faster or more consistent for a specific workload.

GLM-5

Best for: Powerful general reasoning and agentic engineering at a slightly lower cost.

GLM-5 was the first major GLM release explicitly positioned around “agentic engineering.” It handles coding, tool use, complex reasoning and multi-step agent tasks with a 200,000-token context window.

Its lower price makes it potentially attractive for applications that need strong reasoning but do not require the refinements or extended context of GLM-5.2.

GLM-5-Turbo

Best for: High-throughput tool-using agents and OpenClaw-style automation.

GLM-5-Turbo is not simply a faster general-purpose version of GLM-5. Z.ai says it was trained specifically for OpenClaw scenarios, with greater emphasis on:

• Reliable tool invocation
• Command following
• Persistent tasks
• Long-chain execution
• Multi-step planning
• Skills and external integrations

It has a 200,000-token context window and a maximum output of 128,000 tokens. Interestingly, its token price is slightly higher than standard GLM-5, so the reason to select it is its specialised agent behaviour rather than the “Turbo” name alone.

GLM-4.7

Best for: Affordable coding, reasoning, prototypes and general AI applications.

GLM-4.7 remains one of the most attractive models in the range for price-sensitive developers. It provides a 200,000-token context window, up to 128,000 output tokens, thinking mode, function calling and structured outputs.

The GLM-4.7 family contains three main versions:

• GLM-4.7: Full model
• GLM-4.7-FlashX: Very inexpensive, high-speed version
• GLM-4.7-Flash: Free API version

GLM-4.7 is less capable than the GLM-5 series on the most demanding long-running tasks. However, it may offer better value for ordinary content generation, classification, extraction, support bots and moderate coding tasks.

GLM-4.6 and GLM-4.5

GLM-4.6 and GLM-4.5 were important steps in Z.ai’s transition towards agentic coding models. They remain accessible through the API, but most new projects should compare them with GLM-4.7 or GLM-5.

GLM-4.5 also has several cost and performance variants:

• GLM-4.5
• GLM-4.5-X
• GLM-4.5-Air
• GLM-4.5-AirX
• GLM-4.5-Flash

GLM-4.5-Air can still be useful where a developer wants a balance between cost and intelligence. The X versions are more expensive and should be benchmarked carefully before being chosen over newer models.

GLM-4-32B-0414-128K

Best for: Very cheap legacy or straightforward text workloads.

This is a smaller 32-billion-parameter model with a 128,000-token context window. Input and output are both priced at $0.10 per million tokens.

It is dramatically cheaper than the flagship range, but it should not be treated as equivalent to GLM-5.2. It is more suitable for simple transformations, classification, extraction and other predictable tasks.

GLM vision and specialist models

Model	Primary purpose	API price
GLM-5V-Turbo	Advanced multimodal reasoning and visual agents	$1.20 input / $4.00 output per million tokens
GLM-4.6V	Image, video and document understanding	$0.30 input / $0.90 output
GLM-4.6V-FlashX	Low-cost visual workloads	$0.04 input / $0.40 output
GLM-4.6V-Flash	Free visual API model	Free
GLM-4.5V	Multimodal reasoning	$0.60 input / $1.80 output
GLM-OCR	Text extraction from images and documents	$0.03 input / $0.03 output
GLM-Image	Image generation	$0.015 per image
CogView-4	Image generation	$0.01 per image
CogVideoX-3	Video generation	$0.20 per video
GLM-ASR-2512	Speech recognition	About $0.0024 per minute

GLM AI API pricing

The following are Z.ai’s published international API prices as of 27 July 2026. Text-model prices are per one million tokens and are shown in US dollars.

Model	Input	Cached input	Output
GLM-5.2	$1.40	$0.26	$4.40
GLM-5.1	$1.40	$0.26	$4.40
GLM-5	$1.00	$0.20	$3.20
GLM-5-Turbo	$1.20	$0.24	$4.00
GLM-4.7	$0.60	$0.11	$2.20
GLM-4.7-FlashX	$0.07	$0.01	$0.40
GLM-4.6	$0.60	$0.11	$2.20
GLM-4.5	$0.60	$0.11	$2.20
GLM-4.5-X	$2.20	$0.45	$8.90
GLM-4.5-Air	$0.20	$0.03	$1.10
GLM-4.5-AirX	$1.10	$0.22	$4.50
GLM-4-32B-0414-128K	$0.10	Not listed	$0.10
GLM-4.7-Flash	Free	Free	Free
GLM-4.5-Flash	Free	Free	Free

Prices can change, so developers should confirm them on the official Z.ai pricing page before calculating production costs.

Example GLM-5.2 API cost

Suppose an application sends:

• 2,000 input tokens
• 800 output tokens
• No cached input
• No web-search call

The approximate cost is:

• Input: 2,000 ÷ 1,000,000 × $1.40 = $0.0028
• Output: 800 ÷ 1,000,000 × $4.40 = $0.00352
• Total: $0.00632 per answer

At that usage level, 1,000 answers would cost approximately $6.32.

If each answer also used Z.ai’s built-in web search once, search charges would add another $10 per 1,000 answers, producing a combined estimated cost of $16.32.

Real costs will vary with prompt length, reasoning tokens, response length, caching and the number of tool calls.

Which GLM model should you use?

Requirement	Recommended starting model
Best overall GLM model	GLM-5.2
Large codebase or very long documents	GLM-5.2
Strong reasoning at lower cost	GLM-5
Tool-heavy autonomous agent	GLM-5-Turbo
Affordable general chatbot	GLM-4.7
Extremely cheap high-volume answers	GLM-4.7-FlashX
Free experimentation	GLM-4.7-Flash
Image and screenshot understanding	GLM-5V-Turbo
Cheaper vision processing	GLM-4.6V-FlashX
OCR and document text extraction	GLM-OCR
Image generation	GLM-Image or CogView-4
Speech-to-text	GLM-ASR-2512

A sensible production system may use several models. For example, GLM-4.7-FlashX could handle routine requests while GLM-5.2 is selected only for questions that require deep reasoning or extensive context.

This kind of model routing can reduce costs without forcing every user onto the weakest model.

 GLM compare with ChatGPT and OpenAI’s models?

GLM and GPT models overlap in many areas: conversation, coding, reasoning, tool use, structured outputs and agent development.

OpenAI similarly offers different models for demanding reasoning, everyday professional work and high-volume requests. Our breakdown of the latest ChatGPT models explains how its flagship, balanced and lower-cost options differ.

GLM’s strongest advantages are:

• Low API pricing
• Very long context on GLM-5.2
• Open-weight model availability
• Strong emphasis on agentic coding
• OpenAI-compatible API access
• Free and ultra-low-cost Flash options

OpenAI’s ecosystem may remain more attractive when a product requires tight integration with OpenAI tools, established enterprise support or capabilities available only through its platform.

The comparison is also not exactly “GLM versus ChatGPT.” GLM is a model family, whereas ChatGPT is a finished consumer application containing models, tools, memory, file handling and other product features.

A fairer technical comparison is GLM-5.2 versus a specific OpenAI API model, tested using the same prompts, tools, token limits and success criteria.

How does GLM compare with Claude?

Many important GLM models have downloadable weights published by Z.ai, including releases through the company’s Hugging Face organisation. This places GLM within a growing group of open-source and open-weight AI models competing with proprietary systems from OpenAI, Anthropic and Google.

GLM-5.2 offers:

• A one-million-token context window
• Up to 128,000 output tokens
• MCP support
• Tool calling
• Long-horizon engineering
• Much lower published token prices than premium frontier-model pricing has traditionally involved

Z.ai claims GLM-5.2 is close to recent Claude Opus models on selected coding benchmarks. However, benchmark proximity does not guarantee identical performance in production.

Claude may still be preferred for certain writing, reasoning, safety-sensitive or mature coding workflows. GLM may be preferable when token cost, local deployment possibilities or very large context are more important.

The best choice should be made with a representative internal test set rather than one public leaderboard.

How does GLM compare with Gemini?

GLM-5.2 and Google’s Gemini models both target long-context reasoning, coding and agent workflows.

Gemini has the advantage of integration with Google’s wider cloud and productivity ecosystem. Its multimodal capabilities are also central to the Gemini product family.

GLM’s advantages include inexpensive token pricing, open-weight releases, strong coding-agent optimisation and several free or extremely cheap API options.

For multimodal applications, the comparison should include GLM-5V-Turbo rather than only the text-based GLM-5.2.

How does GLM compare with DeepSeek?

GLM and DeepSeek are two of the most prominent Chinese-developed model families available to international developers.

Both emphasise:

• Strong reasoning and coding
• Competitive API costs
• Open or downloadable model weights
• Developer-oriented deployment
• High token efficiency

DeepSeek gained widespread adoption through its reasoning and coding performance at a low cost. GLM differentiates itself through a broad model platform, built-in web search, dedicated vision and media models, OpenAI-compatible access and an increasingly strong focus on long-running agents.

GLM-5.2 may be particularly attractive for massive context and project-level coding. DeepSeek should still be included in testing where reasoning quality and cost efficiency are the main priorities.

Is GLM AI open source?

Many important GLM models have downloadable weights published by Z.ai, including releases through the company’s Hugging Face organisation.

However, “open source” requires some qualification. Access to model weights does not always mean every component, training dataset or training process is fully open. Model licences may also contain terms that differ from conventional open-source software licences.

Before using a GLM model commercially or redistributing a modified version, developers should review the specific licence attached to that model rather than assuming that every GLM release has identical terms.

Can GLM run locally?

Open-weight GLM models can be self-hosted, but hardware requirements vary enormously.

Flagship mixture-of-experts models may contain hundreds of billions of total parameters. Even when only part of the model is active for each token, storing and serving the complete weights can require multiple high-memory GPUs or specialist inference infrastructure.

Smaller GLM releases are more realistic for local workstations. Quantisation can reduce memory usage, but it may also affect speed, output quality or supported context length.

For most small businesses, using Z.ai’s hosted API will be simpler and potentially cheaper than operating flagship GLM models locally.

How to access the GLM API

Z.ai provides its own SDK, REST endpoints and compatibility with the OpenAI Python SDK.

A basic OpenAI-compatible Python request looks like this:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_ZAI_API_KEY",
    base_url="https://api.z.ai/api/paas/v4/",
)

response = client.chat.completions.create(
    model="glm-5.2",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful research assistant."
        },
        {
            "role": "user",
            "content": "Explain the main benefits of renewable energy."
        }
    ],
)

print(response.choices[0].message.content)

Existing applications built around OpenAI-style chat completions may therefore require relatively limited changes. Compatibility should still be tested carefully, particularly for thinking configuration, tool calls, streaming and provider-specific response fields.

GLM AI advantages and disadvantages

Advantages

• Highly competitive API prices
• One-million-token context on GLM-5.2
• Strong coding and agent capabilities
• Function calling and MCP support
• OpenAI SDK compatibility
• Free text and vision models
• Separate models for vision, OCR, images, video and audio
• Downloadable model weights
• Cached-input discounts
• Up to 128,000 output tokens on major language models

Disadvantages

• The model catalogue can be confusing
• Some models overlap heavily in price and purpose
• Independent evaluations may differ from vendor benchmarks
• Self-hosting flagship models requires substantial hardware
• Documentation and product naming can vary between Zhipu AI, Z.ai, ChatGLM and THUDM
• International support and integrations may be less mature than larger US cloud ecosystems
• Data processing location and regulatory requirements need checking before business use
• The newest model may not always be the fastest or cheapest choice

Is GLM AI good?

GLM AI is a serious option for developers who need capable reasoning, coding, long context or AI agents without paying premium token prices for every request.

GLM-5.2 is the most compelling choice for difficult engineering and long-context tasks. GLM-5 provides a cheaper route to strong reasoning, while GLM-4.7-FlashX is unusually affordable for high-volume applications.

Its value is strongest when developers are prepared to test multiple models and route requests according to complexity. A simple support question does not need a one-million-token flagship model, while a repository-wide refactor should not be assigned automatically to the cheapest Flash model.

GLM should not be selected solely because of a benchmark or low headline price. Output reliability, latency, tool success rate, data policies and the full cost per completed task all matter.

Final verdict

GLM has developed from a research architecture into one of the broadest and most competitively priced AI model families available.

The key model is GLM-5.2, which combines a one-million-token context window with strong coding, reasoning and agent features. However, its cheaper siblings may provide better value for everyday applications. GLM-5 is a sensible lower-cost reasoning model, GLM-4.7 is an affordable all-rounder, and GLM-4.7-FlashX is suited to high-volume workloads where cost matters most.

For developers, the strongest reason to consider GLM is the combination of capability, long context, open-weight availability and low API pricing. The main caution is that Z.ai’s benchmark claims and model descriptions should be validated against the exact work an application needs to perform.

In short, GLM is no longer simply an alternative Chinese chatbot model. It is a complete AI platform spanning language, coding, agents, vision, image generation, video and speech—and a credible competitor to the model ecosystems offered by OpenAI, Anthropic, Google and DeepSeek.