GLM-4 is a series of base language models self-developed by Zhipu AI. It includes multiple versions such as GLM-4-Plus, GLM-4-Air, and GLM-4-Flash, covering different needs from high-intelligence flagships to free and lightweight models. It supports text understanding, logical reasoning, tool invocation, network search and other capabilities.

GLM-4

Core parameters and statistics

GLM-4 is a base language model series self-developed by Zhipu AI, covering multiple levels from flagship to free, and is delivered in the form of API through the BigModel open platform.

Projects	Public Information
Model Series	GLM-4-Plus, GLM-4-Air-250414, GLM-4-AirX, GLM-4-FlashX-250414, GLM-4-Flash-250414
input modal	text
output modal	text
Context window	128K (some versions support 1M)
Maximum output	4K–16K (varies by version)
Deployment form	Public API (BigModel open platform)
Billing method	Billed by token, free version speed limit call
Support capabilities	Streaming output Function Call, structured output MCP, context cache
Supported Platforms	Web Console API

The above information is based on BigModel official documentation.

User and market recognition

GLM-4-Plus ranks among the top three in the world in the SuperBench large model evaluation, breaking the previous monopoly of foreign models in the top three. In a comparative test with GPT-4o, GLM-4-Plus is close to or even partially surpasses GPT-4o on most tasks, with a comprehensive capability reaching 99% of GPT-4o. The GLM-4 series is widely used in enterprise-level AI application scenarios in various industries in China, covering finance, education, medical care, government affairs and other fields.

Cost advantage

Model version	Price (yuan/million Tokens)	Positioning
GLM-4-Plus	5	Highly intelligent flagship
GLM-4-Air-250414	0.5	High cost performance
GLM-4-AirX	10	Extremely fast reasoning
GLM-4-FlashX-250414	0.1	High speed and low price
GLM-4-Flash-250414	Free	Inclusive experience

• C-side: Register through the BigModel console to receive free trial tokens.
• Developers: Pay-per-volume pricing based on model, 50% off Batch API.
• Enterprise: Supports privatized deployment and customized solutions, and the price is subject to the business contract.

Main functions

• Text Understanding and Generation: Supports high-quality Chinese and English text understanding, multiple rounds of dialogue, content creation and knowledge Q&A.
• Logical Reasoning and Mathematics: Excellent performance in mathematical problem solving, code algorithms, logical judgment and other tasks.
• Function Call: Supports external tool integration and can call external capabilities such as search and database API.
• Structured Output: Supports output in structured formats such as JSON to facilitate system integration.
• MCP protocol support: External MCP tools and data sources can be flexibly called to expand application scenarios.
• Streaming Output: Supports real-time streaming response to improve interactive experience.
• Context Caching: Intelligent caching mechanism to optimize long conversation performance.

Model and version evolution

The GLM-4 series consists of multiple versions, with increasing capabilities from left to right:

• GLM-4-Flash-250414: Free model, 128K context, supports 26 languages, suitable for lightweight general scenarios.
• GLM-4-FlashX-250414: Enhanced version of Flash, ultra-fast inference speed, 0.1 yuan/million Tokens.
• GLM-4-Air-250414: Cost-effective base, 15T data pre-training, enhanced Agent capabilities.
• GLM-4-AirX: Air extreme speed version, achieving faster inference speed with the same performance.
• GLM-4-Plus: Flagship version, multiple benchmarks approximate GPT-4o, suitable for complex reasoning and high-precision tasks.

Technical advantages

• Architecture: Based on GLM's self-developed architecture, it uses high-quality synthetic data and PPO reinforcement learning to improve reasoning performance.
• Training data: GLM-4-Air uses 15T high-quality data for pre-training and incorporates rich inference synthetic data.
• Long Text Optimization: Significantly enhance the long text reasoning effect through accurate long and short text data mixing strategies.
• Agent capability enhancement: In the post-training phase, through rejection sampling and reinforcement learning, the Agent's atomic capabilities such as instruction following, code generation, and function calling are enhanced.
• Multi-Language Support: GLM-4-Flash supports up to 26 languages.

How to use

Entrance	Description	Links
BigModel Console	Register an account, obtain API Key, and manage applications	https://bigmodel.cn/console/overview
Model Experience Center	Test model effects online	https://bigmodel.cn/trialcenter/modeltrial/text
API call	Called through standard HTTP interface	https://open.bigmodel.cn/api/paas/v4/chat/completions
SDK	Python (zai-sdk), Java (zai-sdk)	See official documentation for details

Typical call (cURL):

curl -X POST "https://open.bigmodel.cn/api/paas/v4/chat/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-4-plus",
    "messages": [{"role": "user", "content": "Hello"}],
    "max_tokens": 4096,
    "temperature": 0.7
  }'

Product Pricing

The GLM-4 series is priced differentially according to model version. The detailed price is subject to the real-time page of the BigModel official website:

• GLM-4-Plus: 5 yuan/million Tokens
• GLM-4-Air-250414: 0.5 yuan/million Tokens
• GLM-4-AirX: 10 yuan/million Tokens
• GLM-4-FlashX-250414: 0.1 yuan/million Tokens
• GLM-4-Flash-250414: Free

New users will receive free experience tokens upon registration, and enjoy a 50% discount on Batch API calls.

Application scenarios

• Intelligent Customer Service and Dialogue: Use GLM-4-Flash or GLM-4-Air to build a high-concurrency, low-cost intelligent question and answer system.
• Content Creation and Copy Generation: Generate high-quality marketing copy, news reports, and social media content based on GLM-4-Plus.
• Code Development Assistance: Assists in code generation, debugging and interpretation through Function Call and Agent capabilities.
• Data classification and information extraction: Semantic understanding, automatic classification and structured field extraction of massive texts.
• Translation and Multilingual Processing: Supports multilingual translation and accurate processing of multilingual mixed text.

Applicable people

• Developers: Integrate GLM-4 capabilities into your own applications through API, billing based on volume, flexible and controllable.
• Enterprise customers: Enterprises that need privatized deployment or customized solutions can obtain them through business channels.
• Individual users: Use GLM-4 capabilities indirectly through the BigModel Experience Center or third-party applications.
• Not applicable scenarios: It is recommended to choose GLM-4-Long for specific tasks that require ultra-long context (1M+); it is recommended to choose GLM-4V series if multi-modal visual understanding is required.

Summary and Outlook

GLM-4 is the core base language model series of Zhipu AI, covering all levels of needs from free to flagship, and has significant cost and performance advantages in Chinese scenarios. Its API is delivered through the BigModel platform, and its ecosystem covers SDK, MCP, intelligent agent market and other peripheral capabilities.

Procurement/Adoption Risk Assessment: Some old versions of the GLM-4 series have entered the deprecation cycle (such as GLM-4-0520), and users need to pay attention to the version replacement timeline; the model is delivered in the form of API, and enterprise privatization deployment requires separate business communication; prices and concurrency limits are subject to real-time information on the BigModel official website, and it is recommended to check the official pricing page regularly.

Related tools: hugging-face, replicate

Technical advantages and capability boundaries

As an AI model and API product, the core capabilities of GLM-4 can be deeply understood through the following dimensions, which directly affect technology selection and implementation effects.

Inference Performance and Benchmark Performance The model’s reasoning performance is reflected in its performance on standard NLP tasks (text generation, code completion, semantic understanding, multi-turn dialogue, information extraction, etc.). It is recommended to conduct horizontal comparison through public benchmark test lists (such as MMLU, HumanEval, GSM8K, etc.), but please note that there may be a gap between benchmark test scores and actual business scenario performance. Key indicators that affect the actual user experience include: inference speed (Token/s or response delay, which directly determines the smoothness of the user experience), context window length (which determines the input size that can be processed at a time, affecting the complexity of the tasks that can be processed), and consistency of output quality (the stability of the results of multiple outputs of the same input, which affects the perception of reliability).

API Compatibility and Development Ecosystem The depth of API compatibility with mainstream development frameworks (LangChain, LlamaIndex, Semantic Kernel, etc.) directly affects the cost and cycle of integrated development. It is recommended to pay attention to the following integration dimensions: the coverage of language types supported by the SDK (whether mainstream languages ​​such as Python, JavaScript, Go, and Java have official SDKs), streaming output support (SSE/WebSocket protocol compatibility), function calling and tool usage capabilities (whether it supports mapping model output to structured function calls), the flexibility of structured output (JSON mode), and the ability to integrate with enterprise-level infrastructure (VPC deployment, Private Link, unified identity authentication). Complete API documentation and rich code examples can significantly lower the entry barrier to development and reduce integration time and costs.

Deployment Flexibility vs. Cost Tradeoff Depending on data privacy requirements, latency sensitivity and usage scale, GLM-4 can choose between cloud API calls or on-premises deployment options. The advantages of cloud deployment are zero operation and maintenance costs and elastic scalability, which is suitable for scenarios with large fluctuations in usage and rapid prototype development; local deployment provides complete data sovereignty and low latency (no network round-trip overhead), but you need to bear the cost of purchasing hardware such as GPUs and operation and maintenance manpower. It is recommended to use a monthly API call volume of 1 million times or a monthly fee of US$1,000 as a reference dividing line: below this threshold, cloud APIs have better cost-effectiveness and flexibility. After exceeding this threshold, the total cost of ownership of the self-deployment solution should be comprehensively evaluated, taking into account factors such as hardware depreciation, electricity, operation and maintenance manpower, etc.

Model selection and version strategy

For the selection of GLM-4 series models, it is recommended to match the model capabilities of different versions according to specific usage scenarios. The large-parameter version performs better on complex reasoning and multi-step tasks, but has higher costs and longer delays; the small-parameter version can already provide satisfactory output quality in scenarios such as daily conversations and simple question and answer, and the cost is only a fraction of the large version. The recommended selection strategy is: use small and medium versions in standard scenarios to reduce costs, and only call large version models when complex inference tasks need to be processed. This hierarchical calling strategy can reduce the overall API cost by 40-60% without significantly affecting the output quality.

Version Info

• GLM-4-Plus Ultimate Edition ：The highly intelligent flagship version of the GLM-4 series has excellent performance in language understanding, logical reasoning, instruction following, long text processing, etc., and most tasks are close to GPT-4o. There is no official precise release date yet. （~2024-08）
• GLM-4-Air-250414 ：Cost-effective base language model, 15T data pre-training, strengthened agent capabilities such as tool calling, network search, and code. There is no official precise release date yet. （~2025-04）
• GLM-4-Flash-250414 Free version ：Free language model, supports 128K contexts, 26 languages, external tool calls, suitable for high concurrency and lightweight scenarios. There is no official precise release date yet. （~2025-04）