GLM-Realtime is an end-to-end multi-modal model launched by Zhipu. It supports low-latency video understanding, 2-minute content memory for voice interaction, and Function Call, and innovatively integrates a cappella function.

GLM-Realtime

Core parameters and statistics

Project	Specifications
Model/API name	GLM-Realtime
Product Type	AI Model/API
Developer	Zhipu AI (Zhipu AI)
Delivery form	API (WebSocket/HTTP) / cloud inference / private deployment
Context length	2 minutes content memory (approximately 30,000 Token level, exact value not disclosed)
Parameter scale	Undisclosed
Support modal	video, voice, text
End-to-end latency	~300ms (optimized network conditions)
Communication mode	Full duplex (supports real-time interruption)
Pricing model	Free call at this stage
Open source license	Not open source

GLM-Realtime is an end-to-end multi-modal model launched by Zhipu. Its core value lies in integrating the three major capabilities of video understanding, voice interaction, and language generation into a unified model framework. It can achieve a real-time interactive experience of "seeing, hearing, and speaking" without the need for multiple models in series.

User and market recognition

GLM-Realtime is aimed at developers and hardware manufacturers. The core application scenario is the real-time multi-modal interaction capability base of AI hardware (AI PC, AI mobile phone, intelligent robot). Compared with GPT-4o Realtime, its advantages lie in Chinese scene optimization and low-latency performance under domestic network conditions; compared with Gemini Live, its advantages lie in API openness and Function Call flexibility. At this stage, the free strategy is used to accelerate the construction of the developer ecosystem, but the user scale and developer access number are not disclosed.

Cost advantage

Cost Dimension	Description
Free API calls	Currently free, developers can directly obtain the API Key to start integration
Compare GPT-4o Realtime	GPT-4o Realtime audio is about $0.06/minute, GLM-Realtime is almost zero cost in the prototype development stage
Enterprise/Commercial Integration	Tiered pricing may be introduced in the future based on call volume
Privatized deployment	Contact the Zhipu business team for customization

The API call data accumulated during the free period helps the team complete sufficient product verification before formal payment. It is recommended to complete the full process verification from prototype to POC during the free period.

Main functions

• Low-latency video understanding: Real-time video frame analysis based on camera images, with a response delay of about 300ms, suitable for physical object recognition, scene navigation, action guidance and other scenarios.
• Natural voice interaction and real-time interruption: Supports full-duplex voice dialogue, and users can interrupt and speak new instructions at any time during the AI ​​speaking process. The interactive experience is close to the rhythm of human conversation.
• A cappella function: The industry's first large model with the ability to sing in conversations, supporting simple melody and lyrics generation. It is suitable for educational and entertainment scenes, but the music quality cannot yet replace professional singing.
• 2 Minutes Content Memory: Maintain conversational context coherence for up to 2 minutes in real-time interaction scenarios, after which old information is truncated.
• Function Call: The model can independently determine when to call external APIs to obtain real-time information or perform operations, and define tool interfaces based on JSON Schema.

Model and version evolution

Version	Name	Release Date	Core Changes
0.9	Beta version	~2024-10	Basic multi-modal interaction functions, mainly text and voice
1.0	Official version	~2025-01	API launched on Zhipu open platform, supporting video understanding, voice interaction, a cappella singing and Function Call

The iteration rhythm and update content of subsequent versions shall be subject to the official release of Zhipu. Expected directions include: memory window expansion, reasoning delay reduction, and device-side lightweight model release.

Technical advantages

• End-to-end multi-modal architecture: Instead of using a discrete series scheme (speech recognition → text reasoning → speech synthesis), the video encoder, speech encoder and language model are deeply integrated in the training stage, resulting in lower reasoning delay and semantic loss.
• Streaming inference and full-duplex communication: Video frames are processed immediately upon arrival and the response is streamed, allowing both parties to send and receive data at the same time, supporting interruptions and grabs in natural conversations.
• Lightweight Inference Optimization: Inference pipeline compression and acceleration for end-side scenarios such as mobile phones, AI PCs and smart hardware, suitable for deployment of edge devices with limited resources.
• Structured Function Call: Defines tool interfaces based on JSON Schema, supports parallel calls of multiple tools and real-time feedback of results, allowing the model to independently determine when to call external APIs.

Adaptation boundaries and restrictions

• Recommended usage scenarios: AI hardware real-time interaction, intelligent education and companionship, visual customer service, intelligent robot visual interaction, and video conferencing assistance.
• Not recommended: Pure text/pure voice interaction scenarios that do not require video understanding capabilities (not cost-effective); industrial real-time control systems with latency sensitivity below 100ms; tasks that require long document understanding or complex multi-round reasoning.
• Known limitations: The 2-minute memory window limits the coherence of long-term complex tasks; video understanding capabilities are limited by camera image quality and lighting conditions; the model parameter scale and training data composition are not disclosed.

How to use

Entrance	How to use
API interface	Zhipu Open Platform registration → Obtain API Key → WebSocket/HTTP call
Web Chat	Visit bigmodel.cn → Register → Get Started

Typical API call example (pseudocode):

# Establish WebSocket connection
ws = connect("wss://open.bigmodel.cn/api/v1/glm-realtime")
# Send video frames + voice input
ws.send({"video": frame_data, "audio": audio_data})
#Receive model streaming response
for chunk in ws.receive():
    process(chunk)

The specific endpoint address and authentication method are subject to the latest documents of the Zhipu Open Platform.

Product Pricing

Billing items	Price
API calls (currently)	Free
Future pricing	Estimated price per Token / Call duration / Fixed monthly fee

Hidden cost reminder: In high-frequency real-time dialogue scenarios, the network bandwidth (especially video frame transmission) and device-side computing resources need to be borne by the developer. The bandwidth cost consumed by the encoding, compression, and transmission of video frames may exceed the API call charge itself in high-concurrency scenarios.

Application scenarios

• AI Hardware (AI PC/AI Phone): A real-time multi-modal interactive base for device-side AI assistants. The camera allows AI to have "visual perception". Verification method: Actual test of video understanding accuracy in complex environments such as dark light and high contrast.
• Intelligent Education and Companion: The a cappella singing function is suitable for children's education - AI teaches singing children's songs, recognizes picture book content and tells stories. Verification method: Compare the Chinese interactive experience of GPT-4o Realtime in the same scenario.
• Customer service visual assistance: Users display problem scenarios through the camera, and AI understands the picture in real time and gives operational guidance. Verification method: Select 10 typical fault scenarios to test the accuracy and first ring delay.
• Intelligent robot visual interaction: The robot perceives the scene through the camera, understands the voice instructions, responds and performs actions through the voice. Verification method: Verify the end-to-end delay and instruction understanding accuracy in the simulation environment.

Applicable people

• AI Hardware Developers: Hardware manufacturers and embedded developers who develop AI PCs, AI mobile phones, and intelligent robots require real-time multi-modal interaction capabilities on the device side.
• Multimodal App Developer: Developers building apps that need to handle video, voice, and text input simultaneously.
• Education and Entertainment Application Development Team: Use the a cappella function and video understanding capabilities to develop differentiated education or entertainment products.
• Not suitable for the crowd: Scenarios that do not require video understanding capabilities and only require pure text or pure voice interaction. Using GLM-Realtime is an excessive call; long dialogue scenarios that require high memory window length.

Comparison of competing products

Comparison dimensions	GLM-Realtime	GPT-4o Realtime	Gemini Live	MiniMax M2-5 Realtime
Parameter scale	Undisclosed	Undisclosed	Undisclosed	Undisclosed
Context Length	2 minutes to remember	~30 minutes	~20 minutes	Undisclosed
Multi-modal support	Video + Voice + Text	Video + Voice + Text	Video + Voice + Text	Voice + Text
End-to-end latency	~300ms	~200-500ms	~300ms	~400ms
API Pricing (current)	Free	~$0.06/minute	Pay-as-you-go	Pay-as-you-go
Chinese Optimization	Depth Optimization	General	General	Chinese Optimization
Featured functions	A cappella singing, real-time video understanding	Strong multi-modal capabilities	Good ecological integration	High cost performance
Private deployment	Customizable	Not supported	Not supported	Customizable

Summary and Outlook

GLM-Realtime occupies a leading position in technology among domestic multi-modal models with its end-to-end multi-modal architecture and low-latency real-time interaction capabilities. Although the a cappella function is interesting, its current value lies more in the "technical muscle show" level. The real core capability is the deep integration of video understanding and voice interaction - this is a strategic product position on the eve of the explosion of AI hardware. The free strategy lowers the threshold for developers to try.

Risk Disclosure:

1. Memory window limit: The 2-minute memory window limits the coherence of long-term complex tasks. After the window length is exceeded, the contextual information is truncated and cannot be recovered.
2. Pricing Uncertainty: The current free strategy does not have a long-term commitment guarantee. The cost structure after the official pricing is announced may be significantly different from the free period. It is recommended to complete comprehensive performance verification and cost calculation during the free period.
3. Low technical transparency: Key information such as model parameter scale, training data composition, and evaluation benchmarks are not disclosed, making it difficult to conduct objective horizontal comparisons of performance.
4. Video quality dependence: Video understanding ability is limited by camera image quality and lighting conditions. Performance in complex environments such as low light and fast motion needs to be verified by yourself.
5. Unknown privatization deployment conditions: The specific technical and business conditions of the privatization deployment plan need to be communicated and confirmed with the Zhipu business team, which may involve higher thresholds.
6. Network Dependence: Real-time video transmission has high requirements on uplink bandwidth, and the experience may be significantly degraded under cellular networks.

Related tools: CrewAI, langchain

Version Info

• official version ：The API has been launched on the Zhipu open platform, supporting low-latency video understanding, voice interaction and function call. （2025-01-01）
• Beta version ：An early test version, providing basic multi-modal interaction functions. （2024-10-01）