Zhipu AI Unveils GLM-4.5V: 106B Open Source Multimodal Powerhouse

Competitive analysis against other open-source vision models shows that GLM-4.5V maintains a consistent performance margin. While smaller models like Llama-3.2-Vision struggle with complex reasoning, GLM-4.5V excels in multi-step visual tasks. The model also passed the SWE-bench benchmark with a 78% pass rate, validating its utility for automated software engineering workflows involving visual codebases.

• MMLU Score: 85.4%
• HumanEval: 88.2%
• ScienceQA: 92.1%
• SWE-bench: 78%

API Pricing

Zhipu AI has structured the pricing for GLM-4.5V to be highly competitive for both startups and large enterprises. The API access model charges based on token usage, with a free tier available for developers to test the model's capabilities. For production workloads, the input cost is set at $0.20 per million tokens, while the output cost is $0.60 per million tokens. This pricing structure is approximately 40% lower than the industry standard for comparable 100B+ parameter models.

In addition to the standard API, Zhipu offers a subscription-based tier for AI agents and specialized workflows, similar to their GLM-5 Turbo offering. This tier includes optimized latency for real-time applications. The free tier allows for up to 10,000 tokens per day, which is sufficient for prototyping and small-scale testing. This accessibility lowers the barrier to entry for the global developer community.

• Input Cost: $0.20 / 1M tokens
• Output Cost: $0.60 / 1M tokens
• Free Tier: 10K tokens/day
• Subscription: Optimized for Agents

Comparison Table

When compared to other leading models in the market, GLM-4.5V offers a balanced trade-off between cost and capability. While some competitors offer higher context windows, they often come with significantly higher inference costs. The table below highlights the key differences between GLM-4.5V and its primary competitors, including the recently announced GLM-5 and other vision-focused models.

• Includes GLM-4.5V, GLM-5, Qwen-2.5-VL, Llama-3.2-Vision

Use Cases

The versatility of GLM-4.5V makes it suitable for a wide array of enterprise applications. In the realm of software development, it can serve as a visual coding assistant, analyzing UI mockups and generating corresponding frontend code. For RAG (Retrieval-Augmented Generation) systems, the model's long context window allows it to ingest massive knowledge bases and answer questions based on both text and visual data.

Another prime use case is automated content moderation and analysis. By combining OCR with semantic understanding, GLM-4.5V can detect sensitive information within screenshots or scanned documents. Furthermore, its open-source nature encourages research into specialized domains, such as autonomous driving perception, where the model's ability to interpret road signs and traffic signals can be fine-tuned for specific vehicle architectures.

• Visual Coding Assistants
• Enterprise RAG Systems
• Document Analysis & OCR
• Autonomous Driving Perception

Getting Started

Accessing GLM-4.5V is straightforward for developers. Zhipu AI provides a dedicated API endpoint accessible via their developer portal. The SDK supports Python, JavaScript, and Go, simplifying integration into existing stacks. For local deployment, the model weights are hosted on Hugging Face, allowing engineers to run the model on-premise using compatible hardware like NVIDIA H100 or Huawei Ascend 910B.

To begin, developers should register for an API key on the Zhipu platform. Documentation is available via the official GitHub repository, which includes sample notebooks for vision-language tasks. The release includes a comprehensive guide on quantization techniques to optimize performance on consumer-grade GPUs, ensuring that smaller teams can also leverage this powerful model.

• API Endpoint: api.zhipu.ai
• SDK Support: Python, JS, Go
• Weights: Hugging Face
• Docs: GitHub Repository

Comparison

API Pricing — Input: 0.6 / Output: 2.2 / Context: 256K

Sources