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Description
The GLM-5V-Turbo is an advanced multimodal coding foundation model specifically tailored for tasks that require visual inputs, capable of handling various formats such as images, videos, texts, and files to generate text-based outputs. This model is particularly refined for agent workflows, which allows it to effectively understand environments, plan appropriate actions, and carry out tasks, while also ensuring compatibility with agent frameworks like Claude Code and OpenClaw. Its ability to manage long-context interactions is noteworthy, boasting a context capacity of 200K tokens and an output limit of up to 128K tokens, making it ideal for intricate, long-term projects. Furthermore, it provides a variety of thinking modes suited for diverse scenarios, exhibits robust visual comprehension for both images and videos, and streams output in real-time to enhance user engagement. Additionally, it features sophisticated function-calling abilities that facilitate the integration of external tools, and its context caching capability significantly boosts performance during prolonged conversations. In practical applications, the model can adeptly transform design mockups into fully functional frontend projects, showcasing its versatility and depth in real-world coding scenarios. This versatility ensures that users can tackle a wide range of complex tasks with confidence and efficiency.
Description
SubQ 1.1 Small is the second iteration of Subquadratic’s long-context AI model, built to help enterprises solve problems that require reasoning across entire artifacts rather than isolated chunks. The model is designed for use cases involving large code repositories, document libraries, legal agreements, financial reports, contracts, and other complex information sets. Its Subquadratic Sparse Attention architecture reduces the compute burden of traditional dense attention, making it more practical to process multi-million-token contexts. SubQ 1.1 Small achieves near-perfect performance on needle-in-a-haystack retrieval tests up to 12M tokens, despite being trained primarily at 1M tokens. It also performs strongly on RULER, GPQA Diamond, LiveCodeBench, and AutomationBench Finance, showing a balance between long-context retrieval and general reasoning ability. At 1M tokens, the model uses 64.5x less compute than dense attention and runs 56x faster than FlashAttention-2 on a single attention layer. This efficiency makes long-context training and inference more scalable for enterprise AI applications. SubQ 1.1 Small is especially valuable for teams that need to analyze relationships across full documents, trace logic across codebases, or connect information across extensive collections. The model is intended to help organizations reduce dependence on complex retrieval workarounds and reason more directly over large-scale data.
API Access
Has API
API Access
Has API
Integrations
Claude Code
Java
Ollama
OpenAI
OpenAI Codex
OpenClaw
Python
SubQ
Integrations
Claude Code
Java
Ollama
OpenAI
OpenAI Codex
OpenClaw
Python
SubQ
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Z.ai
Founded
2023
Country
United States
Website
docs.z.ai/guides/vlm/glm-5v-turbo
Vendor Details
Company Name
Subquadratic
Founded
2026
Country
United States
Website
subq.ai/subq-1-1-small-technical-report