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What is SubQ?

SubQ is a next-generation large language model developed by Subquadratic, designed to handle extremely long-context reasoning tasks with high efficiency. It supports up to 12 million tokens in a single prompt, allowing it to process entire codebases, months of development history, and large datasets in one step. The model uses a fully sub-quadratic sparse-attention architecture, which reduces unnecessary computations by focusing only on meaningful relationships between data points. This approach significantly lowers computational costs while maintaining strong performance across complex tasks. SubQ is optimized for use cases such as software engineering, code analysis, long-context retrieval, and AI agent workflows. It enables developers to analyze large amounts of information without breaking it into smaller segments. The model offers fast processing speeds and lower operational costs compared to traditional transformer-based models. SubQ is accessible through APIs, making it easy for developers and enterprises to integrate it into their systems. It can also be used within coding agents to improve code mapping, exploration, and understanding. The platform supports streaming and tool usage for more dynamic workflows. Its architecture allows it to scale efficiently as data size increases, overcoming common limitations of standard models. SubQ also delivers competitive performance on benchmarks related to coding and long-context tasks. By combining efficiency, scalability, and large context capabilities, it provides a powerful solution for advanced AI applications.

What is GLM-5.3-Flash?

GLM-5.3-Flash is an efficiency-focused multimodal AI model from Z.ai that combines advanced reasoning, coding, agentic execution, and visual intelligence. It is the first GLM-5-series model designed with native multimodal capabilities, allowing it to work directly with both textual and visual inputs. The architecture uses 320 billion total parameters while activating only 18 billion at a time, significantly reducing the amount of computation required for inference. A hybrid attention design blends linear attention for local information with sparse attention for retrieving important context from much larger inputs. Z.ai also uses technologies such as IndexPool and Manifold-Constrained Hyper-Connections to improve memory efficiency, latency, and model scaling. The model can operate with context windows of up to one million tokens, making it suitable for large repositories, lengthy documents, extended agent sessions, and complex multimodal workflows. GLM-5.3-Flash was trained on a 30-trillion-token multimodal corpus intended to strengthen reasoning across code, images, interfaces, documents, spreadsheets, presentations, and other business artifacts. In software development scenarios, the model can visually inspect rendered applications, evaluate its own output, and iteratively correct layout, functionality, or interaction issues. Z.ai’s reported benchmark results show large improvements over GLM-5.2 in areas such as software engineering and automation, while placing GLM-5.3-Flash close to leading frontier systems on several coding and agentic evaluations. Before its formal release, the model was anonymously tested under the name ox-alpha on OpenCode and OpenRouter, where Z.ai says it became one of the most widely used models during its testing period. GLM-5.3-Flash is available through Z.ai’s API and coding products as well as through downloadable weights on Hugging Face, with deployment support for SGLang, vLLM, and TokenSpeed.

Media

Media

Integrations Supported

Claude Code
Cheaper Inference
DeepSeek Harness
GLM Coding Plan
Hermes Agent
OpenAI
OpenAI Codex
OpenClaw
OpenCode Go
OpenCode Zen
OpenRouter
Pi Agent
SubQ 1.1 Small
Z.ai
omp

Integrations Supported

Claude Code
Cheaper Inference
DeepSeek Harness
GLM Coding Plan
Hermes Agent
OpenAI
OpenAI Codex
OpenClaw
OpenCode Go
OpenCode Zen
OpenRouter
Pi Agent
SubQ 1.1 Small
Z.ai
omp

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

$0.15 per 1M tokens (input)
Input: $0.15 per 1M tokens
Output: $0.50 per 1M tokens
Cached input: $0.03 per 1M tokens
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Subquadratic

Date Founded

2026

Company Location

United States

Company Website

subq.ai/

Company Facts

Organization Name

Z.ai

Date Founded

2019

Company Location

China

Company Website

z.ai

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