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What is SubQ Snap?

SubQ Snap is an advanced decision-making framework tailored for both enterprise and agentic workflows, facilitating quick and organized decisions over large datasets. It proficiently handles a range of tasks, such as pinpointing relevant files, selecting the right tools, validating results, evaluating whether to continue with a workflow, and deciding on the need for a frontier model. Unlike conventional text generators, Snap considers the specific context, the question presented, and a finite set of possible answers to create a probability distribution for those answers. It boasts the capability to process up to 384K tokens, allowing it to incorporate varied types of information like codebases, contracts, interaction histories, logs, screenshots, charts, diagrams, and scanned documents all within one query. By employing Subquadratic Sparse Attention technology, Snap greatly reduces the resources required for analyzing lengthy inputs, and its decision head skillfully links contextual representations to acceptable answers without depending on autoregressive text generation. This cutting-edge methodology not only streamlines decision-making but also boosts precision in various application scenarios, ultimately transforming how organizations approach complex choices.

What is GLM-5?

GLM-5 is Z.ai’s most advanced open-source model to date, purpose-built for complex systems engineering, long-horizon planning, and autonomous agent workflows. Building on the foundation of GLM-4.5, it dramatically scales both total parameters and pre-training data while increasing active parameter efficiency. The integration of DeepSeek Sparse Attention allows GLM-5 to maintain strong long-context reasoning capabilities while reducing deployment costs. To improve post-training performance, Z.ai developed slime, an asynchronous reinforcement learning infrastructure that significantly boosts training throughput and iteration speed. As a result, GLM-5 achieves top-tier performance among open-source models across reasoning, coding, and general agent benchmarks. It demonstrates exceptional strength in long-term operational simulations, including leading results on Vending Bench 2, where it manages a year-long simulated business with strong financial outcomes. In coding evaluations such as SWE-bench and Terminal-Bench 2.0, GLM-5 delivers competitive results that narrow the gap with proprietary frontier systems. The model is fully open-sourced under the MIT License and available through Hugging Face, ModelScope, and Z.ai’s developer platforms. Developers can deploy GLM-5 locally using inference frameworks like vLLM and SGLang, including support for non-NVIDIA hardware through optimization and quantization techniques. Through Z.ai, users can access both Chat Mode for fast interactions and Agent Mode for tool-augmented, multi-step task execution. GLM-5 also enables structured document generation, producing ready-to-use .docx, .pdf, and .xlsx files for business and academic workflows. With compatibility across coding agents and cross-application automation frameworks, GLM-5 moves foundation models from conversational assistants toward full-scale work engines.

Media

Media

Integrations Supported

Integrations Supported

Cheaper Inference
Cherry Studio
Claude Code
Claw Code
Cline
Dessix
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API Availability

Has API

API Availability

Has API

Pricing Information

$0.05 per million input tokens
Free Trial Offered?

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Subquadratic

Date Founded

2024

Company Location

United States

Company Website

subq.ai/introducing-subq-snap

Company Facts

Organization Name

Z.ai

Date Founded

2023

Company Location

China

Company Website

z.ai/

Categories and Features

AI Decision Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

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