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What is Vynaris?

Vynaris equips teams with powerful hosted models that are unfiltered and specifically tailored for sanctioned security evaluations, red teaming activities, and investigative research. These models, including Qwen3.8-27B, DeepSeek-V4-Flash-0731, and Qwen3.6-35B-A3B, are available through an OpenAI-compatible API, complete with publicly available token pricing and a commitment to not retaining prompts or outputs. Furthermore, Vynaris improves user experience by routing requests to a broader selection of models and providing detailed cost breakdowns for each request, enabling applications to effortlessly switch between models by simply modifying the base URL while also allowing users to keep track of the costs incurred for each individual request. This cutting-edge approach not only enhances adaptability but also fosters greater transparency in usage expenses for both developers and teams, thereby improving overall operational efficiency. By offering these capabilities, Vynaris ensures that teams can conduct their activities with confidence and clarity.

What is Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next is a pioneering open-weight multimodal Mixture-of-Experts architecture that offers an initial look at the design meant for its successor, Qwen4. This model has been expertly crafted to enhance various aspects such as attention mechanisms, residual pathways, embeddings, and optimization strategies, thereby increasing its overall functionality, enhancing computational efficiency, expanding its model capacity, and ensuring stability during training. Its unique hybrid structure combines Gated DeltaNet, which effectively condenses historical information, with Qwen Sparse Attention, facilitating the selection of meaningful context on a micro-block scale to reduce both attention and indexing expenses for lengthy sequences. The Gated Residual feature enhances the residual pathway by incorporating four streams, which helps in dynamically regulating the information flow across different layers. Moreover, the N-gram Embedding cleverly merges large-scale local-pattern memory with minimal computational overhead for each token, with the capability to transfer to host memory for added efficiency. The entire model is built around a main network comprising 125 billion parameters, supplemented by an additional 51 billion parameters specifically for N-gram embeddings, activating only 6 billion parameters for each token processed. This advanced framework underscores the continuous evolution in machine learning architectures, laying the groundwork for exciting future innovations, and it exemplifies the increasing sophistication and potential of multimodal models in various applications.

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Integrations Supported

Integrations Supported

Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
ModelScope
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud

API Availability

API Availability

Has API

Pricing Information

$5 minimum credit top-up

Pricing Information

$2 per 1M (input)

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub

Company Facts

Organization Name

Vynaris

Date Founded

2025

Company Location

United Kingdom

Company Website

vynaris.com

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

qwen.ai/blog

Categories and Features

LLM API

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

Multimodal Models

Not specified

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