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About

Nomic Embed is a suite of open source, high-performance embedding models designed for various applications, including multilingual text, multimodal content, and code. The ecosystem includes models like Nomic Embed Text v2, which utilizes a Mixture-of-Experts (MoE) architecture to support over 100 languages with efficient inference using 305M active parameters. Nomic Embed Text v1.5 offers variable embedding dimensions (64 to 768) through Matryoshka Representation Learning, enabling developers to balance performance and storage needs. For multimodal applications, Nomic Embed Vision v1.5 aligns with the text models to provide a unified latent space for text and image data, facilitating seamless multimodal search. Additionally, Nomic Embed Code delivers state-of-the-art performance on code embedding tasks across multiple programming languages.

About

Qwen3.8-Flash-Next is an open-weight multimodal Mixture-of-Experts model and an early preview of the architecture planned for Qwen4. It systematically upgrades attention, residual connections, embeddings, and optimization to improve capability, computational efficiency, model capacity, and training stability. Its hybrid architecture combines Gated DeltaNet, which efficiently compresses historical information, with Qwen Sparse Attention, which selects important context at the micro-block level to reduce attention and indexing costs on long sequences. Gated Residual widens the residual stream into four branches and dynamically controls information flow across layers, while N-gram Embedding adds large-scale local-pattern memory with very little extra per-token computation and can be offloaded to host memory. The model uses a 125B-parameter main network plus 51B N-gram embedding parameters, while activating only 6B parameters per token.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Machine learning engineers and developers seeking a solution offering embedding models for multilingual text, multimodal content, and code applications

Audience

Developers, researchers, and AI teams seeking to run or study an efficient multimodal open-weight model with long-context reasoning, coding, multilingual, and agentic capabilities

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

$2 per 1M (input)
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Nomic
United States
www.nomic.ai/embed

Company Information

Alibaba
Founded: 1999
China
qwen.ai/blog

Alternatives

Alternatives

GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
Qwen3.5

Qwen3.5

Alibaba

Categories

Categories

Integrations

Python
Alibaba Cloud Model Studio
Baseten
Cherry Studio
Cline
ClinePass
Go
Happy Shrimp 1.0
Hermes Agent
Hugging Face
Java
Model Context Protocol (MCP)
Novita AI
Odysseus
Ollama
PHP
Qwen Studio
QwenCloud
QwenWork
Ruby

Integrations

Python
Alibaba Cloud Model Studio
Baseten
Cherry Studio
Cline
ClinePass
Go
Happy Shrimp 1.0
Hermes Agent
Hugging Face
Java
Model Context Protocol (MCP)
Novita AI
Odysseus
Ollama
PHP
Qwen Studio
QwenCloud
QwenWork
Ruby
Claim Nomic Embed and update features and information
Claim Nomic Embed and update features and information
Claim Qwen3.8-Flash-Next and update features and information
Claim Qwen3.8-Flash-Next and update features and information