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Average Ratings 0 Ratings

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ease
features
design
support

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Description

Qwen3.5 represents a major advancement in open-weight multimodal AI models, engineered to function as a native vision-language agent system. Its flagship model, Qwen3.5-397B-A17B, leverages a hybrid architecture that fuses Gated DeltaNet linear attention with a high-sparsity mixture-of-experts framework, allowing only 17 billion parameters to activate during inference for improved speed and cost efficiency. Despite its sparse activation, the full 397-billion-parameter model achieves competitive performance across reasoning, coding, multilingual benchmarks, and complex agent evaluations. The hosted Qwen3.5-Plus version supports a one-million-token context window and includes built-in tool use for search, code interpretation, and adaptive reasoning. The model significantly expands multilingual coverage to 201 languages and dialects while improving encoding efficiency with a larger vocabulary. Native multimodal training enables strong performance in image understanding, video processing, document analysis, and spatial reasoning tasks. Its infrastructure includes FP8 precision pipelines and heterogeneous parallelism to boost throughput and reduce memory consumption. Reinforcement learning at scale enhances multi-step planning and general agent behavior across text and multimodal environments. Overall, Qwen3.5 positions itself as a high-efficiency foundation for autonomous digital agents capable of reasoning, searching, coding, and interacting with complex environments.

Description

Sarvam-30B is an advanced open-source large language model that serves as a comprehensive platform for real-time conversational AI and complex reasoning tasks, emphasizing its capability in multilingual settings and practical usage. This 30-billion parameter model is engineered for enhanced speed and efficiency through a Mixture-of-Experts (MoE) framework, which selectively activates a portion of its parameters for each request, thus facilitating high throughput and minimal latency while remaining suitable for environments with limited resources, including local devices and edge computing systems. It excels in various conversational applications, programming tasks, and logical reasoning, achieving impressive outcomes in over 20 Indian languages, which underscores its utility for multilingual applications and voice interaction systems. The model features a dual-tier structure, acting as a rapid and deployable "conversational workhorse," and utilizes MoE techniques to lower computational costs without sacrificing performance. This innovative model not only enhances user experience but also broadens accessibility in diverse linguistic contexts.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

APIFree
Alibaba Cloud Model Studio
Claw Code
Hugging Face
Ollama
OpenClaw
Qwen
Qwen3.5-Plus
Sarvam AI
Together AI
ZooClaw

Integrations

APIFree
Alibaba Cloud Model Studio
Claw Code
Hugging Face
Ollama
OpenClaw
Qwen
Qwen3.5-Plus
Sarvam AI
Together AI
ZooClaw

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
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

Alibaba

Founded

1999

Country

China

Website

qwen.ai

Vendor Details

Company Name

Sarvam

Founded

2023

Country

India

Website

www.sarvam.ai/blogs/sarvam-30b-105b

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