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Description

Inkling-Small is an efficient Mixture-of-Experts transformer model built to provide performance comparable to Inkling while using a much smaller active parameter footprint. The model has 276 billion total parameters and 12 billion active parameters, making it designed for strong capability with more efficient compute usage. Inkling-Small was trained on NVIDIA GB300 NVL72 systems and supports native reasoning across text, images, and audio. It offers context windows of up to one million tokens, making it suitable for long documents, large codebases, multimodal context, and extended agent workflows. Users can set reasoning effort from minimal to extra high to control the balance between speed, cost, compute, and task complexity. The model benefits from improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning. These improvements helped Inkling-Small surpass its larger counterpart on reasoning and coding benchmarks. Its encoder-free multimodal architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens. By combining efficient MoE scaling, long-context reasoning, multimodal input, coding strength, and adjustable thinking effort, Inkling-Small is built for practical high-performance AI deployment.

Description

Sarvam-105B stands as the premier large language model within Sarvam’s open-source lineup, engineered to provide exceptional reasoning capabilities, multilingual comprehension, and agent-driven execution all within a unified and scalable framework. This Mixture-of-Experts (MoE) model boasts an impressive total of approximately 105 billion parameters, activating only a subset for each token, which allows it to maintain superior computational efficiency while excelling in intricate tasks. It is particularly optimized for advanced reasoning, programming, mathematical challenges, and agentic processes, positioning it well for scenarios that necessitate multi-step problem-solving and organized outputs rather than merely engaging in basic conversations. With the ability to process long contexts of around 128K tokens, Sarvam-105B can effectively manage extensive documents, prolonged discussions, and complex analytical inquiries, ensuring coherence throughout. Additionally, its design facilitates a diverse range of applications, providing users with versatile tools to tackle a variety of intellectual challenges.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Hugging Face
Model Context Protocol (MCP)
Sarvam AI
Tinker

Integrations

Hugging Face
Model Context Protocol (MCP)
Sarvam AI
Tinker

Pricing Details

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens
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

Thinking Machines Lab

Founded

2025

Country

United States

Website

thinkingmachines.ai/news/inkling-small/

Vendor Details

Company Name

Sarvam

Founded

2023

Country

India

Website

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

Product Features

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