15 Indian AI Models: Features, Developers and Key Strengths

India is making significant progress in artificial intelligence (AI) research and development. Local technology companies, universities, and research organisations are creating AI models designed to support India's diverse population, languages, and industries.

These models perform different tasks. Some generate text, answer questions, and assist with programming. Others focus on speech recognition, language translation, document processing, and voice-based applications.

Indian AI development also focuses on supporting regional languages. This is important because India has many languages and dialects. Locally developed AI models can help more people access digital services in their preferred languages.

Here are 15 notable Indian AI models, their developers, features, and strengths.

Table of Indian AI Models

The following table highlights some of the notable AI models developed by Indian companies, research institutions, and technology organisations.

1. Sarvam 105B

Developer: Sarvam AI

Sarvam 105B is a large language model developed by Indian AI company Sarvam AI. It is designed to handle advanced language tasks, reasoning, and complex user instructions.

The model is part of India's efforts to develop locally trained foundation models.

Features:

• Supports advanced text generation.

• Handles complex questions and instructions.

• Supports reasoning and problem-solving tasks.

• Offers multilingual language capabilities.

• Supports programming and technical tasks.

• Uses a mixture-of-experts architecture.

Key strengths:

Sarvam 105B is designed for advanced AI applications. Its reasoning capabilities make it useful for research, programming, business analysis, and complex conversations.

Its development and training in India also support the country's goal of building independent AI infrastructure.

2. Sarvam 30B

Developer: Sarvam AI

Sarvam 30B is another large language model from Sarvam AI. It is designed for conversational AI, text generation, and multilingual applications.

It is smaller than Sarvam 105B, making it suitable for different deployment requirements.

Features:

• Supports text generation.

• Handles conversational tasks.

• Supports Indian language applications.

• Provides reasoning capabilities.

• Supports developer experimentation.

• Uses a mixture-of-experts architecture.

Key strengths:

Sarvam 30B offers a balance between model size and performance. It is suitable for businesses and developers interested in building AI-powered applications.

It can support customer service platforms, writing assistants, educational tools, and multilingual chatbots.

3. BharatGen Param2

Developer: BharatGen

BharatGen Param2 is an Indian foundation model developed through a research consortium led by IIT Bombay.

The project focuses on creating AI systems that understand India's languages, culture, and local requirements.

Features:

• Supports multiple Indian languages.

• Uses a mixture-of-experts architecture.

• Supports multilingual text processing.

• Designed for Indian research applications.

• Supports development of local AI solutions.

Key strengths:

Its main strength is its focus on Indian languages and local applications. It can support research, education, public services, and other applications that require Indian language understanding.

It also contributes to India's sovereign AI development efforts.

4. Krutrim

Developer: Ola Krutrim

Krutrim is an Indian AI technology initiative associated with Ola. It focuses on developing AI models and applications for Indian users.

Its model development includes language understanding and generative AI capabilities.

Features:

• Supports language processing.

• Provides generative AI capabilities.

• Supports Indian language applications.

• Offers developer-oriented AI technology.

• Includes models designed for different AI tasks.

Key strengths:

Krutrim's main strength is its focus on building AI technology for Indian users. Its development considers local languages and the needs of India's technology market.

It is relevant to businesses exploring locally developed AI systems.

5. Hanooman

Developer: SML India and BharatGPT ecosystem

Hanooman is a multilingual generative AI initiative designed to support users across different Indian languages.

The project aims to make AI services more accessible to people who do not primarily communicate in English.

Features:

• Supports multilingual AI applications.

• Provides conversational capabilities.

• Supports text-based interactions.

• Targets Indian language users.

• Designed for different industry applications.

Key strengths:

Hanooman's major strength is its multilingual approach. It aims to support communication between users and digital services in different languages.

This can be useful in education, customer service, and public information systems.

6. Airavata

Developer: AI4Bharat, IIT Madras

Airavata is an AI language model developed by AI4Bharat. It focuses on Hindi language understanding and instruction-following applications.

It is part of wider research efforts to improve access to AI technology in Indian languages.

Features:

• Supports Hindi language interactions.

• Provides instruction-following capabilities.

• Supports text generation.

• Can assist with question answering.

• Supports academic AI research.

Key strengths:

Airavata is useful for developers and researchers working on Hindi-language applications.

It also provides a foundation for studying how large language models can be adapted to languages with different writing systems and linguistic structures.

7. OpenHathi

Developer: Sarvam AI

OpenHathi is an Indian language model project focused on Hindi. It was developed to improve the performance of AI systems in Hindi and related language applications.

It is an earlier generation of Sarvam's language model development.

Features:

• Focuses on Hindi language processing.

• Supports text generation.

• Supports multilingual experimentation.

• Can be used for AI research.

• Provides opportunities for model adaptation.

Key strengths:

OpenHathi is important because it demonstrates efforts to adapt advanced language models to Indian languages.

It can support developers creating Hindi chatbots, writing applications, and educational platforms.

8. IndicBERT

Developer: AI4Bharat

IndicBERT is a language understanding model developed to support Indian languages.

Unlike general-purpose conversational models, it is mainly designed for natural language processing tasks that require understanding and analysing text.

Features:

• Supports multiple Indian languages.

• Performs text classification.

• Supports language understanding.

• Can assist with information extraction.

• Supports academic and commercial NLP applications.

Key strengths:

IndicBERT is useful for developers building systems that need to classify, analyse, and understand Indian language content.

It can support search engines, document analysis, language research, and automated text processing.

9. IndicBART

Developer: AI4Bharat

IndicBART is a sequence-to-sequence language model designed for Indian language applications.

It supports tasks that involve transforming existing text into new text.

Features:

• Supports Indian language text processing.

• Performs text generation.

• Supports text summarisation.

• Can assist with translation-related tasks.

• Supports natural language processing research.

Key strengths:

Its main strength is its ability to transform and generate text. It can be useful in summarisation systems, language processing applications, and research projects.

10. IndicTrans2

Developer: AI4Bharat

IndicTrans2 is a machine translation model designed to translate between Indian languages and English.

It addresses the need for better translation technology across India's many languages.

Features:

• Supports all 22 scheduled Indian languages.

• Supports translation between Indian languages.

• Supports English-to-Indian-language translation.

• Supports Indian-language-to-English translation.

• Provides tools for developers and researchers.

Key strengths:

IndicTrans2 is particularly useful for multilingual communication.

Businesses can use translation technology to reach customers who speak different languages. Educational institutions can also use it to make learning materials more accessible.

11. Pragna

Developer: Soket AI

Pragna is a small language model developed by Soket AI. It is designed to support language processing and generative AI applications.

Its relatively small model size makes it relevant to developers exploring more efficient AI deployments.

Features:

• Supports multilingual text processing.

• Provides language generation capabilities.

• Designed for smaller AI deployments.

• Supports developer experimentation.

• Focuses on language accessibility.

Key strengths:

Pragna's main advantage is its smaller model size. Smaller models can require fewer computing resources than larger foundation models.

This makes them useful for developers and businesses exploring cost-conscious AI applications.

12. Gnani Voice Models

Developer: Gnani.ai

Gnani.ai develops voice AI technology for businesses and organisations.

Its models focus on speech processing, voice interactions, and multilingual communication.

Features:

• Supports speech recognition.

• Provides voice-based interactions.

• Supports Indian language applications.

• Enables automated voice assistance.

• Supports business communication systems.

Key strengths:

Gnani's voice technology is useful for businesses that want to automate customer conversations.

It can support contact centres, customer support systems, and voice-enabled digital assistants.

13. BharatGPT

Developer: CoRover

BharatGPT is a conversational AI initiative developed by CoRover. It focuses on creating virtual assistants and conversational applications for different industries.

The platform aims to make digital services easier to access through natural language interactions.

Features:

• Supports conversational AI.

• Provides chatbot capabilities.

• Supports multiple Indian languages.

• Can be used for customer support.

• Supports business and public service applications.

Key strengths:

BharatGPT is useful for organisations that want to introduce automated customer assistance.

It can help answer questions, provide information, and guide users through digital services.

14. Vachana

Developer: Gnani.ai

Vachana is associated with India's growing voice AI development ecosystem. It focuses on speech and voice technology for Indian language applications.

Features:

• Focuses on voice AI infrastructure.

• Supports speech-related applications.

• Targets Indian language communication.

• Supports voice-based digital services.

• Can contribute to automated interactions.

Key strengths:

Its main strength is its focus on voice technology. This is important in markets where many users prefer speaking instead of typing when accessing digital services.

15. AI4Bharat IndicWav2Vec

Developer: AI4Bharat, IIT Madras

IndicWav2Vec is a speech representation model developed for Indian language research and applications.

It focuses on learning useful information from speech recordings. This supports the development of speech recognition systems.

Features:

• Supports speech representation learning.

• Designed for Indian language applications.

• Supports speech recognition research.

• Can assist with multilingual speech processing.

• Supports development of speech technology.

Key strengths:

IndicWav2Vec contributes to the development of speech recognition systems for Indian languages.

It is useful for researchers and developers working on voice assistants, transcription tools, and speech-based applications.

Benefits of Indian AI Models

Indian AI models offer several important benefits to businesses, developers, researchers, and ordinary users.

1. Better Indian Language Support

Many models focus on Indian languages. This helps users communicate with technology in languages they understand.

2. Support for Local Businesses

Businesses can use locally developed AI systems to build customer service platforms, chatbots, translation tools, and automated communication systems.

3. Education and Research

AI models can support educational platforms, research institutions, and students through language processing, translation, and information retrieval.

4. Lower Language Barriers

Translation and voice recognition models can help people access information even when they do not speak English.

5. Development of Local Technology

Indian AI development helps strengthen the country's technology industry and reduces dependence on foreign AI infrastructure.

India has a growing collection of artificial intelligence models designed for different applications. Sarvam 105B, Sarvam 30B, BharatGen Param2, and Krutrim represent important developments in foundation model research.

Other projects, including IndicTrans2, IndicBERT, Airavata, and Gnani's voice models, address specialised needs in translation, language understanding, and speech processing.

These models have different capabilities and are not direct replacements for one another. Some are designed for general AI assistance, while others focus on specific language and speech tasks.

As research continues, Indian AI models are expected to play an important role in education, business, communication, public services, and digital innovation.

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