Models
Sarvam AI provides a purpose-built AI stack for building applications in Indian languages. Our models span speech-to-text, speech translation, text translation, and high-quality text-to-speech, designed specifically for India’s linguistic diversity, accents, and real-world usage patterns.
Each model is trained and evaluated on Indian languages and culturally grounded data, enabling higher accuracy in production scenarios. With simple, well-documented APIs and predictable performance, developers can build, deploy, and scale India-first AI experiences without managing model complexity.
New to building for Indian languages? Start with Building for Indian Languages, a practical guide to language coverage, code-mixing, scripts, native numerals, 8kHz telephony audio, and pronunciation control.
Model Selection Guide
Sarvam Models: trained for Indian languages:
Open-Weight Models
Open-weight models are available on /v2 using the same Sarvam API key and credits.
GLM-5.3, Gemma 4 31B, and DeepSeek V4 Flash are available in beta and rolling out gradually. See Access to Beta APIs.
Read more about the open-weight models →
Language Support Overview
Language coverage varies by model. Check the table below before picking one. Full per-model tables are linked from each model’s own page.
23-language set (Saaras v4, Sarvam Translate, Sarvam Vision)
11-language set (Bulbul v3, Mayura, Sarvam-105B)
Use Cases
Voice Assistant
Content Localization
Call Center Analytics
Educational Platform
Document Processing
Build a multilingual voice assistant
- Speech Input: Use Saaras v4 with
mode="transcribe"to convert user speech to text - Understanding: Process with Sarvam-105B for intelligent responses
- Speech Output: Convert responses to speech with Bulbul
Perfect for customer service, smart home devices, and accessibility applications.