- Sarvam AI says its Sarvam Vision model beats Gemini and ChatGPT on key OCR benchmarks
- The startup focuses on all 22 official Indian languages
- Its “sovereign AI” approach aims to build technology tailored specifically to India’s needs
ChatGPT, Gemini, and other AI chatbots are often very good at reading English and many other languages, but while they can interpret Hindi, they begin to wobble when confronted with more complex scripts or regional nuance among Indian languages.
Now, a Bengaluru startup called Sarvam AI is stepping up with models it says can outperform the global rivals when it comes to optical character recognition (OCR) and multilingual speech, particularly when it comes to the tongues of the sub-continent.
On Indian languages, Sarvam Vision is the best model by far, while supporting all 22 scheduled Indian languages pic.twitter.com/nM4Ujz0wvPFebruary 5, 2026
The Sarvam Vision and Bulbul V3 models are built with India’s linguistic complexity in mind. Sarvam Vision can interpret complex tables, understand charts, recognize text in real-world scenes, and generate captions, while Bulbul V3 handles the text-to-speech system. They support all 22 official Indian languages.
With 35 voices, Bulbul is able to always sound like a local. As many multilingual users know, the awkwardness of hearing their language pronounced as if it were a distant cousin of English can make someone reluctant to try the technology. A well-trained text-to-speech model that captures rhythm and tone more accurately can make people feel more comfortable using it.
And while OCR may not sound glamorous, it quietly powers everything from when you scan a document with your phone, upload a PDF, or digitize an old record. Garbled characters, misread names, and missing context can be a real issue. Sarvam says it will help small business owners and government offices convert records into searchable archives faster and more accurately than otherwise possible.
Sovereign AI
Sarvam AI calls itself a builder of sovereign AI. The idea is to distinguish itself from foreign platforms. With AI models spreading across government, business, and education, questions of who builds them and whose data they understand matter a lot. Sarvam wants to have tools tailored to India.
Sarvam’s emergence also nudges a larger conversation about where innovation originates. The AI boom has often been framed as a race among a few dominant players. Yet breakthroughs increasingly come from focused teams solving specific problems. Sarvam appears to have identified a gap in high-quality, language-rich OCR and speech systems for Indian scripts.
Of course, benchmarks are snapshots, not guarantees of performance, especially in the real world. The proof of Sarvam’s impact will lie in adoption. Plus, if Sarvam’s claims hold up, larger AI companies will feel pressure to improve their own support for more languages and scripts.
At its best, Sarvam AI’s story goes beyond beating Gemini or ChatGPT on a leaderboard and becomes a way of showing technology reflecting the people who use it. If AI is going to shape the next decade of digital life, it will need to speak many languages fluently and read more than just clean English text.
Sarvam is betting that attention to detail and cultural specificity can compete with sheer scale. For millions of users who have felt underserved by mainstream AI tools, that bet may feel more like a sure thing.
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