India’s constitution lists 22 languages, and hundreds more are spoken day to day, written in several different scripts. One person might text in Hindi typed with Latin letters, speak Tamil at home and read official forms in English. For a system trained mostly on English web pages, that is a mess. It is also a big opportunity, which is why national and corporate efforts to build Indian language models keep appearing.
Here is what the work actually involves, and how you can tell whether a result is any good.
Problem one: the data is thin and uneven
Indian language models learn from text like any others. English has an enormous supply online. Hindi has a decent amount. Languages such as Odia, Assamese or Santali have far less digital text, and much of what exists is locked in scanned documents or images of pages.
Teams respond in a few ways. They collect and clean what exists: news archives, government publications, books and community-contributed collections. They transcribe speech, since a lot of language lives in audio. They use machine translation to stretch a small dataset, accepting that translation errors can leak into the model. And they pay native speakers to write and review examples, which is slow and costly but usually the highest quality.
Quality matters more than raw volume here. For a language with little text, a small clean dataset often beats a large noisy one.
Problem two: tokenization eats the budget
Indian language models do not read letters or words directly. They break text into tokens, chunks learned from training data. If the tokenizer was built mostly on English, it shatters a Malayalam sentence into many tiny fragments. The consequences are practical: the same sentence uses more tokens, fills the context window faster, runs slower and costs more to serve.
A purpose-built tokenizer that treats common word pieces in Indian languages as single units can make a model cheaper and faster for those users. It is one of the least glamorous decisions in the project and one of the most useful.
Problem three: code-mixing and scripts
Real speech does not stay inside one language. People blend Hindi and English in a single sentence, a habit often called code-mixing, and they frequently type in romanized form because keyboards make it easier. A model tested only on clean, formal text will stumble on the way people really write messages. The better Indian language models are evaluated on casual, mixed and transliterated input, not just textbook sentences.
Adapting a model versus training from scratch
There are two broad routes.
Continue training an existing model. Start with a capable base model and train it further on Indian-language data. This is cheaper and faster and often gets strong results.
Train from scratch. Build the whole thing, tokenizer included, around your target languages. It costs far more in compute and talent, but you control the data, the licensing and the design.
Which route suits Indian language models depends on the goal. A company that wants a support assistant in five languages will probably adapt. A government or large enterprise that cares about independence may accept the extra expense of building its own, a motive we also discuss in our look at sovereign AI compute.
Compute is part of the story for Indian language models either way. Training and serving these systems takes specialized chips in large clusters, so whoever has access to that hardware shapes who can build what. That is why telecom operators, cloud providers and chip makers often partner on national projects. I will not guess at the details of any specific deal, but the structure is common: one side brings data centers and customers, the other brings hardware and software.
How to judge whether Indian language models are any good
Marketing claims about language support can be loose. A few checks help.
- Test it in a language you know. Ask for a summary of a news article, a polite email and a simple reasoning puzzle. Watch for grammar slips, unnatural phrasing and sudden drifts into English.
- Read the benchmark fine print. Find out whether the test material was written natively or translated from English. Translated tests can flatter a model.
- Check register and dialect. Formal written Hindi differs from how people talk in a given city. A model may be good at one and awkward at the other.
- Ask about safety. Moderation tools trained on English often miss slang and harmful content in other languages. That is a known gap.
Why this matters beyond the technology
Many people in India reach the internet mainly through a phone, and many prefer speaking to typing. Services that work well in their own language, such as banking help, farming advice or assistance with government forms, can reach people poorly served by English-only tools. Our post on why chat apps built for Indian languages matter covers the user side of the same story.
Three things will show whether Indian language models mature. Are datasets shared openly so smaller teams can build on them? Do the models handle voice as well as text? Can smaller, cheaper versions run on modest hardware? If all three happen, language support stops being a headline feature and becomes ordinary infrastructure, which is where it belongs. For background on the linguistic diversity involved, see the Wikipedia article on languages of India. Until then, the best test of any Indian language models remains simple: try them in the language you actually speak.
