Open-Weight Models for Hindi
Open-weight models can read and write Hindi, but quality varies far more than it does in English. The table above ranks every model with our Hindi grade; switch on the self-host filter to see only the models you can download and run yourself.
Three things decide how well an open-weight model handles Hindi: how much Hindi and other Indic text was in its training data, how efficiently its tokenizer encodes Devanagari, and its size. Larger multilingual models from vendors that publish multilingual scores are the safest starting point. Small models (under about 10B parameters) often drift into English after a few turns.
Models built specifically for Indian languages are worth testing alongside the general-purpose leaders. Public, like-for-like Hindi benchmark results for them are still scarce, so we do not rank a model on a claim we cannot source. Run your own evaluation on 20–30 real prompts from your product before you commit to one.
To check whether a model fits on your hardware, open it in the Can I Run LLM calculator.
API Models for Hindi Tasks
The API models at the top of the table are the strongest choices for Hindi today. They differ less in raw ability than in three practical points: how natural the output sounds, how well they hold formal Hindi over a long answer, and how many tokens they spend on Devanagari.
Naturalness. Models trained on a large amount of Indian web text use everyday vocabulary and phrasing. Others produce Hindi that is correct but reads like a translation. Ask for a short informal message and a short formal notice; the difference is obvious within two prompts.
Formal Hindi. Legal, government and educational text needs consistent grammatical agreement across long passages. Test with a 500-word output, not a sentence.
Latency and cost. For real-time chat, voice pipelines or tutoring, a smaller, faster model from the same vendor is usually the practical choice. Sort the table by price to compare the options graded A or better.
The Tokenizer Problem — Why Hindi Costs More
Here is the most underappreciated issue in Hindi LLM development: Hindi costs 3–4× more API tokens than the same content in English, and most teams don't discover this until they receive their first invoice.
Consider this concrete example. The English sentence "The capital of India is New Delhi" tokenizes to approximately 8 tokens with a typical English-first tokenizer. The identical sentence in Hindi — भारत की राजधानी नई दिल्ली है — tokenizes to 22–28 tokens depending on the model. That's a 3–3.5× multiplier for a simple factual sentence; complex, grammatically rich Hindi prose can push this to 4× or higher.
The root cause is structural. Most frontier model tokenizers were built using Byte Pair Encoding (BPE) trained on English-dominated corpora. Devanagari uses multi-byte Unicode characters — each akshara (syllabic unit) may be several bytes — and a BPE tokenizer trained on English has no incentive to merge common Devanagari character clusters into efficient single tokens. It falls back to splitting them into multiple sub-word pieces.
The good news: tokenizer efficiency differs between model families, and newer multilingual models encode Devanagari more compactly than older English-first ones. Before choosing, paste the same Hindi passage into each vendor's token counter. A saving of 15–20% per message adds up quickly for a chatbot that handles thousands of conversations a day.
Hinglish and Code-Mixed Text
Most benchmarks test Hindi in isolation — clean Devanagari, grammatically correct sentences. But that's not how most Indians actually type. Real-world Hindi on WhatsApp, Twitter, and customer support chats looks more like: "Mujhe ek best LLM chahiye jo Hindi mein kaam kare aur mera kaam easy ho jaye." This blend of romanized Hindi and English, often called Hinglish, is the dominant register for hundreds of millions of users.
Models struggle with Hinglish for a specific reason: training data rarely includes romanized Hindi in significant quantities. A model that has never seen "mujhe pata nahi kya karna hai" as a natural sentence may not recognize it as Hindi at all — it might respond in English, or produce broken Devanagari that doesn't match the user's intent.
The behaviour to look for is script matching: the model answers in Devanagari when the user writes Devanagari, in roman letters when the user writes romanized Hindi, and mixed when the user mixes. The leading API models do this consistently. Most small open-weight models do not — they fall back to plain English or standard Devanagari. If your users type in Hinglish, and most consumer apps in India will see it, test on romanized Hindi inputs before committing to a model.