Guide
Best Open-Source LLM for Hindi and Indian Languages in 2026
Last updated: October 2, 2026 — model data is refreshed automatically from the id8 dataset
If your product must work in Hindi and your data has to stay on your own servers, you need an open-weight model. Published Hindi benchmarks are scarce, so this guide combines the id8 Hindi ranking with a test method you can run yourself.
All models ranked for Hindi, with a self-host filter.
See the Hindi ranking →The honest state of Hindi evaluation
For English there are dozens of public benchmarks. For Hindi there is no benchmark that most vendors report. Of the 164 models on the id8 leaderboard, only 2 have a published multilingual score. Our Hindi grade therefore combines human-preference ratings, the multilingual scores that exist, and a review of Devanagari and Hinglish output.
That makes the ranking a starting point. The test plan further down is what will tell you which model works for your product.
Leading open-weight models for Hindi
| # | Model | Provider | Grade | Price in / out per 1M | Context |
|---|---|---|---|---|---|
| 1 | DeepSeek V4-Pro | DeepSeek | B | $0.435 / $0.87 | 1M |
| 2 | Gemma 4 31B | B | — | 256K | |
| 3 | DeepSeek V4-Flash | DeepSeek | B | $0.14 / $0.28 | 1M |
| 4 | Nemotron 3 Ultra 550B A55B | NVIDIA | B | — | 262K |
| 5 | Kimi K3 | Moonshot AI | B | $3 / $15 | 1M |
| 6 | MiMo-V2.5-Pro | Xiaomi | B | $0.435 / $0.87 | 1M |
| 7 | GLM-5.1 | Zhipu AI | B | $1.4 / $4.4 | 203K |
| 8 | DeepSeek V4.1 Flash | DeepSeek | B | $0.15 / $0.6 | 1M |
Grades run from S to D. A dash in the price column means the model has no listed API price and is self-hosted.
For comparison: the leaders including closed models
| # | Model | Provider | Grade | Price in / out per 1M | Context |
|---|---|---|---|---|---|
| 1 | Gemini 2.5 Pro | A | $1.25 / $10 | 1M | |
| 2 | GPT-6 Astra | OpenAI | A | $10 / $50 | 1M |
| 3 | Claude Fable 5 | Anthropic | B | $10 / $50 | 1M |
| 4 | Gemini 3.1 Pro | B | $2 / $12 | 1M | |
| 5 | Claude Opus 4.8 | Anthropic | B | $5 / $25 | 1M |
If your data can go to an API, the closed leaders are generally stronger in Hindi than open-weight models of a size you can host yourself. The reason to choose open weights is control: privacy, fixed cost, and no dependence on a provider.
What decides Hindi quality in an open model
Training data. Models trained on large multilingual corpora write more natural Hindi. Vendors that publish multilingual results usually invested here.
Tokenizer. A tokenizer that splits Devanagari into many pieces makes every request slower and more expensive, and fills the context window sooner.
Size. Below roughly 10B parameters, models tend to lose the thread in long Hindi answers. Mid-size and large models hold formal Hindi much better.
Fine-tuning. A general model fine-tuned on your own Hindi data often beats a larger general model on your task. See Can I Fine-Tune LLM? for the hardware needed.
A test you can run in an afternoon
Write 30 prompts from your real use, covering:
| Type | Example |
|---|---|
| Formal Devanagari | Summarise a government notice in Hindi |
| Everyday Devanagari | Reply to a customer complaint politely |
| Hinglish in roman letters | "mera order kab aayega?" |
| Mixed script | Hindi sentence with English product names |
| Long output | A 400-word explanation |
| Instruction in English, answer in Hindi | "Explain this in simple Hindi" |
For each model, check:
- Script matching. Does it answer in the script the user wrote in?
- Grammar. Gender and number agreement across a long answer.
- Naturalness. Does it read like Hindi, or like translated English?
- Staying in Hindi. Does it slip into English halfway?
- Token count. How many tokens did the same answer take?
Ask a native reader to score the answers without knowing which model wrote them.
Hinglish is its own problem
Most real messages in India are neither pure Hindi nor pure English. Models see far less romanized Hindi in training than Devanagari. Many open-weight models answer a Hinglish question in English, or in Devanagari the user did not ask for. If your users type Hinglish, weight that part of the test most heavily.
Hardware
Running a capable multilingual model locally needs a GPU with enough memory for the weights and the context. Because Hindi uses more tokens, you will also want a larger context than for the same English workload. Use Can I Run LLM? to check a specific model against your GPU, and see Best Local LLM by VRAM for what fits at each memory size.
Practical recommendation
- Shortlist the top three open-weight models from the table that fit your hardware.
- Run the 30-prompt test.
- If none is good enough, fine-tune the best one on a few thousand examples of your own Hindi data before moving to a larger model.
Frequently asked questions
Which open-source LLM is best for Hindi?
On the id8 Hindi ranking as of October 2, 2026, the leading open-weight models are DeepSeek V4-Pro, Gemma 4 31B and DeepSeek V4-Flash. Hindi benchmarks are sparse, so test the top few on your own prompts.
Which LLM is best for Hindi overall, including closed models?
The top three for Hindi across all models are Gemini 2.5 Pro, GPT-6 Astra and Claude Fable 5.
Why does Hindi use more tokens than English?
Most tokenizers are trained mostly on English and split Devanagari words into several pieces. The same sentence commonly takes two to four times as many tokens in Hindi.
Can a small model handle Hindi?
Small models can follow simple Hindi prompts but often drift into English or mix scripts in longer answers. Larger multilingual models are more reliable.
Is there a standard Hindi benchmark?
Not one that is reported consistently across models. Only 2 of the models we track have a published multilingual score, which is why your own test set matters.