Hindi & Indian-language NLP API (India-first, ₹ pricing)

Hindi & Indian-language NLP API (OpenAI-compatible)

Build Hindi chatbots, sentiment analyzers, and text classifiers without specialized libraries. unoblox's Qwen3 and Claude handle Hindi, Marathi, Tamil, Telugu natively. Billed in ₹ rupees from India.

Here's the 3-step setup + real ₹ costs.

Why unoblox for Hindi NLP

• Native Hindi support: Qwen3 trained extensively on Hindi text
• Zero extra setup: Use the same OpenAI-compatible API
• Indian context: Named entities, slang, regional variants work
• Cost: Qwen3 Max at ₹120.95/1M (affordable for high-volume)

Recommended models

• Qwen3 Max (₹120.95 input / ₹604.77 output): Best for Hindi
• DeepSeek V3.2 (₹26.21 input / ₹38.30 output): Fastest, cheapest
• Claude Sonnet (₹201.6 input / ₹1,008 output): Superior context

Step 1: Sentiment analysis (Hindi)

from openai import OpenAI

client = OpenAI(
    base_url="https://api.unoblox.ai/v1",
    api_key="ub-gw-..."
)

response = client.chat.completions.create(
    model="qwen/qwen-3-max",
    messages=[
        {"role": "system", "content": "Analyze sentiment: 'positive', 'negative', or 'neutral'."},
        {"role": "user", "content": "यह प्रोडक्ट बहुत बढ़िया है! मुझे बहुत पसंद आया।"}
    ]
)

sentiment = response.choices[0].message.content
print(sentiment)  # Output: "positive"

Step 2: Named entity recognition (Hindi)

response = client.chat.completions.create(
    model="qwen/qwen-3-max",
    messages=[
        {"role": "system", "content": "Extract entities (person, location, organization) as JSON."},
        {"role": "user", "content": "राज दिल्ली में रहता है और Google में काम करता है।"}
    ]
)

entities = response.choices[0].message.content
print(entities)

Step 3: Text classification (Hindi)

response = client.chat.completions.create(
    model="deepseek-ai/deepseek-v3.2",
    messages=[
        {"role": "system", "content": "Classify into: [product_quality, shipping, customer_service]."},
        {"role": "user", "content": "मेरे पार्सल को 5 दिन हो गए। अभी तक नहीं आया।"}
    ]
)

category = response.choices[0].message.content
print(category)  # Output: "shipping"

Real ₹ cost: analyze 10,000 Hindi reviews

Assume ~150 input tokens per review (the review text) and ~10 output tokens (sentiment label + a short reason).

Using DeepSeek V3.2:

• Input: 1.5M tokens × ₹26.21 = ₹39.32 · Output: 0.1M tokens × ₹38.30 = ₹3.83
• Total: ₹43.15 for 10k reviews — ₹0.004 per review

Using Qwen3 Max:

• Input: 1.5M tokens × ₹120.95 = ₹181.43 · Output: 0.1M tokens × ₹604.77 = ₹60.48
• Total: ₹241.91 for 10k reviews — ₹0.024 per review

Common Hindi NLP tasks

Task	Example	Model
Sentiment	"बेहद पसंद आया" → "positive"	DeepSeek or Qwen3
Classification	"डिलीवरी देरी" → "shipping"	DeepSeek (fast)
Entity extraction	"अजय मुंबई से है" → Person: अजय	Qwen3
Summarization	500-word article → 2-line summary	Qwen3 or Claude
Translation	Hindi ↔ English	Qwen3 (fastest)
Q&A	"GST क्या है?" → Hindi answer	Claude (nuanced)

Tips for Hindi NLP

1. Encoding: Always use UTF-8
2. Prompt language: Write in English or Hindi—both work equally
3. Transliteration: Qwen3 handles both: "namaste" and "नमस्ते"
4. Regional support: Marathi, Gujarati all supported by Qwen3
5. Batch overnight: Process 1000s overnight without rate limits

Use-cases

• E-commerce: Analyze ₹ rupee reviews, auto-categorize complaints
• Fintech: Detect fraud in Hindi support chats
• EdTech: Auto-grade Hindi essays
• Customer support: Auto-route Hindi tickets by issue type
• Social media: Monitor sentiment in Hindi posts

Frequently asked questions

Q: Does Qwen3 understand Hindi slang? Yes. Trained on Reddit, Twitter, blogs in Hindi. Slang and idioms all work.

Q: Can I mix Hindi and English? Absolutely. Code-switching is natural: "मुझे यह laptop बहुत पसंद है."

Q: How about Hinglish (romanized Hindi)? Qwen3 recognizes both: "namaste" and "नमस्ते" treated the same.

Q: Accuracy on regional languages (Tamil, Telugu)? Qwen3 is strong on Hindi and Marathi, good on others. Test on your data for critical tasks.

Q: Can I fine-tune for domain-specific Hindi? No fine-tuning on unoblox. Use base models + few-shot prompts for domain adaptation.

Q: What about PII in prompts? Don't send PII (SSN, Aadhaar, bank details). Hash/mask sensitive data before sending.

Get started in rupees

Get started → https://unoblox.ai/sign-in