Mayura vs Sarvam-Translate: Which Indian Translation API

Introduction

Sarvam AI ships two separate translation models, and most developers pick the wrong one on the first try. Mayura vs Sarvam-Translate is not a "which one is better" question, it is a "which one matches your text" question, and the two models are built for opposite jobs.

Developers hit this decision the moment they need to translate anything beyond a demo. A chatbot needs casual, code-mixed replies that sound like a real person texting in Hinglish. A government document pipeline needs formal, structured, legally precise output in Odia or Konkani. Using the wrong model for either job produces translations that are technically correct and completely wrong for the context, stiff government language in a chatbot, or loose colloquial phrasing in a legal contract.

This post breaks down exactly what separates Mayura from Sarvam-Translate, which languages each one actually supports, and gives you a decision framework so you stop guessing and start routing requests to the right model on the first API call.

🎯 Quick Answer (30-Second Read)

• Main solution: Use Mayura for casual, code-mixed, or conversational translation across 11 major Indian languages. Use Sarvam-Translate for formal, structured translation across all 22 constitutionally scheduled Indian languages.
• When to use Mayura: Chatbots, WhatsApp-style messaging, social media content, customer support, anything with mixed English-Hindi phrasing.
• When to use Sarvam-Translate: Legal documents, government communication, enterprise reports, anything requiring precise formal language across a broader language set.
• Main benefit: Mayura gives you output script control (Roman, native, spoken) and four translation styles. Sarvam-Translate gives you 11 additional low-resource languages that Mayura does not support at all.
• Limitation: Sarvam-Translate only supports formal mode, no code-mixed or colloquial output, and no output_script control for transliteration.
• Recommendation: Route by content type and target language, not by habit. Most production apps end up calling both models depending on the request.

What Actually Separates the Two Models

Mayura and Sarvam-Translate are not two versions of the same model. They were built with different priorities, and the API documentation makes that split explicit rather than incidental.

Mayura supports translation across Hindi, Bengali, Tamil, Telugu, Gujarati, Kannada, Malayalam, Marathi, Punjabi, Odia, and English, all bidirectional with English. On top of language coverage, Mayura exposes four distinct translation styles: formal, modern-colloquial, classic-colloquial, and code-mixed. The code-mixed mode is the one most developers actually need and do not know exists, it produces output that mirrors how people genuinely speak, mixing English and the target language within the same sentence instead of forcing a pure translation into one language.

Sarvam-Translate takes the opposite bet. It covers all 22 scheduled Indian languages under the Eighth Schedule of the Constitution, including languages Mayura does not touch at all: Assamese, Bodo, Dogri, Konkani, Kashmiri, Maithili, Manipuri, Nepali, Sanskrit, Santali, Sindhi, and Urdu. The tradeoff is that Sarvam-Translate only operates in formal mode. There is no colloquial setting, no code-mixed setting, and the output_script parameter that controls transliteration on Mayura is not supported on Sarvam-Translate at all, if you need script control, the documentation directs you back to Mayura.

Decision Flow: Which Model to Call

The routing logic comes down to two questions: what tone does the content need, and does the target language fall inside Mayura's 11-language set or does it require Sarvam-Translate's broader 22-language coverage.

Step-by-Step: Picking the Right Model for Your Use Case

Step 1: Check your target language first. If you need Assamese, Bodo, Dogri, Konkani, Kashmiri, Maithili, Manipuri, Nepali, Sanskrit, Santali, Sindhi, or Urdu, the decision is already made. Sarvam-Translate is the only option, since Mayura does not cover these languages at all.

Step 2: If your language is inside Mayura's 11-language set, look at tone. A support chatbot, a social media caption generator, or a WhatsApp-style messaging feature needs output that sounds like a real conversation, not a government notice. This is where Mayura's code-mixed and colloquial modes matter, they are the difference between a translation that reads naturally and one that reads like it came out of a dictionary.

Step 3: Check whether you need script control. Mayura supports Roman, native, and spoken output scripts, so you can render Hindi in Devanagari or in Roman transliteration depending on your UI. Sarvam-Translate does not expose this parameter at all, the documentation is explicit that script control lives only on Mayura.

Step 4: For formal, structured content, decide based on scale, not just current need. If you are translating legal documents, compliance text, or enterprise reports and you might expand into more Indian languages later, Sarvam-Translate's 22-language coverage saves you a migration down the line. If you are staying within the 11 major languages and want formal tone, Mayura's formal mode works fine and gives you the option to add script control later without switching models.

Step 5: Set numerals_format explicitly on either model. Both Mayura and Sarvam-Translate support international or native numeral formatting, defaulting to international (0-9) unless you specify native, which renders numbers using the target language's own numeral system.

The Better Way vs The Worst Way

The worst way: Default to whichever model your team integrated first and use it for everything. This is how chatbots end up sounding like legal notices, and how legal documents end up with casual code-mixed phrasing that undermines the formality the content actually needs. It also means teams building for a wider language set discover mid-project that Mayura simply does not support the language they need, forcing a late migration.

The better way: Build a thin routing layer at the API call level that checks target language and content type before choosing the model. This is a few lines of logic, not a rewrite, since both models sit behind Sarvam's translation endpoint with a model parameter you can set per request. Teams that do this once never have to think about it again, the router handles Mayura vs Sarvam-Translate the same way a good LLM router handles picking between a fast model and a reasoning model.

My Take

The real reason Sarvam split translation into two models instead of shipping one generalist is that formal and colloquial Indian-language text are almost different tasks, not different settings on the same task. A model tuned to translate government notices faithfully will produce stilted, over-formal output on a WhatsApp message, and a model tuned for natural code-mixed conversation will lose precision on legal terminology. The best case here is developers actually use both models as intended and end up with translation quality that beats generic engines like Google Translate on both ends, formal and casual, instead of settling for a single mediocre middle ground. The worst case is teams pick one model out of habit and never realize their chatbot sounds like a legal document or their compliance translations sound like a text message. Right now most Indian-language AI products still treat translation as an afterthought bolted onto an English-first product, when it should be a first-class routing decision made per request. Where this heads is translation becoming another routed model choice inside the same infrastructure teams already use for LLM and STT routing, and the products that treat language coverage as a real engineering decision instead of a checkbox are the ones that will actually win Indian-language users.

Comparison Table

Real Developer Use Case

A customer support platform building a Hindi-English chatbot for e-commerce initially integrated Sarvam-Translate because it appeared first in Sarvam's model list and covered more languages on paper. Every response came back stiff and overly formal, phrasing that sounded like a legal disclaimer instead of a support agent replying to a customer asking about a delayed order.

The team switched the chatbot-facing translation calls to Mayura with mode set to code-mixed, and kept Sarvam-Translate active only for their separate compliance and terms-of-service translation pipeline, which needed the broader 22-language coverage for regional legal filings. The chatbot responses immediately read as natural Hinglish instead of translated English, and their compliance documents stayed precise and formal across languages Mayura does not even support. The fix was a one-line change in which model parameter their translation service called, not a new integration.

Frequently Asked Questions

Can I use Mayura and Sarvam-Translate in the same product?
Yes, and most production apps end up doing exactly this. Route casual, conversational, or chatbot-facing content to Mayura, and route formal, legal, or structured content to Sarvam-Translate, often within the same application depending on the request type.

Does Sarvam-Translate support code-mixed Hinglish output?
No. Sarvam-Translate only operates in formal mode. If you need code-mixed or colloquial output, Mayura is the only option since it exposes those style parameters directly.

Why does Sarvam-Translate cover more languages than Mayura?
Sarvam-Translate was built to cover all 22 constitutionally scheduled Indian languages, including lower-resource languages like Santali and Sanskrit that Mayura does not support, while Mayura focuses on the 11 most widely spoken languages with deeper style and script control.

Can I control whether output uses Devanagari or Roman script?
Only with Mayura. The output_script parameter supports Roman, native, and spoken transliteration styles. Sarvam-Translate does not expose this parameter, so output stays in the target language's native script only.

Which model is cheaper for high-volume translation?
Both models are priced per character in INR through Sarvam's API, and pricing is comparable across the two. The cost difference in practice comes from picking the wrong model and needing rework, not from a meaningful per-character gap between them.

Conclusion

Use Mayura when your content is conversational, code-mixed, or needs script control, chatbots, social media, and customer support all fall here. Use Sarvam-Translate when your content is formal, structured, or needs to reach beyond Mayura's 11 languages into the full 22 scheduled Indian languages, legal and government content fits this case directly. The one takeaway that matters: this is not a "pick one model" decision, it is a routing decision, and the products that route correctly are the ones whose translations actually sound right to the people reading them.

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