India Has Its Own AI Now — But How Good Is It Really?
You’ve heard of ChatGPT, Claude, Gemini, and Grok. These are the global giants of artificial intelligence, built by billion-dollar American companies. But quietly, India has built its own: Sarvam AI — a full-stack sovereign AI platform built entirely in India, for India. The question everyone is asking: Is it powerful enough to compete? The honest answer might surprise you.
What Is Sarvam AI?
Sarvam AI describes itself as “India’s Full-Stack Sovereign AI Platform.” It is built on sovereign Indian compute, operated entirely within India, and designed specifically for Indian languages, culture, and scale. It is not just a chatbot — it is a complete platform offering speech recognition, text-to-speech, translation, vision, and conversational AI. Companies like Tata Capital are already using Sarvam to power multilingual customer interactions across their loan products — reaching customers in their own languages, at scale.
The Real Benchmark Data — No Marketing, Just Numbers
Sarvam published detailed technical results for their flagship model, Sarvam-M (24 billion parameters), in May 2025. Here is what the numbers actually show, compared to other open models of similar size:
| Benchmark | What It Tests | Sarvam-M | Gemma 3 (27B) | Llama 3.3 (70B) |
|---|---|---|---|---|
| MMLU | General knowledge across 57 subjects | 87% | 90% | 92% |
| ARC-C | Logical reasoning | 95% | 93% | 93% |
| GSM-8K | Math word problems | 94% | 93% | 95% |
| HumanEval | Coding ability | 88% | 88% | 85% |
| LiveCodeBench | Advanced coding | 44% | 30% | 39% |
| MMLU-IN | General knowledge in Indian languages | 79% | 75% | 74% |
| GSM-8K-IN | Math reasoning in Indian languages | 92% | 89% | 86% |
| Sarvam-M is a 24B parameter model. Llama 3.3 is a 70B model — nearly three times larger. Yet Sarvam beats it on coding and Indian language tasks. That is a remarkable efficiency achievement. |
Where Sarvam Leads — And Where It Doesn’t
Where Sarvam AI is genuinely the best: Supporting all 22 scheduled Indian languages natively is something no other major AI platform does. Its speech models (Bulbul V3 for text-to-speech, Saaras V3 for speech recognition) handle code-mixed language like Hinglish naturally. Its translation model handles regional expressions and colloquial speech that global models consistently struggle with. On the benchmark for Indian language conversations (MILU-IN), Sarvam-M scores 75% versus Gemma 3’s 65% and Llama 3.3’s 60% — a 10–15 percentage point advantage. That gap is enormous in practice. Where global models still have an edge: On broad English general knowledge (MMLU), GPT-4o and Claude 3.5 score around 88–90%, while Sarvam-M scores 87%. The gap is small — about 1–3 percentage points — but it exists. This is partly because Sarvam’s fine-tuning deliberately prioritises Indian language data over English trivia breadth. Global models also have far larger parameter counts and broader training datasets. For complex multi-step reasoning in English, very large models like GPT-4o still hold a structural advantage due to sheer size.
The Big Picture: Why This Matters for India
Consider this: India has 1.4 billion people and 22 official languages. Most global AI tools are built for English-first users. Sarvam is building for the billion Indians who think, speak, and work in languages other than English. There are three things that make Sarvam strategically unique beyond benchmark scores: Data Sovereignty. Sarvam runs entirely on Indian servers. For government agencies, regulated industries like banking and insurance, and any business concerned about data leaving Indian borders, this is not optional — it is essential. ChatGPT, Claude, and Gemini all process data on American servers. Deployment Flexibility. Sarvam offers private cloud, on-premise, and air-gapped deployments. This means a government department or a hospital can run Sarvam entirely within their own infrastructure with zero external data exposure. Population-Scale Design. Sarvam is not built for individual productivity — it is built to run at the scale of hundreds of millions of users across India’s diverse population.
Sarvam AI is not trying to replace ChatGPT or Claude for an English-speaking professional writing emails or debugging code. That is not the point. Sarvam is building the AI infrastructure for India — the same way India built UPI for payments, Aadhaar for identity, and IRCTC for railways. It is sovereign, scalable, and specialised for a market that global players have consistently underserved. For a 24-billion-parameter model focused on Indian languages, its benchmark performance is extraordinary. And as Sarvam continues to release more model drops and improvements, the gap with global giants on general reasoning is narrowing fast. India’s AI moment is here. Sarvam is leading it.
Sources: Sarvam AI official technical blog (sarvam.ai), Sarvam-M benchmark results (May 2025)