When we started building Jivam, we evaluated every major frontier model. Claude, GPT-4o, Gemini, Llama — we ran them all through the same battery of tests. Sarvam-105B won on the dimensions that matter most for Indian developers, and we want to explain exactly why.
What “reasoning” actually means
The word gets thrown around a lot. For us, it has a specific meaning: can the model hold multiple constraints in mind simultaneously, work through a problem step by step, and arrive at a correct answer without hallucinating intermediate steps?
Most models are good at this for simple problems. Sarvam-105B is consistently good at this for hard problems — the kind that require juggling business logic, language semantics, and technical correctness all at once.
The multilingual edge
India is linguistically diverse in a way that English-first models simply weren’t designed for. Code comments in Hindi. Documentation that switches between Tamil and English. User requirements described in Telugu, implemented in Python.
Sarvam-105B handles this natively. Not through translation — through genuine multilingual understanding trained at scale on Indian language data.
What this means in practice
In our internal testing, Sarvam-105B outperformed alternatives on:
- Fixing bugs in codebases with mixed-language comments
- Understanding domain-specific requirements described in Indian languages
- Reasoning about Indian regulatory and compliance contexts
- Code generation that accounts for Indian infrastructure constraints (slower networks, specific payment gateway APIs, etc.)
These aren’t edge cases. They’re the daily reality of building software for Indian users and markets.