> For clean Markdown of any page, append `.md` to the page URL. > For a complete documentation index, see https://docs.sarvam.ai/llms.txt. > For full documentation content in one file, see https://docs.sarvam.ai/llms-full.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.sarvam.ai/_mcp/server. # Sarvam-105B > Sarvam-105B - 105B parameter flagship multilingual language model delivering state-of-the-art performance on Indian language understanding, reasoning, and generation tasks. **Sarvam-105B (Flagship Chat LLM)** Sarvam AI's flagship Mixture-of-Experts reasoning model trained from scratch, with Multi-head Latent Attention (MLA) for efficient long-context inference. Matches or outperforms most open and closed-source frontier models of its class across knowledge, reasoning, and agentic benchmarks. **Highlights:** * **105B+ total parameters** — our most capable MoE model with Multi-head Latent Attention * Pre-trained on **12 trillion tokens** across code, math, multilingual, and web data * **98.6 on Math500**, **88.3 on AIME 25** (96.7 with tools), **49.5 on BrowseComp** * State-of-the-art Indian language performance: wins 90% of pairwise comparisons * Powers **Indus**, Sarvam's AI assistant for complex reasoning and agentic workflows * OpenAI-compatible chat completions API | Apache 2.0 open-source ## Key Features Wins 90% of pairwise comparisons across Indian language benchmarks and 84% on STEM, math, and coding. Trained extensively on native script, romanized, and code-mixed inputs across the 10 most-spoken Indian languages. 98.6 on Math500, 88.3 on AIME 25 (96.7 with tools), 85.8 on HMMT, and 69.1 on Beyond AIME — reflecting deep multi-step reasoning and complex mathematical problem solving. 49.5 on BrowseComp and 68.3 on Tau2 (avg.) — highest among compared models. Optimized for tool use, long-horizon reasoning, and environment interaction in real-world workflows. Mixture-of-Experts Transformer with 128 sparse experts and Multi-head Latent Attention (MLA), a compressed attention formulation that reduces memory requirements for long-context inference. ## Learn More For detailed information on architecture, training methodology, performance benchmarks, and inference optimizations, visit [our blog](https://www.sarvam.ai/blogs/sarvam-30b-105b). ## Model Specifications ## Choosing Between Sarvam Models | Feature | Sarvam-30B | Sarvam-105B | | ---------------------------- | ---------------------------------------- | ---------------------------------------------- | | **Total Parameters** | 30B (2.4B active) | 105B+ | | **Architecture** | MoE + GQA | MoE + MLA | | **Pre-training Data** | 16T tokens | 12T tokens | | **Best for** | Real-time deployment & conversational AI | Maximum quality, reasoning & agentic workflows | | **Math500** | 97.0 | 98.6 | | **AIME 25** | 88.3 | 88.3 (96.7 w/ tools) | | **BrowseComp** | 35.5 | 49.5 | | **Indian Language Win Rate** | 89% avg | 90% avg | | **Inference** | H100, L40S, Apple Silicon | Server-centric (H100) | Choose **Sarvam-30B** for a balanced performance-to-cost ratio and real-time conversational workloads, and **Sarvam-105B** when you need the highest quality outputs for complex reasoning and agentic tasks. **Sarvam-M (24B)** has been [deprecated](/api-reference-docs/getting-started/models/sarvam-m) and is no longer available through the API. ## Key Capabilities Simple, one-turn interaction where the user asks a question and the model replies with the highest quality response leveraging its 105B parameter knowledge. ```python from sarvamai import SarvamAI client = SarvamAI( api_subscription_key="YOUR_SARVAM_API_KEY", ) response = client.chat.completions( model="sarvam-105b", messages=[ {"role": "user", "content": "Explain the economic impact of GST implementation in India."} ], temperature=0.5, top_p=1, max_tokens=2000, ) print(response.choices[0].message.content) ``` ```javascript import { SarvamAIClient } from "sarvamai"; const client = new SarvamAIClient({ apiSubscriptionKey: "YOUR_SARVAM_API_KEY", }); async function main() { const response = await client.chat.completions({ model: "sarvam-105b", messages: [ { role: "user", content: "Explain the economic impact of GST implementation in India.", }, ], temperature: 0.5, top_p: 1, max_tokens: 2000, }); console.log(response.choices[0].message.content); } main(); ``` ```bash curl -X POST https://api.sarvam.ai/v1/chat/completions \ -H "Authorization: Bearer $SARVAM_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "messages": [ {"role": "user", "content": "Explain the economic impact of GST implementation in India."} ], "model": "sarvam-105b", "temperature": 0.5, "top_p": 1, "max_tokens": 2000 }' ``` Involves multiple exchanges between the system, user, and assistant. Sarvam-105B excels at maintaining deep context across long conversations. ```python from sarvamai import SarvamAI client = SarvamAI( api_subscription_key="YOUR_SARVAM_API_KEY", ) response = client.chat.completions( model="sarvam-105b", messages=[ {"role": "system", "content": "You are a senior legal advisor specializing in Indian corporate law."}, {"role": "user", "content": "What are the key compliance requirements for a startup in India?"}, {"role": "assistant", "content": "Key compliance requirements include company registration under the Companies Act 2013, GST registration, PF/ESI registration for employees, and annual filings with MCA."}, {"role": "user", "content": "Can you elaborate on the annual filing requirements and deadlines?"} ], temperature=0.3, top_p=1, max_tokens=2000 ) print(response.choices[0].message.content) ``` ```javascript import { SarvamAIClient } from "sarvamai"; const client = new SarvamAIClient({ apiSubscriptionKey: "YOUR_SARVAM_API_KEY" }); async function main() { const response = await client.chat.completions({ model: "sarvam-105b", messages: [ { role: "system", content: "You are a senior legal advisor specializing in Indian corporate law." }, { role: "user", content: "What are the key compliance requirements for a startup in India?" }, { role: "assistant", content: "Key compliance requirements include company registration under the Companies Act 2013, GST registration, PF/ESI registration for employees, and annual filings with MCA." }, { role: "user", content: "Can you elaborate on the annual filing requirements and deadlines?" } ], temperature: 0.3, top_p: 1, max_tokens: 2000 }); console.log(response.choices[0].message.content); } main(); ``` ```bash curl -X POST https://api.sarvam.ai/v1/chat/completions \ -H "Authorization: Bearer $SARVAM_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "messages": [ {"role": "system", "content": "You are a senior legal advisor specializing in Indian corporate law."}, {"role": "user", "content": "What are the key compliance requirements for a startup in India?"}, {"role": "assistant", "content": "Key compliance requirements include company registration under the Companies Act 2013, GST registration, PF/ESI registration for employees, and annual filings with MCA."}, {"role": "user", "content": "Can you elaborate on the annual filing requirements and deadlines?"} ], "model": "sarvam-105b", "temperature": 0.3, "top_p": 1, "max_tokens": 2000 }' ``` Stream responses token-by-token for real-time output. Ideal for chat interfaces and applications requiring progressive response rendering. ```python from sarvamai import SarvamAI client = SarvamAI( api_subscription_key="YOUR_SARVAM_API_KEY", ) for chunk in client.chat.completions( model="sarvam-105b", messages=[ {"role": "user", "content": "Write a detailed analysis of India's digital transformation journey."} ], temperature=0.7, max_tokens=2000, stream=True, ): if chunk.choices: delta = chunk.choices[0].delta if delta.content: print(delta.content, end="", flush=True) print() ``` ```javascript import { SarvamAIClient } from "sarvamai"; const client = new SarvamAIClient({ apiSubscriptionKey: "YOUR_SARVAM_API_KEY" }); async function main() { const stream = await client.chat.completions({ model: "sarvam-105b", messages: [ { role: "user", content: "Write a detailed analysis of India's digital transformation journey." } ], temperature: 0.7, max_tokens: 2000, stream: true, }); for await (const chunk of stream) { if (chunk.choices?.[0]?.delta?.content) { process.stdout.write(chunk.choices[0].delta.content); } } console.log(); } main(); ``` ```bash curl -X POST https://api.sarvam.ai/v1/chat/completions \ -H "Authorization: Bearer $SARVAM_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "messages": [ {"role": "user", "content": "Write a detailed analysis of India'\''s digital transformation journey."} ], "model": "sarvam-105b", "temperature": 0.7, "max_tokens": 2000, "stream": true }' ``` ## Next Steps Learn how to integrate chat completion into your application. Complete API documentation for chat completion endpoints.