Sarvam-105B

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

At a Glance

Key Features

Learn More

For detailed information on architecture, training methodology, performance benchmarks, and inference optimizations, visit our blog.

Model Specifications

Conversational variant

Model ID sarvam-105b-conversations is a post-trained variant of Sarvam-105B for real-time conversational workloads: voice agents, chatbots, and multi-turn dialogue where natural, colloquial Indic responses matter more than deep reasoning traces.

Both model IDs share the same OpenAI-compatible request schema and Indian language coverage, but sarvam-105b-conversations has a smaller 32K context window (vs. 128K for sarvam-105b). Pass the model ID in the model field, everything else stays the same.

from sarvamai import SarvamAI
client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
response = client.chat.completions(
    model="sarvam-105b-conversations",
    messages=[
        {"role": "user", "content": "who are you?"}
    ],
    temperature=1,
    top_p=1,
    max_tokens=100,
)
print(response.choices[0].message.content)

Key Capabilities

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)

Limits

Known Limitations

Next Steps