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Model details

Sarvam 105B

Sarvam 105B is Sarvam AI's flagship open-weights reasoning model, released alongside the smaller Sarvam 30B on March 6, 2026. According to the official announcement, it was trained from scratch on large-scale, in-house curated datasets spanning pre-training, supervised fine-tuning, and reinforcement learning, with all training compute provided through the IndiaAI mission. The release covered the full stack, including tokenization, model architecture, execution kernels, scheduling, and inference systems, so the same weights can be deployed efficiently across flagship GPUs and consumer laptops.

Sarvam positions the 105B variant for complex reasoning, programming, and agentic workflows, where it currently powers the Indus assistant. The company reports strong performance on reasoning, code, and agentic benchmarks, and state-of-the-art results on Indian language evaluations relative to larger models. Because the weights are openly available, teams can self-host for private or regulated deployments, while hosted access through Sarvam remains an option for those who prefer an API, making the model a flexible choice for multilingual assistants, tool-using agents, and India-focused applications.

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Quick Info

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FastRouter
Model key
sarvam/sarvam-105b
Release date
Sep 1, 2025
Last updated
Sep 1, 2025
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.04
Output token cost
$0.16

Limits

Output tokens
131,072 tokens
Context window
131,072 tokens

Latest news about Sarvam 105B

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CoverageBenchmark

Artificial Analysis published a technical profile of Sarvam 105B (high), a reasoning-capable open-weights model released in March 2026. It scores 9 on the Artificial Analysis Intelligence Index, placing it above average among comparable open-weight models in the 40B-150B parameter medium size class (median 8). The mode Pricing for Sarvam 105B (high) is reported at $0.04 per 1M input tokens and $0.17 per 1M output tokens, with a 38% cache discount, and is characterized as moderately priced versus comparable open-weights models. Artificial Analysis notes the page shows the reasoning variant of the model and that a non-reasoning variant

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