Sarvam AI · llm model

Sarvam Prism

Sarvam Prism is Sarvam AI's latest language model in our dataset, released Nov 3, 2026. Open weights (Apache 2.0). Sarvam AI's open-weights decision model built on Sarvam 105B, released Nov 3, 2026: it returns calibrated probabilities for fixed answer options (routing, triage, moderation, judging agent runs) instead of generating text. Verified against vendor sources on Nov 4, 2026.

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Primary source: https://www.sarvam.ai/blogs/sarvam-prism. We are the fast, independent index; the vendor’s own page is the source of record for these model facts.

Sarvam Prism at a glance: 32,768-token context window; takes text in.

  • ReleasedNov 3, 2026
  • Context window33K32,768 tokens
  • LicenceOpen weightsApache 2.0
InTextOutnot verified

Key facts

Vendor-reported facts are read from the linked vendor page on the date shown above; we did not run the model. Independently tested means we ran it; no fact on this page is. Editorial is our own classification, not a vendor claim. Where a linked page does not itself state a value, the row says so. How to read provenance.

Sarvam Prism: key facts with sources
FactValueLabelSource
VendorSarvam AIEditorialn/a
ReleasedNov 3, 2026 Date on Sarvam's release post.Vendor-reportedsource
Statusgenerally availableEditorialn/a
Context window32,768 tokens (33K) Hosted API limit per question (state + question + options). The release post says the model handles up to 131,072 tokens per question.Vendor-reportedsource
InputtextVendor-reportedsource
LicenceApache 2.0 (open weights)Vendor-reportedsource

Where Sarvam Prism sits in our data

Alternatives to Sarvam Prism

Language models from other vendors that share at least one of our groups with Sarvam Prism. Comparable on paper, from each model’s sourced facts; we have not run them.

  • Hydrogen-1.0Deep Principle · Both in: Open weights · released Nov 2, 2026
  • Clover-35B-A3B-PreviewDiffuSpace · Both in: Open weights · released Oct 30, 2026
  • SenseNova-Loop-9B-PreviewSenseNova · Both in: Open weights · 262K context · released Oct 30, 2026
  • LFM3-1.7BLiquid AI · Both in: Open weights · 256K context · released Oct 27, 2026
  • Nemotron 3.5 Bolt E4B (4GB)NVIDIA · Both in: Open weights · 64K context · released Oct 23, 2026
  • MiniMax M3.1 FlashMiniMax · Both in: Open weights · $0.15 in / $0.6 out per 1M tokens · 1M context · released Oct 19, 2026

Side by side: Best open-weight LLMs (Nov 2026): GLM-5.3 leads Terminal-Bench 4.0

What the sources say

These are claims from the linked pages, not our measurements.

  • Sarvam reports a 71.8 average across three decision indices at a 44 ms median response on one B200 GPU, the highest score under 100 ms among the 25 decision models it tested. Vendor claim; we did not run it.

Notes

  • Running the weights locally needs one NVIDIA GPU with FP8 support and about 120 GB of free memory (B200 or H200) and about 106 GB of disk, per the model card.

What we tested

Nothing yet. We have not run Sarvam Prism ourselves, so every fact on this page is Reported from the sources below. When we do test it, the scope will be stated here and in our test logs. See methodology.

Not verified, left out

We could not confirm these on a vendor page, so we do not state them.

  • Prism is a decision model, not a chat model: it returns a probability for each option you supply and never generates text, so it is not comparable with chat LLMs on our leaderboards.
  • Hosted API pricing is in rupees (₹4 per 1M input tokens, output not billed) and is not converted here.

Sarvam Prism FAQ

When was Sarvam Prism released?

Sarvam AI released Sarvam Prism on Nov 3, 2026. Source.

How large is the Sarvam Prism context window?

32,768 tokens. Source.

Is Sarvam Prism open weights?

Yes. The weights are published under Apache 2.0. Source.

Has Agentic Weekly tested Sarvam Prism?

Not yet. Every fact on this page is Reported: read from the linked vendor pages, not measured by us.

Sources

Compare and guides

We have no head-to-head page or guide that names Sarvam Prism yet. These hubs cover the models we do compare.

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