Sarvam Prism is a decision model that processes context and questions against fixed answer sets and returns probabilities, with web and API access.
Why it matters
API Docs
API Available
overview
Sarvam Prism is a decision-model tool that enables developers to evaluate context and questions against fixed answer sets and receive probabilities for the available choices. It is intended for decision-making applications and is available through an API. The listed model is Sarvam 105B, with text as its stated modality and a 131,072-token context window.
features
Sarvam Prism is described as a decision model rather than a general-purpose prose-generation tool. Its specified output is a set of probabilities over a fixed answer set, based on supplied context and a question.
use cases
Sarvam Prism is relevant to teams building applications that need to select among fixed choices using context and return probability estimates. Sarvam also describes broader use of its AI products for Indian-language and population-scale applications.
how to use
Start with Sarvam's API documentation and use the API to submit context, a question, and a fixed set of answer choices. The supplied product information does not specify a particular endpoint, request schema, or authentication procedure.
pricing
Sarvam is identified as freemium. The supplied pricing data lists input tokens at $0.000048 per 1,000 tokens and output tokens at $0 per 1,000 tokens. It does not describe the free tier's limits or specify whether those token rates apply to every Sarvam product. Text-to-speech, speech-to-text, translation, and Document AI are listed as contact-for-pricing services, rather than specified Prism plans.
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Similar Tools
Sarvam Prism is described as a model that reasons over context and returns probabilities for fixed choices. The alternatives listed below use different approaches; the supplied product information does not establish benchmark results or direct performance comparisons.
A dedicated System One model designed strictly to evaluate arbitrary state and schema questions in parallel and return typed choices and probabilities without generating prose.
Jev pioneered the dedicated API decision-model category and offers fully managed sub-second inference, but it is proprietary cloud software unlike Prism's open weights.
Uses vector embeddings to match queries to predefined routes and categories deterministically in sub-millisecond lookups with zero generative LLM calls.
It is extremely lightweight and runs locally with almost no hardware demands, but it decides based purely on semantic similarity rather than reasoning over complex unstructured context or multi-variable state.
Guarantees deterministic, structured outputs from standard LLMs by applying regex and CFG-constrained decoding directly to model logits at generation time.
Outlines lets you enforce strict categorical choices or Pydantic schemas using any local or hosted LLM, but it relies on token generation which is inherently slower and more compute-heavy than single-pass scoring heads.
Wraps standard provider LLM APIs with Pydantic validation to enforce rigid schemas, enum selections, and automated retries.
You avoid managing custom decision-model weights or dedicated FP8 hardware, but decisions rely on standard text-generation latency and vendor API token pricing.
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