Sarvam Prism vs Jev: Two System One Models, Two Very Different Bets

On November 3, 2026, Sarvam AI released Prism, a model that does not write a single word of text. It answers questions by scoring fixed options in one pass and returning a probability for each one. The launch post names the model that created this category: Jev, TypeSafe AI's System One decision model, in early access since September. Prism is a direct challenge to Jev, and Sarvam ran the comparison itself, timing the models and publishing the scoreboard.

The two models are easy to confuse and hard to choose between. Both skip token-by-token generation and promise calibrated confidence scores a workflow can trust. But they differ on the questions that decide a purchase: speed, honesty of the confidence, whether you can run the model yourself, and how much independent evidence backs the claims. Every number from a vendor's own testing is flagged below.

The idea both models share

The shared idea comes from Daniel Kahneman's distinction between fast, intuitive System 1 thinking and slow, deliberate System 2 reasoning. A System One model handles the small, well-scoped decisions inside software: routing a support ticket, checking whether a claim matches a retrieved document, flagging a risky tool call, or scoring an agent's run against a rubric. It never generates prose. You send it a state, a question, and a fixed set of options, and it returns a choice, a position on a scale, or the probability that a statement is true. Because the valid answers are declared in advance, there is nothing to parse, the probabilities are the model's own, and one forward pass answers every question about a state at once.

Sarvam Prism is a LoRA fine-tune of Sarvam's 105B model, built for enterprise decisions such as support routing, document analysis, and evaluating AI agents. Jev is TypeSafe AI's commercial model in the same category, with three question types: Choice picks one of up to 255 options, Score places a state on an ordered rubric, and Noul returns the probability that a yes/no statement is true. Jev entered early access on September 15, 2026; Prism followed seven weeks later, on November 3.

Speed and accuracy: Prism's numbers, on Prism's home turf

Sarvam's headline claim is a point on a speed-accuracy frontier: Prism averages 71.8 across three decision indices while answering in 44 milliseconds on one NVIDIA B200 GPU. In the same test, Jev 1.14 averages 64.5, and Perplexity Decider v1.2 averages 68.0 at 117 milliseconds. Prism is the highest-scoring model answering in under 100 milliseconds, but not the highest overall: mammoth-1 averages 75.2 at 284 milliseconds per request.

Every number here comes from Sarvam's own testing, measured differently for each model. Sarvam timed the models it could run on its own B200 hardware over 1,000 requests. Jev 1.14 and Microsoft-Decision-1 are API-only, so Sarvam plotted their publicly reported times, 196 and 85 milliseconds; measured from Sarvam's own cluster, they took 431 and 533 milliseconds.

The three indices are different kinds of test. The first is the Jev Decision Index, 37 public benchmarks: Prism 67.0, Jev 60.7. The second is Sarvam's recreation of the Artificial Analysis Decision Index, with the private knowledge test replaced by curated hard questions that include an "I don't know" option: Prism 65.9, Jev 61.9. The third is Sarvam's own Indian-language benchmark: Prism 82.5, Jev 70.8. Prism's lead is widest where Sarvam built the test itself, and narrowest on the public index, exactly the pattern a vendor-run evaluation produces.

Calibration: where the two evaluations disagree

Calibration decides whether either model can automate decisions safely: a workflow acts only above a confidence threshold, which works only if the confidence means what it says. Sarvam reports Prism's errors stay within its confidence bounds at every threshold from 50% to 95%. For answers at 90% confidence or higher in English, Prism expects 3.3% errors and makes 2.2%. Jev 1.14 expects 1.3% and makes 3.5%, underestimating its error rate by a factor of 2.7. In Indian languages Jev expects 3.4% and makes 16.1%, a factor of 4.8.

An independent evaluation of Jev does not fully support that charge. Researchers at the University of Bonn and the Lamarr Institute evaluated Jev 1.13.0 on 37 public datasets, 346,009 requests in total, and found its choice probabilities well calibrated, good enough to support selective prediction. The same paper found a real weakness in the same area: Jev's binary probabilities need thresholds tuned on training data, raising micro-F1 on one legal dataset from 0.50 to 0.75. The two evaluations agree that calibration is Jev's sensitive spot and disagree about how bad it is.

The honest reading is that calibration depends on the test set. Sarvam's test leans on its own Indian-language benchmark, where Jev is weakest. The Bonn paper's test leans on public classification datasets, where Jev is strongest. If you plan to automate decisions above a threshold, the only calibration number that matters is the one measured on your own labelled data, for both models, before you switch anything on.

One consistency result is harder to argue with: changing the order of the options can change a model's answer. In Sarvam's 2,000-question test, Jev 1.14 changed 12.3% of answers when the options were shuffled, and 5.6% even when the same request was simply re-sent. Prism changed 4.0% when shuffled; Sarvam's fix, sorting options into one fixed order before scoring, brought that to zero of 2,000, with no extra computation and no accuracy loss.

Deployment: open weights against an API key

Here the two models stop being comparable and start being different products. Prism's weights are published on Hugging Face under the Apache 2.0 licence, so an enterprise can fine-tune the model on its own decisions and run it on its own infrastructure. The price is hardware: Prism runs in FP8 on one GPU with at least 120 GB of memory. Sarvam also offers it as a hosted API on Sarvam Indus, with no published price.

Jev is API-only, with no self-hosted option and no weight release. What you get instead is a published price and no infrastructure to run: $0.042 per million input tokens, output free, at the time the Bonn researchers wrote their paper. Their evaluation of 346,009 requests cost under $10, so a million routine decisions cost tens of dollars, not thousands. Jev accepts up to 32,000 tokens for the state plus the longest question, against Prism's 131,072, so long agent traces fit in one Prism call where Jev would need them split.

The choice here is about control, not quality. Prism suits teams that need to own the model: regulated data, decisions that need fine-tuning on internal outcomes, or Indian-language workloads where its lead is largest. Jev suits teams that want a priced API with no GPU to manage and value the independent evidence base more than self-hosting.

Where Jev still holds the stronger independent record

Prism's numbers are all Sarvam's own, and no independent evaluation has been published yet. Its security benchmark, 89.0% on 5,517 internal questions across nine tasks, ahead of Clef at 84.5% and Jev 1.14 at 78.6%, is also internal, though Sarvam says it will release the set publicly. Until then, Prism's lead rests on one lab's word.

Jev has been through an independent wringer. The Bonn evaluation found 95 to 99% accuracy on IMDB, SST-2, HellaSwag, and ARC, 86.7% on Belebele across 122 languages, and wins over Qwen3.8-27B on 27 of 37 datasets and over Gemma-4 on all 37. The same paper is candid about where Jev degrades: low-resource languages, fine-grained or noisy labels, rubric-based judgments, and sequential decisions, with Jev completing 35% of unseen ALFWorld tasks against Qwen's 54.5%. A decision model is not an agent, and neither should be judged on multi-step tasks it was not built for.

Prism's agent-trace evaluation is the one head-to-head result that uses outside labels rather than another model's judgment. On 700 recorded agent runs and 1,770 questions, Prism scored 78.5% overall against Jev's 76.4% and Claude Haiku 5.5's 70.7%, including 75.0% on judging task success and 86.4% on deciding whether a run needs human review. Prism reads a trace once and scores five questions together in about half a second per run, against a median 2.5 seconds and $0.64 per 1,000 runs for Claude Haiku 5.5 through its API, a genuine operational advantage at agent scale.

Verdict: who should pick which

Pick Prism if you need to self-host or fine-tune, if your decisions run in Indian languages, if your latency budget is under 100 milliseconds, or if you are evaluating agent runs at a volume where half a second per trace and no per-token bill change the economics. Its open weights and 131,072-token context are real structural advantages, not benchmark decorations.

Pick Jev if you want a priced API with no infrastructure, if you value the independent evaluation record over the newest launch numbers, or if your decisions are mostly English classification and routing, where the Bonn paper's results apply most directly. Its $0.042 per million input tokens and free output make a trial trivial: a few dollars buys hundreds of thousands of decisions to test against your own labels.

Whichever you pick, test both on your own data before automating anything. The two evaluations here disagree about Jev's calibration and agree only that the numbers move with the test set. Run the same hundred labelled decisions through each model and let that table, not a launch post, make the decision.

Sources

• Sarvam AI — sarvam․ai
• Unite.AI — unite․ai
• arXiv — arxiv․org