Perplexity announced on November 4, 2026 that web grounding is available immediately for its Decisions API, adding optional live web searches before the model returns probabilities. The feature targets decisions whose answers can change after training, including a person’s executive role, a software package’s latest release or whether an event remains scheduled.
The Decisions API accepts content as state and returns structured numeric answers instead of prose. A yes-or-no question produces the probability of yes; a choice question produces a probability for every defined option; and a score question produces probabilities across an ordered rubric plus an expected score. Developers can use those values as thresholds in classification, routing and other automated decisions. State can contain text, JSON or images, and the launch also allows a URL that points the model toward a specific page.
Live Search Before the Model Answers
Setting mode to web_grounding activates Fast Search in Perplexity’s API gateway before the model call. The gateway builds one query per question from the question and the beginning of the state text, runs searches in parallel, merges results and removes duplicate URLs. Questions producing the same query share a search. The gateway then appends page titles, URLs and text to the state as evidence. The answers remain probability-based, while a search_results field lets developers inspect what the model saw.
The searches have explicit limits. As many as 10 run concurrently, with a one-second limit for each search and three seconds overall. Images are not searched. Queries go to the Perplexity Search API and can contain both question text and the opening portion of state, making the data sent to search part of the integration’s privacy considerations.
Search failures do not necessarily stop a decision response. If only some searches succeed, the model uses the results that arrived. If none succeeds, it answers from the original state and returns an empty search_results array. A response can therefore retain the normal answer structure even when live evidence did not contribute to it.
Accuracy Gains and Measurement Differences
Perplexity reported testing the same decision model on 328 true-or-false claims drawn from October 2026 news and rare Wikipedia facts. Each true claim had a false counterpart differing in one detail, and no evidence was supplied in state. Accuracy was 56.4% without grounding and 97.3% with it, while errors fell from 143 to nine. Median latency increased from 139 ms to 366 ms, and 95th-percentile latency from 514 ms to 742 ms, placing a measurable speed cost beside the accuracy gain.
Parallel Web Systems said its PWS-Decisions benchmark contains 300 closed-ended questions: 100 public with reference answers and 200 private. Scoring uses exact match, and the set was curated for difficulty rather than to estimate accuracy on average traffic. Parallel’s leaderboard reports mean accuracy and sample standard deviation across three runs evaluated October 29, 2026, including 51.00% public-set accuracy for Perplexity Decisions.
Perplexity said pplx-decider-v1.2 with web grounding answered 86 of the 100 public questions correctly in a November 4, 2026 run, which it described as the highest public-set accuracy against that published leaderboard. Its client measured median latency of 370 ms and 95th-percentile latency of 685 ms on those 100 questions. Parallel’s separate latency measurements cover complete answers across three runs of the full 300-question set, so the two sets of latency figures have different scopes.
Pricing and Integration
A grounded request can include up to 100 questions. Each completed search costs $0.001, putting search charges at no more than $0.10 for a 100-question request, but retrieved evidence is also billed as input tokens and counts toward the 256K input limit. The standard pplx-decider-v1.2 model costs $0.02 per million input tokens and pplx-decider-flash-v1.2 costs $0.01 per million; output tokens are free.
Question grouping affects the final token bill. On the standard model, evidence is counted again when input limits force questions into separate groups. Flash evaluates every question independently and bills the state and evidence again each time. The cost of grounding therefore depends on the number of searches, the amount of text retrieved and how the selected model batches questions.
Integration currently has a typing caveat. Perplexity’s Python library 0.44.0 or later and TypeScript SDK 0.39.0 or later support the API, but their types do not yet expose mode and search_results. Python passes the mode through extra_body, while TypeScript sends it in the request body.
Perplexity’s suggested workloads include checking whether accounts have raised funding in the past 90 days, watching dependencies for stable releases newer than a pinned version and confirming an event is still scheduled before an agent books it. In those workflows, grounding adds inspectable current evidence to a probability that software can threshold or review. The tradeoff extends beyond accuracy: latency, token usage, search fees and any missing evidence in search_results all shape what the returned probability represents.
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