AI decision models are fast and cost-effective, but they only know what users send them and what they learned in training. For questions about changing facts, like whether someone is still a company’s CEO or a product has a new release, a model needs up-to-date information to answer accurately.
Starting today, Perplexity’s Decisions API can now search the web before it answers.
Add one setting, "mode": "web_grounding" to any Decisions API request, and Perplexity Fast Search runs one query for each question. The results are added to your state as evidence, so the decision model returns probabilities informed by what is on the web right now. State can also be a URL to point the model at a specific web page rather than enter its text.
On PWS-Decisions, a public benchmark with 100 questions that depend on current web facts, accuracy rose from 51% to 86% with typical responses in under 400 ms.
How a decision model works
A decision model returns a number as an answer to each question. Users send content as state (text, JSON, or images) with one or more questions. For a noul question (yes or no), the result is the probability of yes. For a choice question, the result is a probability for each option a user defines. For a score question, the result is a probability for each level of your rubric and the expected score.
The model has two sources of information: the state a user sends and what it learned in training. That’s enough for a question like "Does this review report a defect?" It’s not enough to answer, "Is this person still the CEO?" The answer may have changed after training, and nothing in the state says so. Web grounding adds a third source: the web, searched at the time of the request. The state can also be a URL, so a user can point the model at a web page instead of text.
How web grounding works
Grounding runs in the API gateway before the model call. For each question in a request, the gateway builds a Fast Search query. The queries run in parallel. The gateway merges the results, drops duplicate URLs, and appends the evidence to the state: title, URL, and page text. If the state is a URL, the search query carries that URL, so the page itself comes back as evidence and the model answers from its text. The model returns probabilities the same as it would for any Decisions API request. If a search fails or times out, the model answers from whatever evidence came back. The response includes search_results so a user can view what the model saw. A user’s request and the shape of the response stay the same. What changes is the evidence the model has when it answers.
Testing web grounding
We sent 328 true-or-false claims to the same decision model twice, once with web grounding and once without. The claims were split between October 2026 news and rare facts from Wikipedia, and each true claim had a false twin that differed in one detail. No evidence was supplied in the state.
Without web grounding, the Decisions API answered 56.4 percent of claims correctly, close to guessing. With web grounding, it answered 97.3% correctly, cutting errors from 143 to 9. The tradeoff is speed: median latency rose from 139 ms to 366 ms, and 95th-percentile latency from 514 ms to 742 ms.
PWS-Decisions
PWS-Decisions is a public benchmark of closed-ended questions about account qualification, product capabilities, policy rules, and fast-moving events. 100 questions are public and scored by exact match against a reference answer. We ran them against the Decisions API + Web Grounding (pplx-decider-v1.2) on November 4, 2026, and it answered 86 correctly. The other results below are the benchmark authors' published leaderboard. Their 51% for the Decisions API without Web Grounding matches our own run.
Decisions API + Web Grounding requests returned in 370 ms at the median and 685 ms at the 95th percentile, measured from our client on the 100 public questions. The benchmark authors report 560 ms and 710 ms for their API on the full 300-question set, measured from their client on October 29, 2026. Against the published leaderboard, the Decisions API + Web Grounding has the highest public-set accuracy and a median response under 400 ms.
What developers can build
Enable web grounding to answer questions data can't: when the facts changed after training, and there’s new information published on the web. Some examples:
- Qualify accounts on recent news. "Raised funding in the last 90 days?" across an entire account list.
- Track the tools you depend on. "Is there a stable release newer than our pinned version?" reviewed on a schedule.
- Confirm before an agent acts. "Is this event still on?" checked before it books.
Each answer is a probability a developer’s code can act on, with the web sources behind it.
Get started
Add "mode": "web_grounding" to any Decisions API request. The state can be text, such as a claim or a record, or a URL.
Check a claim against the web:
bash
curl -X POST https://api.perplexity.ai/v1/decisions \ -H "Authorization: Bearer $PERPLEXITY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "pplx-decider-v1.2", "mode": "web_grounding", "state": "Claim: The James Webb Space Telescope launched in December 2021.", "questions": { "supported": { "type": "noul", "instructions": "Is the claim true?" } } }'
The response adds search_results, the pages the model read. On November 4, this returned a probability of yes above 0.99, with NASA's spacecraft record as the first result.
Point the model at a page by passing its URL as the state:
bash
curl -X POST https://api.perplexity.ai/v1/decisions \ -H "Authorization: Bearer $PERPLEXITY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "pplx-decider-v1.2", "mode": "web_grounding", "state": "https://pypi.org/project/requests/", "questions": { "newer_stable_release": { "type": "noul", "instructions": "Has this package published a stable release newer than 2.31.0 as of today?" } } }'
In the Perplexity Python library, pass mode in the request body:
python
from perplexity import Perplexity client = Perplexity() response = client.decisions.create( model="pplx-decider-v1.2", state="Claim: The James Webb Space Telescope launched in December 2021.", questions={"supported": {"type": "noul", "instructions": "Is the claim true?"}}, extra_body={"mode": "web_grounding"}, ) print(response.answers["supported"].noul)
Each completed search costs $0.001 in addition to input tokens. Search evidence counts toward input tokens. A request using Web Grounding can include up to 100 questions.
Get started by exploring the web grounding docs.
