Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.
“Zoom Webinars: Basic features, requires separate Zoom subscription” Kimi K2, direct prompt
“What to avoid for this use case- **ON24 and Goldcast** are powerful, but they're **sales-quoted, enterprise-priced**” DeepSeek V4 Flash, paraphrase prompt
“Reviewers cite reliability and audio-lag issues... Widely considered overpriced” DeepSeek V4 Flash, negative prompt
“avoid WebinarJam, EverWebinar, and WebinarKit based on consistent patterns of failure and poor support” Kimi K2, negative prompt
20 of 72 answers in this category came back with a source list, from 4 of 6 models: citations where the model returns them, or the search results it consulted. 5 of those lists are Google grounding redirects that name no site and are left out of the counts. 201 links across 95 sites, every framing counted. Ranked by the number of answers carrying the site or page. The other 2 models searched through a gateway that returned no source list on this edition.
The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|
Flips between the edition run and its calibration repeat. Six prompts per model is a small sample; the index-wide floor is the number to trust.
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.