| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Employee giving and volunteering | Onboarding and employee experience | 22% | 1 of 70 | 3% | 33 | accepted challenger |
| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 1 | 2 | 0 | 0 | 3 |
| GPT-5.4 mini | 1 | 0 | 1 | 0 | 2 |
| Gemini 3.5 Flash | 2 | 2 | 0 | 0 | 4 |
| Perplexity Sonar | 1 | 1 | 0 | 0 | 2 |
| Grok 4.1 Fast | 1 | 1 | 0 | 0 | 2 |
| Mistral Small | 0 | 2 | 0 | 1 | 3 |
| DeepSeek V4 Flash | 2 | 1 | 0 | 0 | 3 |
| Llama 4 Maverick | 0 | 1 | 1 | 0 | 2 |
| Qwen 3.7 Flash | 0 | 2 | 0 | 0 | 2 |
| Kimi K2 | 2 | 1 | 0 | 0 | 3 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 2 | 1 | 0 | 0 | 3 |
| GPT-6 Luna | 1 | 2 | 0 | 0 | 3 |
| Muse Glimmer 30B | 1 | 0 | 0 | 0 | 1 |
Verbatim evidence the judge attached to positive labels.
“I'd suggest starting with Millie if you're already on Slack, as it's designed specifically for smaller businesses with cost in mind” MiniMax M2.5 · Giving & volunteering · budget prompt · first choice
“look into modern, employee-experience-first platforms such as Deed, Groundswell, Uncommon Giving, or Millie” Gemini 3.5 Flash · Giving & volunteering · negative prompt · first choice
“If you want the single best default choice, I'd start with Millie for a mid-market B2B company” Perplexity Sonar · Giving & volunteering · direct prompt · first choice
“I'd start with Millie if you want something straightforward and budget-friendly” MiniMax M2.5 · Giving & volunteering · paraphrase prompt · first choice
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“its volunteering component is relatively basic and may not meet the needs of organizations seeking robust volunteer management” Mistral Small · Giving & volunteering · negative prompt · soft negative
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 85 of the 85 answers that named Millie and are not a share of its labels.
No domain is on file for Millie, so its own site is not marked.
Pages are listed as the models cited them.
Search figures are US estimates from DataForSEO, read October 5, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Millie's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Millie, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
A new claim receives the current edition's vendor brief for Millie by email, built from the raw record of the edition. It shows:
AI models recommend Millie first in Employee giving and volunteering this edition. Each badge says so in the buyer's words, names the edition, and links to the standing. The next edition issues a new badge; this one stays true as a record of October 2026.
Badges read on light and dark pages. Alt text carries the claim, the category, the edition and the index, so it stays a citation where the image does not load.