Phenom vs SeekOut
Zero of fourteen models named Phenom first on the direct prompt; one named SeekOut. Both were named by all fourteen models and Phenom carries 30 labels and SeekOut 31, so the shares are not directly comparable.
Named in six categories this edition.
Named in five categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the talent intelligence page.
By framing
PhenomFirst choices, of fourteen modelsSeekOut
Across every category in the October 2026 Edition, Phenom and SeekOut were named in the same answer sixty-four times, of the 294 answers naming Phenom and the 224 naming SeekOut. In those answers SeekOut took the first choice three times and Phenom four.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | | | | |||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | | | ||
| Grok 4.1 Fast | | | | | ||
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | |||||
| Llama 4 Maverick | | |||||
| Qwen 3.7 Flash | | | | |||
| Kimi K2 | | | | |||
| GLM 4.7 FlashX | | |||||
| MiniMax M2.5 | | |||||
| GPT-6 Luna | | | ||||
| Muse Glimmer 30B | | | | |
Phenom SeekOut first choice named as an alternative argued againstblank: not namedEach cell is one answer, Phenom on the left and SeekOut on the right.The direct prompt
SeekOut first, Phenom not the choice
1 of 14 modelsPhenom was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
5 of 14 modelsThe answer made no first choice from these two in this category.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
Phenom4%#7 of 13SeekOut2%#8 of 13SeekOut6%#3 of 15Phenom2%#6 of 15What the models said about Phenom
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“My default recommendation: Phenom, if you want one platform to support both external hiring and internal mobility” GPT-6 Luna · paraphrase prompt · first choice
What the models said about SeekOut
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“18% negative sentiment noted. Seat-based model draws complaints on contact data accuracy, pricing, and ROI challenges” Grok 4.1 Fast · negative prompt · soft negative
“Users report it's "slower to reach ROI"” Kimi K2 · negative prompt · soft negative
“SeekOut – Best Overall for Mid-Market” Kimi K2 · direct prompt · first choice
“Choose a single-seat SeekOut Recruit Core plan to leverage enterprise-grade search filters without an enterprise contract.” Gemini 3.5 Flash · budget prompt · alternative
“Excellent for deep, diverse, and technical talent pools... though pricing has increasingly leaned toward enterprise budgets.” Gemini 3.5 Flash · scale prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.