Orum vs Close
Four of fourteen models named Orum first on the direct prompt; two named Close. Orum was named by twelve of the fourteen models and Close by twelve and Orum carries 29 labels and Close 24, so the shares are not directly comparable.
Named in one category this edition.
Named in seven 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 sales dialers and calling page.
By framing
OrumFirst choices, of fourteen modelsClose
Across every category in the November 2026 Edition, Orum and Close were named in the same answer thirty times, of the 91 answers naming Orum and the 134 naming Close. In those answers Close took the first choice six times and Orum 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 4.20 | | | | | ||
| 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 |
Orum Close first choice named as an alternative argued againstblank: not namedEach cell is one answer, Orum on the left and Close on the right.The direct prompt
Orum first, Close an alternative
4 of 14 modelsClose was named in the answer but not as the choice, or not at all.Close first, Orum not the choice
2 of 14 modelsOrum was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 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
Orum13%#2 of 11Close6%#4 of 11What the models said about Orum
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid high-minimum tools like Orum or Nooks (~$250-400+/user) if budget-constrained.” Grok 4.20 · budget prompt · hard negative
“Multi-line/parallel dialing (Nooks, Orum) is only worth it if you're running high-volume SDR blitzes” DeepSeek V4 Flash · direct prompt · soft negative
“Consider these for very high-volume prospecting ... justify substantially higher pricing” Perplexity Sonar · paraphrase prompt · soft negative
“Choose an AI Parallel Dialer (Orum/Nooks) if you have a high volume of unverified cold leads, low connect rates, and dedicated SDRs.” Gemini 3.5 Flash · comparative prompt · first choice
“My quick picks: Orum or Nooks for high-volume SDR prospecting” GPT-6 Luna · comparative prompt · first choice
“Best for high-volume outbound SDR teams: Nooks or Orum.” GPT-5.4 mini · comparative prompt · first choice
What the models said about Close
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“it's usually less "enterprise mid-market" than Orum/Nooks/Salesloft” GPT-5.4 mini · direct prompt · soft negative
“Best Built-in CRM Dialer Close – The dialer is the core product, fully integrated into their CRM” GLM 4.7 FlashX · comparative prompt · first choice
“Close is often the top recommendation. It is a native CRM with a powerful built-in power dialer.” Qwen 3.7 Flash · direct prompt · first choice
“Close: Strong for SMB/mid-market, excellent CRM-native experience, power/parallel modes.” Grok 4.20 · scale prompt · first choice
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.