ChatGPT vs Glean
Three of fourteen models named ChatGPT first on the direct prompt; one named Glean. ChatGPT was named by fourteen of the fourteen models and Glean by eleven and ChatGPT carries 61 labels and Glean 33, so the shares are not directly comparable.
Named in six categories this edition.
Named in two 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 Enterprise AI assistants page.
By framing
ChatGPTFirst choices, of fourteen modelsGlean
Across every category in the November 2026 Edition, ChatGPT and Glean were named in the same answer seventy-five times, of the 210 answers naming ChatGPT and the 95 naming Glean. In those answers Glean took the first choice four times and ChatGPT eighteen.
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 | | | | |
ChatGPT Glean first choice named as an alternative argued againstblank: not namedEach cell is one answer, ChatGPT on the left and Glean on the right.The direct prompt
ChatGPT first, Glean not the choice
3 of 14 modelsGlean was named in the answer but not as the choice, or not at all.Glean first, ChatGPT an alternative
1 of 14 modelsChatGPT 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
2 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
ChatGPT45%#1 of 7Glean0%#7 of 7Glean10%#2 of 9ChatGPT8%#3 of 9What the models said about ChatGPT
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“No suite lock-in and want the lowest predictable enterprise seat: ChatGPT Business or Claude Team at ~ $20/user/mo annual” Muse Glimmer 30B · budget prompt · first choice
“For most budget-conscious companies, ChatGPT Business is the safest, most capable choice at $20/user/month” DeepSeek V4 Flash · budget prompt · first choice
“Choose ChatGPT Enterprise if you want the most flexible general-purpose enterprise assistant.” GPT-5.4 mini · comparative prompt · first choice
What the models said about Glean
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-cost options like Microsoft 365 Copilot ($30/user/month) or Glean (~$40–50/user/month)” Kimi K2 · budget prompt · hard negative
“Avoid ChatGPT Enterprise, Glean, or custom agent platforms initially” Perplexity Sonar · budget prompt · hard negative
“Stay away from ... Glean (~$98K/year)” DeepSeek V4 Flash · budget prompt · hard negative
“Glean — *Best Overall for Multi-App Search & Work Knowledge* ... is widely considered the top enterprise AI platform.” Gemini 3.5 Flash · paraphrase prompt · first choice
“The Best All-Rounder for Knowledge Search: Glean ... Glean is the industry leader for "Enterprise Search."” Qwen 3.7 Flash · paraphrase prompt · first choice
“Winner: Glean ... Glean is the leader. It acts as a universal search bar across all enterprise apps” Qwen 3.7 Flash · direct 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.