Celtra vs Viewst
One of fourteen models named Celtra first on the direct prompt; one named Viewst. Celtra was named by thirteen of the fourteen models and Viewst by eight and Celtra carries 40 labels and Viewst 13, so the shares are not directly comparable.
Named in one category this edition.
Named in one category 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 creative automation platforms page.
By framing
CeltraFirst choices, of fourteen modelsViewst
Across every category in the October 2026 Edition, Celtra and Viewst were named in the same answer twenty-four times, of the 130 answers naming Celtra and the 28 naming Viewst. In those answers Viewst took the first choice four times and Celtra 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 |
Celtra Viewst first choice named as an alternative argued againstblank: not namedEach cell is one answer, Celtra on the left and Viewst on the right.The direct prompt
Celtra first, Viewst not the choice
1 of 14 modelsViewst was named in the answer but not as the choice, or not at all.Viewst first, Celtra not the choice
1 of 14 modelsCeltra was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
8 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
Celtra6%#5 of 12Viewst4%#8 of 12What the models said about Celtra
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“you should immediately bypass heavy enterprise creative automation platforms like Celtra, Storyteq, Smartly, or Rocketium” Gemini 3.5 Flash · budget prompt · hard negative
“Avoid enterprise platforms like Celtra, Flashtalking, or Smartly.io until you have recurring ad spend of $20K+/month” DeepSeek V4 Flash · budget prompt · hard negative
“Avoid: Enterprise platforms like Celtra, Smartly.io, or Storyteq” Kimi K2 · budget prompt · hard negative
“Bannerflow and Celtra lead in comparisons for high-volume production, AI personalization, and integrations relevant to B2B channels” Grok 4.1 Fast · direct prompt · first choice
“Celtra is best for global brands automating enterprise-grade DCO and creative production across a wide range of media platforms” Claude Haiku 4.5 · comparative prompt · first choice
“I'd generally recommend Celtra if your priority is ad creative production plus workflow management at scale” GPT-5.4 mini · paraphrase prompt · first choice
What the models said about Viewst
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“The platforms most often recommended by privacy-conscious marketing teams include Viewst, Bannerflow, Celtra, and Adobe Firefly Services” MiniMax M2.5 · negative prompt · first choice
“the best creative automation platform for a mid-market B2B company is Viewst” GLM 4.7 FlashX · direct prompt · first choice
“I'd recommend Viewst as the best fit” Kimi K2 · paraphrase 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.