Four of fourteen models named Meltwater first on the direct prompt; zero named CisionOne. Both were named by all fourteen models and Meltwater carries 72 labels and CisionOne 67, so the shares are not directly comparable.
By Meltwater Group, San Francisco, founded 2001. Named in nine categories this edition.
By Cision, Chicago, United States, founded 1867. 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 PR and media monitoring page.
Across every category in the November 2026 Edition, Meltwater and CisionOne were named in the same answer 190 times, of the 343 answers naming Meltwater and the 195 naming CisionOne. In those answers CisionOne took the first choice twelve times and Meltwater thirty-five.
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“As a product/platform, Meltwater was an overpriced, worse alternative to Google alerts and provided no semblance of quality monitoring results” Muse Glimmer 30B · negative prompt · hard negative
“Frequently criticized as overpriced relative to value... Risk score noted as "caution" in some analyses due to commercial practices.” Grok 4.20 · negative prompt · hard negative
“What to Avoid on a Limited Budget Enterprise platforms like Meltwater, Cision, or Brandwatch ... overkill and expensive” Grok 4.20 · budget prompt · hard negative
“Meltwater is the current consensus leader... Meltwater is currently rated #1 on G2 and the top first-choice in enterprise AI buyer tests.” Muse Glimmer 30B · direct prompt · first choice
“For the broadest general media coverage: Choose Meltwater, which is widely positioned as a global enterprise platform” Perplexity Sonar · comparative prompt · first choice
“Choose Cision or Meltwater if you operate globally and need a comprehensive "news in a box" solution.” Qwen 3.7 Flash · comparative prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Cision/CisionOne is the clearest "proceed carefully" name: G2 reviewers report data inaccuracy, irrelevant results, and missed coverage” GPT-5.4 mini · negative prompt · hard negative
“Enterprise-level contracts, high cost, not recommended unless you have a dedicated PR team and a substantial budget.” GLM 4.7 FlashX · budget prompt · hard negative
“Users experience significant data inaccuracy, as searches often yield irrelevant articles and missed coverage.” Muse Glimmer 30B · negative prompt · hard negative
“Cision - Comprehensive platform offering media database, press release distribution, and monitoring... Strong for B2B with extensive journalist contact information.” Claude Haiku 4.5 · paraphrase prompt · first choice
“For an all-in-one PR department: Choose CisionOne if journalist databases, pitching, press-release distribution, and monitoring must live together.” Perplexity Sonar · comparative prompt · first choice
“Cision | Largest journalist database, PR Newswire distribution, enterprise features | Higher cost, complex interface” Kimi K2 · 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.