Greenly vs Persefoni
Four of fourteen models named Greenly first on the direct prompt; zero named Persefoni. Greenly was named by twelve of the fourteen models and Persefoni by fourteen and Greenly carries 36 labels and Persefoni 30, 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 ESG and carbon reporting page.
By framing
GreenlyFirst choices, of fourteen modelsPersefoni
Across every category in the October 2026 Edition, Greenly and Persefoni were named in the same answer thirty-eight times, of the 103 answers naming Greenly and the 79 naming Persefoni. In those answers Persefoni took the first choice three times and Greenly seventeen.
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 | | |
Greenly Persefoni first choice named as an alternative argued againstblank: not namedEach cell is one answer, Greenly on the left and Persefoni on the right.The direct prompt
Greenly first, Persefoni not the choice
4 of 14 modelsPersefoni 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
6 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
Greenly48%#1 of 12Persefoni4%#3 of 12Greenly36%#1 of 13Persefoni5%#4 of 13Persefoni6%#4 of 11Greenly0%#– of 11What the models said about Greenly
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Usually not ideal on a limited budget: tools like Greenly ... can be costly for small businesses” GPT-5.4 mini · budget prompt · soft negative
“High cost for smaller firms; first cycle needs heavy verification.” Grok 4.1 Fast · negative prompt · soft negative
“Need supported SME service with transparent pricing: Greenly. The €1,400 / $3,800 per year entry tier is the lowest published SME carbon/CSRD package in 2026.” Muse Glimmer 30B · budget prompt · first choice
“That makes it a good first pick when you want automated data collection, a user-friendly UI and lower total cost of ownership.” Muse Glimmer 30B · paraphrase prompt · first choice
“Greenly is the strongest all-around choice, offering a balance of affordability, ease of use, and robust features” Mistral Small · paraphrase prompt · first choice
What the models said about Persefoni
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“good for complex organizations, but may be overkill for mid-sized” GLM 4.7 FlashX · paraphrase prompt · soft negative
“Choose Watershed or Persefoni if: Your primary immediate pain point is calculating Scope 1, 2, and 3 emissions accurately” Qwen 3.7 Flash · comparative prompt · first choice
“Start for free with carbon + basic reporting: Persefoni Pro.” Muse Glimmer 30B · budget prompt · first choice
“Best overall for a mid-sized B2B company: Persefoni” GPT-5.4 mini · 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.