Persefoni vs Watershed
Zero of fourteen models named Persefoni first on the direct prompt; one named Watershed. Both were named by all fourteen models and both carry 30 labels, so the shares below are 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
PersefoniFirst choices, of fourteen modelsWatershed
Across every category in the October 2026 Edition, Persefoni and Watershed were named in the same answer fifty-five times, of the 79 answers naming Persefoni and the 84 naming Watershed. In those answers Watershed took the first choice ten times and Persefoni six.
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 | |
Persefoni Watershed first choice named as an alternative argued againstblank: not namedEach cell is one answer, Persefoni on the left and Watershed on the right.The direct prompt
Watershed first, Persefoni an alternative
1 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
1 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
12 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
Persefoni4%#3 of 12Watershed0%#12 of 12Watershed33%#2 of 11Persefoni6%#4 of 11What 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
What the models said about Watershed
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“What to Avoid - Watershed — Excellent but typically over $50K–$250K/year; better suited for large enterprises” Kimi K2 · paraphrase prompt · hard negative
“rather than self-serve platforms (like Watershed, which assumes you have an in-house expert to run the strategy)” Gemini 3.5 Flash · direct prompt · soft negative
“built for large corporations and carry five-figure price tags and long sales cycles” Gemini 3.5 Flash · budget 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
“It is the current market leader for mid-market to enterprise. It excels at automating carbon accounting” Qwen 3.7 Flash · direct prompt · first choice
“I'd suggest focusing on vendors like Greenly, Novisto, Tracera, or Watershed” MiniMax M2.5 · 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.