Greenly vs Watershed
Two of fourteen models named Greenly first on the direct prompt; zero named Watershed. Greenly was named by fourteen of the fourteen models and Watershed by eleven and Greenly carries 37 labels and Watershed 24, 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 modelsWatershed
Across every category in the November 2026 Edition, Greenly and Watershed were named in the same answer thirty-two times, of the 96 answers naming Greenly and the 78 naming Watershed. In those answers Watershed took the first choice three times and Greenly twelve.
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 | | | |
Greenly Watershed first choice named as an alternative argued againstblank: not namedEach cell is one answer, Greenly on the left and Watershed on the right.The direct prompt
Greenly first, Watershed an alternative
2 of 14 modelsWatershed was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
7 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
Greenly33%#1 of 17Watershed0%#15 of 17Greenly24%#1 of 13Watershed6%#4 of 13Watershed31%#2 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. Three of four in this category shown.
“Greenly has emerged as the breakout carbon accounting platform for SMEs and mid-market businesses” Claude Haiku 4.5 · paraphrase prompt · first choice
“start with Greenly or Horizon ESG for their low monthly costs and essential features” Mistral Small · budget prompt · first choice
“the most recommended carbon accounting platforms are Greenly and Coolset” Mistral Small · 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.
“technically rigorous but are generally framed for enterprises & financial institutions with complex regulatory needs and pricing/complexity” Muse Glimmer 30B · paraphrase prompt · soft negative
“Watershed, Workiva are powerful but priced for bigger organizations” DeepSeek V4 Flash · scale prompt · soft negative
“Most advanced AI and benchmarking (enterprise-grade but pricey)” Kimi K2 · paraphrase prompt · soft negative
“Mid-Market / Agile ESG & Carbon Platforms (Often the best fit for 500 employees): *Examples:* Watershed, Sweep, Position Green, Novata, Persefoni, Greenly.” Gemini 3.5 Flash · scale prompt · first choice
“Start by requesting demos from Watershed and Sweep. These two cover the vast majority of mid-sized B2B needs” Qwen 3.7 Flash · paraphrase prompt · first choice
“Best for: Enterprise climate and carbon accounting (Scope 1, 2, 3) with a focus on decarbonization action” DeepSeek V4 Flash · comparative 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.