Sweep vs Watershed
Two of fourteen models named Sweep first on the direct prompt; zero named Watershed. Sweep was named by fourteen of the fourteen models and Watershed by eleven and Sweep carries 35 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
SweepFirst choices, of fourteen modelsWatershed
Across every category in the November 2026 Edition, Sweep and Watershed were named in the same answer thirty-nine times, of the 76 answers naming Sweep and the 78 naming Watershed. In those answers Watershed took the first choice nine times and Sweep two.
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 | | | |
Sweep Watershed first choice named as an alternative argued againstblank: not namedEach cell is one answer, Sweep on the left and Watershed on the right.The direct prompt
Sweep first, Watershed not the choice
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
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
5 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
Watershed31%#2 of 11Sweep4%#5 of 11What the models said about Sweep
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“~$15,000/year, good for corporate decarbonization and CSRD compliance” GLM 4.7 FlashX · budget prompt · soft negative
“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
“Often the best fit for 500 employees): *Examples:* Watershed, Sweep, Position Green, Novata, Persefoni, Greenly.” Gemini 3.5 Flash · scale prompt · first choice
“My default pick: Sweep—if you want one platform for emissions, broader ESG disclosures, and value-chain data.” GPT-6 Luna · direct 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.