AI Indexes
GTM AI Index
Index › Partner and channel › Affiliate › PartnerStack vs Refersion
Affiliate and partner management · October 2026 Edition

PartnerStack vs Refersion

Fourteen of fourteen models named PartnerStack first on the direct prompt; zero named Refersion. PartnerStack was named by fourteen of the fourteen models and Refersion by eleven and PartnerStack carries 50 labels and Refersion 23, so the shares are not directly comparable.

PartnerStack

endorsed leader

Named in five categories this edition.

Refersion

accepted challenger

Named in three categories this edition.

First-choice share49%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%9%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#5A position in a field of 12; printed, not drawn.
Labels5023A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, PartnerStack reading right to left. Rank and label count are printed, not drawn.impact.com was named alongside these two in eleven of the fourteen direct answers. PartnerStack vs impact.com · PartnerStack vs Amazon Associates · PartnerStack vs Tapfiliate

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 affiliate and partner management page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
PartnerStackFirst choices, of fourteen modelsRefersion
Direct140
Paraphrase1111 against Refersion
Comparative10
Budget-constrained011 against Refersion
Scale-constrained20
Negative00
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where PartnerStack and Refersion stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
PartnerStack Refersion first choice named as an alternative argued againstblank: not namedEach cell is one answer, PartnerStack on the left and Refersion on the right.

The direct prompt

The plain question, one answer per model, grouped by where PartnerStack and Refersion stood in it.

PartnerStack first, Refersion an alternative

14 of 14 modelsRefersion was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5PartnerStack, impact.com alternatives: Reditus, Tapfiliate
GPT-5.4 miniPartnerStack alternatives: impact.com
Gemini 3.5 FlashPartnerStack alternatives: Everflow, Reditus, impact.com
Perplexity SonarPartnerStack alternatives: Partnero, Trackdesk, impact.com
Grok 4.20PartnerStack alternatives: Tapfiliate, impact.com
Mistral SmallPartnerStack alternatives: Everflow, Tapfiliate
DeepSeek V4 FlashPartnerStack alternatives: Trackdesk, impact.com
Llama 4 MaverickPartnerStack alternatives: Modash, Phonexa
Qwen 3.7 FlashPartnerStack, impact.com alternatives: Post Affiliate Pro, Refersion
Kimi K2PartnerStack alternatives: Post Affiliate Pro, impact.com
GLM 4.7 FlashXPartnerStack alternatives: Rewardful, Tapfiliate, impact.com
MiniMax M2.5PartnerStack alternatives: Refersion, ShareASale, Tapfiliate
GPT-6 LunaPartnerStack alternatives: Rewardful, impact.com
Muse Glimmer 30BPartnerStack alternatives: Tapfiliate, impact.com

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
PartnerStack leads by twenty-three points.
PartnerStack25%#1 of 16
Refersion2%#8 of 16
The full small business standing →
Mid-marketThe figures above
PartnerStack leads by forty-five points.
PartnerStack49%#1 of 12
Refersion4%#5 of 12
The full mid-market standing →
Enterprise
PartnerStack leads by thirty-four points.
PartnerStack34%#2 of 11
Refersion0%#– of 11
The full enterprise standing →

What the models said about PartnerStack

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.

“For most mid-market B2B companies, PartnerStack offers the best balance of features, pricing, and B2B-specific functionality” GLM 4.7 FlashX · direct prompt · first choice
“Impact or PartnerStack – Both offer enterprise features, dedicated support, and handle complex commission structures” MiniMax M2.5 · scale prompt · first choice
“Choose a SaaS (Impact/PartnerStack) if you are a B2B company...It is generally cheaper and easier to set up than Impact.” Qwen 3.7 Flash · comparative prompt · first choice

What the models said about Refersion

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.

“What to Avoid on a Limited Budget ... Starts at $39/month + 3% of affiliate sales” DeepSeek V4 Flash · budget prompt · hard negative
“excellent for ecommerce/DTC brands but are not purpose-built for B2B” DeepSeek V4 Flash · paraphrase prompt · soft negative
“Refersion’s Launch plan is a strong budget-friendly pick at $29/month billed annually” GPT-5.4 mini · budget prompt · first choice
“Refersion stands out as a strong recommendation for affiliate program management” Mistral Small · paraphrase prompt · first choice
“If you're already embedded in the Shopify ecosystem, *Refersion* is a natural fit” MiniMax M2.5 · direct prompt · alternative
Also compared

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.