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Digital adoption and onboarding · October 2026 Edition

Pendo vs Tango

Three of fourteen models named Pendo first on the direct prompt; zero named Tango. Pendo was named by thirteen of the fourteen models and Tango by six and Pendo carries 60 labels and Tango 10, so the shares are not directly comparable.

Pendo

criticized challenger

Named in eleven categories this edition.

Tango

accepted challenger

Named in one category this edition.

First-choice share8%3%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate33%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#6A position in a field of 11; printed, not drawn.
Labels6010A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Pendo reading right to left. Rank and label count are printed, not drawn.Whatfix was named alongside these two in twelve of the fourteen direct answers. Userpilot vs Pendo · Userpilot vs Tango · UserGuiding vs Pendo

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 digital adoption and onboarding page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
PendoFirst choices, of fourteen modelsTango
Direct303 against Pendo
Paraphrase107 against Pendo
Comparative10
Budget-constrained021 against Pendo
Scale-constrained10
Negative109 against Pendo
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 Pendo and Tango stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Pendo Tango first choice named as an alternative argued againstblank: not namedEach cell is one answer, Pendo on the left and Tango on the right.

The direct prompt

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

Pendo first, Tango not the choice

3 of 14 modelsTango was named in the answer but not as the choice, or not at all.
Grok 4.1 FastPendo, Userpilot alternatives: UserGuiding, Whatfix
Mistral SmallPendo, Whatfix alternatives: Gainsight PX, Userpilot
MiniMax M2.5Pendo, Whatfix alternatives: Appcues

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.
Claude Haiku 4.5Appcues, UserGuiding alternatives: Pendo, Product Fruits, Userpilot, Whatfix
GPT-5.4 miniUserpilot alternatives: Pendo, WalkMe, Whatfix
Gemini 3.5 FlashUserpilot alternatives: Appcues, Spekit, Tango, Userflow, Whatfix
Qwen 3.7 FlashWhatfix alternatives: Appcues, Pendo, WalkMe
GLM 4.7 FlashXWhatfix alternatives: Appcues, Pendo, Product Fruits, UserGuiding, Userpilot
GPT-6 LunaUserpilot alternatives: Pendo, Whatfix
Muse Glimmer 30BUserGuiding, Userpilot alternatives: Gainsight PX, Pendo, Spekit, Whatfix

Neither was named

4 of 14 modelsThe answer made no first choice from these two in this category.
Perplexity SonarUserpilot alternatives: Chameleon, Product Fruits
DeepSeek V4 FlashWhatfix alternatives: Appcues, UserGuiding, Userpilot
Llama 4 MaverickChameleon alternatives: Skalin, UserGuiding, Userflow
Kimi K2Userpilot alternatives: Appcues, Product Fruits, Whatfix

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
Level: the same share of first choices.
Pendo4%#7 of 10
Tango4%#4 of 10
The full small business standing →
Mid-marketThe figures above
Pendo leads by five points.
Pendo8%#4 of 11
Tango3%#6 of 11
The full mid-market standing →
Enterprise
Pendo leads by eleven points.
Pendo11%#3 of 8
Tango0%#– of 8
The full enterprise standing →

What the models said about Pendo

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

“Skip Pendo unless your product team desperately needs a deep "system of record" for product analytics” Gemini 3.5 Flash · paraphrase prompt · hard negative
“Platforms to Avoid for Mid-Market ... Pendo | Expensive (~$4,083/month median); steep learning curve” Kimi K2 · direct prompt · hard negative
“Avoid Pendo if your primary need is in-app guidance.” DeepSeek V4 Flash · negative prompt · hard negative
“I'd typically suggest starting with Appcues or Pendo depending on whether you prioritize simplicity (Appcues) or analytics-driven guidance (Pendo).” Claude Haiku 4.5 · paraphrase prompt · first choice
“I'd suggest starting with Pendo or Whatfix as they offer the best balance of features, pricing, and ease of use” MiniMax M2.5 · direct prompt · first choice

What the models said about Tango

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

“Tango (Best for Automated Walkthroughs & Documentation) ... Download the free browser extension for Tango or Guidde.” Gemini 3.5 Flash · budget prompt · first choice
“UserGuiding and Tango are often considered the best options” Qwen 3.7 Flash · budget prompt · first choice
“Choose Tango if you need to train people quickly and don't have a large IT budget.” Qwen 3.7 Flash · comparative 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.