Customer support and helpdesk
Asked as “customer support and helpdesk platform”, and as “helpdesk and customer support software”, on behalf of a mid-market B2B software company. 43 first choices recorded across the direct, paraphrase, budget and scale prompts, six models each.
01The standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all six models, for a mid-market B2B software company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrant
Show the nine products at 0%, ordered by negative rate
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its vendor page.
Recommended versus criticized
Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.
Criticized challengerCriticized default
Negative label rate →
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14Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative60%
Key
01Freshdesk44%
Freshdesk44%02Zoho Desk23%
Zoho Desk23%03Zendesk16%
Zendesk16%04HubSpot Service Hub2%
HubSpot Service Hub2%05TeamSupport2%
TeamSupport2%06Help Scout0%
Help Scout0%07Intercom0%
Intercom0%08Front0%
Front0%09Gorgias0%
Gorgias0%10Jira Service Management0%
Jira Service Management0%11Hiver0%
Hiver0%12LiveAgent0%
LiveAgent0%13ServiceNow0%
ServiceNow0%14Salesforce Service Cloud0%
Salesforce Service Cloud0%02What they warned about
A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
Zendesk25%
13 of 53 labels negative · 9 of 6 models · 4 hard negative
“**Verdict:** Avoid unless you're a large enterprise with dedicated admin resources and budget for the full suite.” DeepSeek V4 Flash, negative prompt
Intercom25%
11 of 44 labels negative · 9 of 6 models · 3 hard negative
“Avoid starting new implementations with: ... Intercom/Fin (until Salesforce integration clarifies)” Kimi K2, negative prompt
Salesforce Service Cloud47%
14 of 30 labels negative · 8 of 6 models · 6 hard negative
“smaller or earlier-stage teams should avoid it at this stage because the **implementation complexity isn't worth it**” Perplexity Sonar, negative prompt
ServiceNow50%
6 of 12 labels negative · 6 of 6 models · 4 hard negative
“ServiceNow's rigid, aging platform often requires forced upgrades, can exhibit performance issues, demands heavy maintenance” Claude Haiku 4.5, negative prompt
03What they cite
Citations exist only for the models that return a source list: four of the six in this edition.
Sites the answers cite
22 of 72 answers in this category came back with a source list, from 4 of 6 models: citations where the model returns them, or the search results it consulted. 6 of those lists are Google grounding redirects that name no site and are left out of the counts. 221 links across 106 sites, every framing counted. Ranked by the number of answers carrying the site or page. The other 2 models searched through a gateway that returned no source list on this edition.
9 answers · 10 citations · 2 models
vendor site · Intercom7 answers · 8 citations · 3 models
vendor site · Freshworks5 answers · 7 citations · 3 models
5 answers · 7 citations · 3 models
vendor site · Kustomer5 answers · 7 citations · 2 models
vendor site · Zendesk5 answers · 7 citations · 3 models
vendor site · HappyFox5 answers · 5 citations · 2 models
vendor site · Kayako5 answers · 5 citations · 2 models
vendor site · Nextiva5 answers · 5 citations · 1 model
4 answers · 6 citations · 3 models
4 answers · 6 citations · 1 model
vendor site · Desk3654 answers · 5 citations · 2 models
Pages the answers cite
The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.
6 answers · 2 models
6 answers · 3 models
4 answers · 1 model
4 answers · 3 models
4 answers · 2 models
4 answers · 2 models
4 answers · 1 model
4 answers · 1 model
4 answers · 1 model
4 answers · 2 models
04How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ModelDirect“What is the best customer support and helpdesk platform for a mid-market B2B company?”Paraphrase“Which helpdesk and customer support software would you recommend to a mid-sized B2B company?”Comparative“What are the top customer support and helpdesk platforms and how do they differ?”Budget-constrained“What is the best customer support and helpdesk platform for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a customer support and helpdesk platform. What should we look at?”Negative“Which customer support and helpdesk platforms should I avoid or be cautious about?”
Claude Opus 5no first choiceno first choiceno first choiceno first choiceno first choicenothing named
Claude Opus 4.8no first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-6 Astrano first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-5.6 Solno first choiceno first choiceno first choiceno first choiceno first choicenothing named
Gemini 3.1 Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Perplexity Sonar Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Bold is the first choiceAlternatives are counted; the count opens them.What the answer argued against
05The record
One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency. Full answer text is in the free responses file. Download the record
Zero rows: every prompt, every model, every answer.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|
Noise floor in this category
Flips between the edition run and its calibration repeat. Six prompts per model is a small sample; the index-wide floor is the number to trust.
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
Category-scoped readings
Freshworks read as Freshdesk
HubSpot read as HubSpot Service Hub
Salesforce read as Salesforce Service Cloud
Unresolved, counted raw
BMC Remedy / BMC Helix
Deskpro
Drag App
EY's Third-Party Help-Desk Platform
Fortsune / Gorgias
Intercom with Fin AI
Ivanti Neurons for ITSM
Microsoft Lists + Power Automate
ProProfs Desk
ProProfs Help Desk
Zendesk Service
Discontinued, still offered
No shut-down product was recommended here.