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Lead routing and management · November 2026 Edition

Freshsales vs Default

Zero of fourteen models named Freshsales first on the direct prompt; one named Default. Freshsales was named by seven of the fourteen models and Default by ten and Freshsales carries 11 labels and Default 25, so the shares are not directly comparable.

Freshsales

accepted challenger

By Freshworks, San Mateo. Named in seven categories this edition.

Default

accepted challenger

Named in three categories this edition.

First-choice share4%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%16%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#6#7A position in a field of 15; printed, not drawn.
Labels1125A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Freshsales reading right to left. Rank and label count are printed, not drawn.LeanData was named alongside these two in thirteen of the fourteen direct answers. LeanData vs Freshsales · LeanData vs Default · LeadAngel vs Freshsales

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 lead routing and 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.
FreshsalesFirst choices, of fourteen modelsDefault
Direct01
Paraphrase00
Comparative00
Budget-constrained204 against Default
Scale-constrained00
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 Freshsales and Default 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
Freshsales Default first choice named as an alternative argued againstblank: not namedEach cell is one answer, Freshsales on the left and Default on the right.

The direct prompt

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

Default first, Freshsales not the choice

1 of 14 modelsFreshsales was named in the answer but not as the choice, or not at all.
DeepSeek V4 FlashChili Piper, Default alternatives: LeadAngel, LeanData, RevenueHero

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.
Gemini 3.5 FlashChili Piper, LeanData alternatives: Default, Distribution Engine, HubSpot Operations & Sales Hub Enterprise, RevenueHero
Mistral SmallLeanData alternatives: Chili Piper, Default, LeadAngel, RevenueHero
Llama 4 MaverickLeadAngel alternatives: Chili Piper, Default, LeanData
MiniMax M2.5LeadAngel alternatives: Default, HubSpot Sales Hub, OnceHub Route
Muse Glimmer 30BLeadAngel alternatives: Chili Piper, Default, Kubaru, LeanData, RevenueHero

Neither was named

8 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5LeanData alternatives: Chili Piper, HubSpot Sales Hub, Kubaru, LeadAngel, Traction Complete
GPT-5.4 miniLeanData alternatives: Chili Piper, RevenueHero
Perplexity SonarLeadAngel alternatives: Chili Piper, HubSpot Sales Hub, LeanData
Grok 4.20LeanData alternatives: Apollo.io, Chili Piper, Kubaru, LeadAngel, RevenueHero, Salesforce Einstein Lead Scoring + Assignment Rules
Qwen 3.7 FlashLeadAngel alternatives: Chili Piper, LeanData, MarketBetter
Kimi K2LeadAngel alternatives: Chili Piper, LeanData
GLM 4.7 FlashXLeadAngel alternatives: Chili Piper, LeanData
GPT-6 LunaLeanData alternatives: Chili Piper, HubSpot Workflows, RevenueHero

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
Freshsales leads by two points.
Freshsales2%#9 of 16
Default0%#14 of 16
The full small business standing →
Mid-marketThe figures above
Freshsales leads by two points.
Freshsales4%#6 of 15
Default2%#7 of 15
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Freshsales0%#– of 10
Default0%#9 of 10
The full enterprise standing →

What the models said about Freshsales

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

“HubSpot Free CRM or Freshsales Free Plan provide the best value” MiniMax M2.5 · budget prompt · first choice

What the models said about Default

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

“Avoid Chili Piper / LeanData / Default unless you have enterprise budget” Muse Glimmer 30B · budget prompt · hard negative
“Avoid: ... Default ($15,000/year)” Kimi K2 · budget prompt · hard negative
“can easily cost thousands of dollars per year, making them impractical for smaller budgets” Gemini 3.5 Flash · budget prompt · soft negative
“Chili Piper or Default are the strongest picks” DeepSeek V4 Flash · direct prompt · first choice
“Default / Qualified: Modern, all-in-one inbound routing, scheduling, and enrichment platforms built for agile RevOps teams.” Gemini 3.5 Flash · scale prompt · alternative
“Modern mid-market teams wanting routing + scheduling in one clean UX | Quote-based | Newer platform; less proven at scale” DeepSeek V4 Flash · paraphrase 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.