Ada vs Freshchat
Zero of fourteen models named Ada first on the direct prompt; zero named Freshchat. Ada was named by twelve of the fourteen models and Freshchat by nine and Ada carries 28 labels and Freshchat 13, so the shares are not directly comparable.
Named in two categories this edition.
Freshchat
accepted challengerNamed in four categories 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 Chatbots and conversational AI for support page.
By framing
AdaFirst choices, of fourteen modelsFreshchatAcross every category in the October 2026 Edition, Ada and Freshchat were named in the same answer eleven times, of the 66 answers naming Ada and the 53 naming Freshchat. In those answers Freshchat took the first choice zero times and Ada one.
Every model, every framing
| Model | DirectFR | ParaphraseFR | ComparativeFR | Budget-constrainedFR | Scale-constrainedFR | NegativeFR |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | |||||
| GPT-5.4 mini | FR | | FR | |||
| Gemini 3.5 Flash | | | | |||
| Perplexity Sonar | FR | FR | | |||
| Grok 4.1 Fast | | | | | ||
| Mistral Small | FR | |||||
| DeepSeek V4 Flash | | | | |||
| Llama 4 Maverick | FR | |||||
| Qwen 3.7 Flash | | | ||||
| Kimi K2 | | |||||
| GLM 4.7 FlashX | FR | |||||
| MiniMax M2.5 | FR | | FR | |||
| GPT-6 Luna | | |||||
| Muse Glimmer 30B | FR | | FR |
AdaFR Freshchat first choice named as an alternative argued againstblank: not namedEach cell is one answer, Ada on the left and Freshchat on the right.The direct prompt
Neither was the first choice, one was named
9 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
Ada0%#– of 11What the models said about Ada
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“it may require hands-on tuning and works best when your knowledge lives in structured help-center content” Perplexity Sonar · negative prompt · soft negative
“The gold standard for "pure" AI support bots. Expensive, but very powerful for higher volumes.” Qwen 3.7 Flash · scale prompt · soft negative
“Sierra and Ada are pricing-prohibitive and feature-heavy for mid-market” Muse Glimmer 30B · direct prompt · soft negative
“Ada: Excellent for mid-market to enterprise companies. Highly advanced in taking actual business "actions"” Gemini 3.5 Flash · scale prompt · first choice
“Enterprise-level performance with no-code authoring and automation-first design.” Muse Glimmer 30B · comparative prompt · first choice
“If you need high-volume, enterprise-scale automation with professional services, Ada is the next step up” Muse Glimmer 30B · paraphrase prompt · alternative
What the models said about Freshchat
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“Best overall budget pick for a small support team: Freshdesk/Freshchat” GPT-5.4 mini · budget 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.