Together vs Ten Thousand Coffees
Eight of twelve models named Together first on the direct prompt; zero named Ten Thousand Coffees. Together was named by twelve of the twelve models and Ten Thousand Coffees by eleven and Together carries 43 labels and Ten Thousand Coffees 15, so the shares are not directly comparable.
Named in two categories this edition.
Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 mentoring software page.
By framing
TogetherFirst choices, of twelve modelsTen Thousand Coffees
Across every category in the September 2026 Edition, Together and Ten Thousand Coffees were named in the same answer forty times, of the 145 answers naming Together and the 43 naming Ten Thousand Coffees. In those answers Ten Thousand Coffees took the first choice zero times and Together six.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 | | | |
Together Ten Thousand Coffees first choice named as an alternative argued againstblank: not namedEach cell is one answer, Together on the left and Ten Thousand Coffees on the right.The direct prompt
Together first, Ten Thousand Coffees an alternative
8 of 12 modelsTen Thousand Coffees was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
3 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
1 of 12 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
Together5%#4 of 11Ten Thousand Coffees0%#– of 11Together37%#1 of 11Ten Thousand Coffees2%#8 of 11Together27%#3 of 7Ten Thousand Coffees0%#5 of 7What the models said about Together
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“While industry giants like Together, Chronus, and MentorcliQ are excellent, their pricing often starts at $5,000 to $12,000+ per year, which is out of reach” Gemini 3.5 Flash · budget prompt · soft negative
“more premium/custom-priced and usually overkill for tight budgets” DeepSeek V4 Flash · budget prompt · soft negative
“If I had to pick one safe bet for a typical mid-sized B2B company, I'd start with Together Platform or Mentorgain for a demo.” Kimi K2 · paraphrase prompt · first choice
“Together is the best all-around choice due to its mid-market-specific design, transparent flat-rate pricing, strong AI matching” DeepSeek V4 Flash · direct prompt · first choice
“Together stands out as the most balanced choice due to its AI-driven matching, strong integrations, and proven track record” Mistral Small · paraphrase prompt · first choice
What the models said about Ten Thousand Coffees
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Mid-market / fast deployment: Together, Mentorloop, Ten Thousand Coffees, Mentorgain” DeepSeek V4 Flash · scale prompt · first choice
“specializes in automated, smart introductions to break down organizational silos” Gemini 3.5 Flash · comparative prompt · alternative
“Best For: Networking, peer-to-peer learning, and organic connection.” GLM 4.7 FlashX · comparative prompt · alternative
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.