Tability vs OKRs Tool
Seven of fourteen models named Tability first on the direct prompt; zero named OKRs Tool. Tability was named by thirteen of the fourteen models and OKRs Tool by thirteen and Tability carries 44 labels and OKRs Tool 20, so the shares are not directly comparable.
Named in one category 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 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 goals and okrs page.
By framing
TabilityFirst choices, of fourteen modelsOKRs Tool
Across every category in the November 2026 Edition, Tability and OKRs Tool were named in the same answer thirty-seven times, of the 119 answers naming Tability and the 58 naming OKRs Tool. In those answers OKRs Tool took the first choice six times and Tability ten.
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 4.20 | | | | | | |
| 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 | | | | | |
Tability OKRs Tool first choice named as an alternative argued againstblank: not namedEach cell is one answer, Tability on the left and OKRs Tool on the right.The direct prompt
Tability first, OKRs Tool not the choice
7 of 14 modelsOKRs Tool was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
2 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
Tability18%#1 of 20OKRs Tool12%#5 of 20Tability29%#1 of 17OKRs Tool4%#6 of 17OKRs Tool18%#2 of 11Tability12%#4 of 11What the models said about Tability
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Overwhelming UI for new users” DeepSeek V4 Flash · negative prompt · soft negative
“Tability: AI goal drafting, automated check-ins, Slack nudges, AI-suggested progress ... AI needs: Tability leads in AI-assisted workflow” MiniMax M2.5 · comparative prompt · first choice
“Consistently ranked as a top overall and enterprise solution, Tability is praised for its structured approach, strong benchmark scores” Mistral Small · comparative prompt · first choice
“Tability ranks first among tested platforms, as it's the only platform where dashboards, governance and AI didn't need assembling.” Claude Haiku 4.5 · comparative prompt · first choice
What the models said about OKRs Tool
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Users face a steep learning curve with OKRsTool, making it challenging to manage and utilize effectively.” Muse Glimmer 30B · negative prompt · soft negative
“Steep learning curve, one-way integrations, 24-hour sync delays” DeepSeek V4 Flash · negative prompt · soft negative
“The best OKR software for a company with a limited budget is OKRs Tool, which offers a flat-rate pricing plan of $30/month” Llama 4 Maverick · budget prompt · first choice
“Best free/cheapest: OKRsTool or Synergita Starter.” GPT-5.4 mini · budget prompt · first choice
“If you need to pay but want predictable costs, OKRs Tool's $49/month flat rate is hard to beat.” Claude Haiku 4.5 · budget 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.