Count
What ChatGPT, Claude, Gemini & Grok actually say · July 2026
The verdict
Count appears in 1 AI-ranked category — best position #5 for collaborative data notebook for analytics teams.
Positioning brief — for the Count team
Why the models put Count at #5 for collaborative data notebook for analytics teams
- collaborative investigation on a visual canvas GPT · Gemini“making collaborative investigation and stakeholder participation exceptionally strong”
- combine SQL, Python, charts, and narrative GPT · Gemini“visually chain SQL queries, Python scripts, and charts together”
- multi-stakeholder problem solving GPT · Gemini“multi-stakeholder problem solving”
What the models credit Hex (#1) with — and don’t credit Count
- reactive execution eliminates stale-cell bugs GPT · Claude · Gemini“reactive DAG execution eliminates stale-cell bugs”
- publishing analyses as interactive apps GPT · Claude · Gemini · Grok“one-click publishing of notebooks as interactive apps for stakeholders”
- strong review and versioning workflow GPT · Claude · Grok“its review/versioning workflow is the strongest”
What would move the rank — the models’ fix lines, unified
- less suitable for Python-heavy data science GPT“less suitable for Python-heavy data science”
- not for traditional code-first analysts GPT · Gemini“Not for traditional code-first analysts”
- prefer structured linear execution and git workflows Gemini“prefer structured, linear notebook execution and standard git workflows”
Restructured from verbatim model output · nothing invented · every quote machine-verified
Its multiplayer canvas lets analysts combine SQL, visual exploration, narrative, and decision-making in one unusually fluid workspace, making collaborative investigation and stakeholder participation exceptionally strong
Gemini Reimagines collaborative notebooks on an infinite visual canvas, allowing analytics teams to visually chain SQL queries, Python scripts, and charts together for multi-stakeholder problem solving; assumed visual data mapping drives team analysis.
Where Count falls short, per the models
- GPT The canvas model is less suitable for Python-heavy data science or teams wanting conventional Jupyter-compatible notebooks
- Gemini Not for traditional code-first analysts who prefer structured, linear notebook execution and standard git workflows.
Poll history — On this board 1 of 2 polls since Jul 19 — off it in the latest
#3 → –
Top alternatives per the models: Hex · Deepnote · Databricks Notebooks · marimo
Watch Count
Boards re-poll weekly and the models change their minds. One short email only when Count's standing moves — a rank change, a rival overtaking, or new reasoning from the models. Nothing otherwise.
Embed your ranking badge
Count ranks #5 for best collaborative data notebook for analytics teams by AI-model consensus. Put the badge in your README, docs or site — it updates automatically as the models re-rank.
[](https://modelsagree.com/best/best-collaborative-data-notebook-for-analytics-teams?utm_source=badge&utm_medium=embed&utm_campaign=badge-count)<a href="https://modelsagree.com/best/best-collaborative-data-notebook-for-analytics-teams?utm_source=badge&utm_medium=embed&utm_campaign=badge-count"><img src="https://modelsagree.com/badge/count.svg" alt="Count — ranked #5 for Best collaborative data notebook for analytics teams by AI models on ModelsAgree" height="28"></a>Rankings are computed from what the models answer, re-polled weekly · raw reasoning shown verbatim · methodology