The best AI depends on task, data, governance and workflow. Companies often need not one model, but a platform that safely orchestrates several models.
Editorial team: mAItflow · Publisher: Masterplan Tech Solutions GmbH · Updated: 2026-08-26
ChatGPT, Claude, Gemini and Perplexity have different strengths. Agentic workspaces combine them per task.
These are different products that happen to share a category name, and the honest summary is that none dominates.
ChatGPT has the broadest tool and integration surface and the largest ecosystem of third-party connections, which matters more in practice than benchmark differences.
Claude is generally preferred for long-document work and for tasks where following detailed instructions precisely matters more than breadth.
Gemini is strongest where an organisation already runs on Google Workspace, for the same reason Copilot is strongest inside Microsoft 365: the data is already there.
Perplexity is built around cited web retrieval rather than general assistance, which makes it a research tool more than a work assistant.
Model quality also moves. Any comparison written today describes a snapshot, which is a good reason not to architect a company around one vendor's current lead.
Published list prices as of August 2026:
Prices change; the vendor pages under Sources are authoritative. Note that comparing per-seat prices across these tells you less than it appears to, because the add-on structure of Copilot and the quote-only structure of Enterprise are not comparable to a flat per-seat subscription.
Organisations rarely choose several AI tools. They accumulate them, because each was adopted for the step it handles best, and then discover the actual cost.
It is not the licences. It is that company context ends up partitioned across tools that cannot see each other: the research lives in one, the analysis in another, the draft in a third, and nothing carries between them except a human copying and pasting. The knowledge produced by the work is not retained anywhere in particular.
The second cost is governance. Each tool has its own permission model, its own data path, its own retention behaviour and its own answer to whether inputs train models. Answering "where is our company data being processed?" becomes a survey rather than a lookup — and that question is asked by auditors, customers and regulators, not just internally.
The alternative to standardising on one assistant is to treat the model as an implementation detail of a step. Research uses whichever model is best at grounded retrieval; long-document analysis uses whichever handles long context best; drafting uses whichever matches the house voice. The workflow, the company knowledge and the audit trail stay in one place.
The practical benefit is that a model change becomes configuration rather than migration. When a better model appears — and one will — the cost of adopting it is a routing change, not a re-platforming and a retraining exercise.
This is the design mAItflow implements: specialized agents that hand work between each other, each step routed to an appropriate model, grounded in the organisation's own connected knowledge, with the handovers visible and the outbound actions approved. It is a different purchase from an assistant subscription, and worth it only when the work is genuinely multi-step.
All links verified on 26 August 2026. Prices are vendor list prices as of that date and do change; the vendor's own page is authoritative.