ChatGPT
ChatGPT Enterprise documentation
Verify workspace controls, data terms, integrations, support and plan access.
AI product comparison
ChatGPT vs Claude vs Gemini: how should your team choose?
There is no universal winner. Test the same work, with the same inputs and review criteria. Then choose the product setup that performs reliably inside your team’s real constraints.
ChatGPT, Claude and Gemini are products, not fixed models. Their underlying models, workspace plans, integrations, controls and availability change. A model benchmark cannot tell you whether the product and plan you can deploy will work with your data, systems and governance.
Treat the choice as an operational evaluation. The goal is not to crown a permanent winner. It is to establish a sensible default, name the work that needs an exception and keep enough evidence to revisit the decision.
Compare the deployable products
Start with the workspace and plan your team would actually use
Claude
Claude Enterprise documentation
Verify organisation controls, data terms, integrations, support and plan access.
Gemini
Gemini access in Google Workspace
Verify Workspace eligibility, service access, data controls, integrations and admin configuration.
Check the current documentation during procurement. Do not carry a capability from a consumer product, public model release or API into an enterprise workspace assumption.
Evaluation criteria
Use dimensions that reflect the whole job
| Dimension | What to test | Evidence to retain |
|---|---|---|
| Representative tasks | Real work at the range of difficulty, sensitivity and frequency your team faces. | Task brief, inputs, expected outcome and reviewer. |
| Output quality | Accuracy, completeness, judgement, format and the amount of correction required. | Review notes, failures, edits and accepted output. |
| Source grounding | Whether claims remain traceable to the supplied material and unsupported claims are caught. | Source pack, citations, omissions and verification record. |
| Integrations and data access | Whether the deployed product can reach the approved systems and preserve access boundaries. | Connection map, permissions and hand-off steps. |
| Admin and governance | Identity, retention, audit, sharing, policy and human approval requirements. | Control review, owner and unresolved gaps. |
| Availability | Regional and plan availability, service reliability and a workable fallback. | Access checks, incidents and contingency route. |
| Total cost | Licences, integration, review time, rework, support and change overhead. | Comparable cost assumptions at the same usage level. |
A reproducible method
Run the comparison so another team can challenge it
1. Define the work
Select representative tasks and record why each one matters.
2. Fix the inputs
Use the same brief, source pack, constraints and requested format.
3. Set the review criteria
Agree what passes, what fails and which errors carry the most risk before testing.
4. Test the real setup
Use the workspace, plan, integrations and controls the team could deploy.
5. Review independently
Have a subject expert assess outputs without relying on the product’s self-evaluation.
6. Keep the decision record
Store inputs, outputs, review notes, cost assumptions, exceptions and the decision owner.
NIST’s Generative AI Evaluation Program is a useful primary reference for thinking about repeatable evaluation rather than relying on anecdotes.
Operating policy
Choose one default, then make exceptions explicit
A default reduces choice and support overhead for routine, approved work. It is not a claim that one product is best at everything. An exception should name the task, reason, required controls and accountable reviewer. For example, a research task tied to one approved data store may justify one even if another product produced the better draft.
Revisit the default when the evidence or constraints change. This avoids chasing every release without turning today’s decision into a permanent commitment.
From selection to adoption
The product choice only matters if the work improves
The useful unit is not a model preference. It is a tested way of working that people can follow, review and improve. Capture the source material, instructions, checks, hand-offs and accepted result, with the product choice recorded as part of the evidence.
CURN helps organisations redesign work around AI, evaluate tools in context, build what is missing and support adoption. The evidence stays attached to the work so teams can see what transferred, where review is still needed and when an exception earns its place.
Start with the problem.
Bring a decision, a stuck piece of work or something that may need building. We can work out whether CURN is the right fit.
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