Comparison — updated 14 July 2026
The enterprise plans, head to head
SSO, data retention, administration, context and pricing: the detailed comparison of both enterprise plans, by an OpenAI and Anthropic partner.
In brief
Claude Enterprise and ChatGPT Enterprise share the same foundation: SSO and SCIM, no use of your data for training, an administration console, SOC 2 compliance. The decisive differences: ChatGPT Enterprise bets on versatility, internal GPTs and the Microsoft ecosystem; Claude Enterprise on the extended context window of up to 500,000 tokens, Projects, Claude Code and the MCP protocol. The choice hinges on your dominant use cases, rarely on security.
Claude Enterprise vs ChatGPT Enterprise (2026)
This is the head-to-head that IT departments most often ask us to settle: ChatGPT Enterprise, OpenAI's plan already widely installed across businesses, against Claude Enterprise, Anthropic's plan that has won over technical and document-heavy teams. Both promise the same thing — cutting-edge generative AI with the guarantees of an enterprise contract — and both keep that promise on the essentials.
Yet the essentials alone are not enough to sign: an IT department commits a six- or seven-figure budget, a security policy and two years of adoption. This comparison — the most detailed in our series — puts both plans through the criteria that sway an architecture committee: identity and provisioning, data retention and use, administration, real-world capabilities, pricing and deployment trajectory. Hunter BI deploys both: we have no side to defend, only field feedback to share.
Claude Enterprise or ChatGPT Enterprise: what exactly should you compare?
Before comparing, let us frame the objects. ChatGPT Enterprise is OpenAI's top-tier plan: access to the GPT-5.x models with no restrictive usage cap, custom GPTs, connectors to office suites, agent mode, image generation, all under centralised administration. Claude Enterprise is its counterpart at Anthropic: the full range of Claude models, shared Projects, an extended context window, Claude Code for developers under the terms of the plan, native GitHub integrations and MCP connectors.
Both differ from their smaller siblings — ChatGPT Business and Claude Team — through three additions: full SSO/SCIM, strengthened contractual commitments (configurable retention, dedicated support, tailored limits) and an administration console worthy of an enterprise IT estate. It is this tier, and this tier alone, that this comparison examines: if your organisation has fewer than around fifty users, the mid-tier plans deserve a first look, at a public price of roughly US$25 to US$30 per user per month.
A final scoping point: the API is not included in these licences. Application use cases — customer chatbots, automated processing — are contracted separately, and are costed with our cost calculator.
SSO, SCIM and identity management: which integrates best?
On identity, parity is almost complete. Both plans support SSO via SAML 2.0 and OIDC, connect to the major directories — Entra ID, Okta, Google Workspace — and provision accounts through SCIM: automatic creation, updating and deactivation of users at the pace of your directory, with group-based assignment. For an IT department, this means neither one creates an identity silo: an employee's arrival and departure are handled within the existing HR flow.
The nuances are operational. Fine-grained role assignment — who is an administrator, who can create shared spaces, who can use which connectors — is expressed differently: ChatGPT Enterprise reasons in terms of workspaces and permissions on GPTs; Claude Enterprise in terms of roles on Projects and policies on MCP connectors. In both cases, plan a role-design workshop before opening the service: that is where real governance is decided, not in the SSO checkbox.
Field feedback: identity integration takes two to five days in both cases, assuming a clean directory. None of our deployments has stumbled on this criterion — it is necessary, never a differentiator.
Data retention and use: what contractual guarantees?
This is the question legal departments ask first, and the answer deserves precision. On training: both vendors commit contractually not to use Enterprise data to train their models — by default and with no action required on your part. This commitment appears in OpenAI's enterprise privacy documentation and in Anthropic's commercial terms.
On retention: both plans let you configure how long conversations are kept, with deletion options controlled by the administrator. Deleted conversations are purged from the systems within timeframes documented by each vendor. On location: both services operate mainly from US and European infrastructure, with data-residency options depending on regions and plans — a point to verify contractually if you are subject to localisation requirements.
On compliance: SOC 2 Type II on both sides, GDPR commitments with standard contractual clauses for transfers, and audit documentation accessible through their respective trust portals. Our conclusion as field auditors: the written guarantees are equivalent; differences in residual risk come from your usage — what data your employees paste into the tool — far more than from the contracts. It is the usage policy and training that make the difference.
Administration console and governance: what does the IT department see?
Both consoles cover the foundation: member and role management, usage dashboards — active users, conversation volumes, features used — enabling or blocking capabilities (web browsing, image generation, connectors, external sharing) and activity logs exportable to your SIEM tools.
ChatGPT Enterprise stands out through GPT governance: the IT department can build an internal catalogue of approved assistants, control who publishes what, and track adoption by GPT — valuable when hundreds of business assistants emerge. Its usage analytics are granular and designed to steer a large-scale rollout. Claude Enterprise stands out through the governance of injected data: Projects make explicit which document corpus feeds which workspace, and MCP connectors are managed by policy — which internal sources are accessible, to whom, for reading or writing. The audit logs there are particularly detailed.
Beneath the surface, two philosophies: OpenAI equips the controlled proliferation of assistants; Anthropic equips the traceability of data flows. Depending on whether your dominant risk is proliferation or leakage, one console or the other will speak to you more — both are serious tools.
Context window and capabilities: what differences in practice?
This is where the gaps become tangible. Claude Enterprise advertises an extended context window of up to 500,000 tokens — on the order of a thousand pages — whereas ChatGPT Enterprise works with windows on the order of 128,000 to 200,000 tokens depending on the model. For roles that handle bulky files — lawyers, auditors, credit analysts, quality engineers — this difference changes the experience: less manual chunking, cross-cutting summaries over a complete file.
In the other direction, ChatGPT Enterprise offers a broader functional palette: native image generation, mature web search, an agent mode that carries out multi-step tasks, a dense ecosystem of office connectors. For generalist support functions — marketing, HR, communications — this versatility feeds daily adoption.
On the developer side, Claude Enterprise includes, under the terms of the plan, access to Claude Code, native GitHub integration and the MCP standard to plug the assistant into your internal repositories; OpenAI answers with Codex and its own engineering tools. Our deployments confirm it: technical teams more often express a preference for Claude, support functions for ChatGPT — hence the frequency of mixed deployments, which both contracts allow without friction.
Claude Enterprise and ChatGPT Enterprise pricing: what to expect?
Neither vendor publishes Enterprise pricing: everything goes through a quote, driven by the number of seats, the commitment term and the options. The orders of magnitude reported by the market are comparable on both sides — several tens of dollars per user per month, tapering with volume, a typical annual commitment, and a minimum seat count at entry with Anthropic. We will not publish a more precise figure: it would be wrong for half of our readers, and only your quote is authoritative.
What to budget beyond the licences: deployment support — usage policy, training, priority use cases — which is a budget line in its own right in the first year, without which the licences stay under-used; and any API consumption for application use cases, contracted separately.
Three negotiation levers work on both sides: the plan mix — not all your users need the Enterprise plan, and blending in the mid-tier plan noticeably lightens the bill — a phased ramp-up rather than a contractual big bang, and a review clause on API prices if your application volumes grow. To simulate your full budget, licences and API, use our AI cost calculator by entering your own quotes.
Deployment and adoption: how to succeed in the first 90 days?
The platform does not do the deployment. Across our engagements, on either plan, the winning trajectory is the same. Weeks 1 to 2: the technical foundation — SSO, SCIM, roles, retention policy — and a written usage policy: which data is permitted, which uses are encouraged, whom to contact in case of doubt. Weeks 3 to 6: a pilot cohort of 30 to 100 users chosen from two or three business functions, with named and measurable use cases — not a generic switch-on. Weeks 7 to 12: rollout in waves, short training per function, measuring adoption through the console and the first internal assistants — GPTs or Projects — built with the business teams.
The pitfalls are symmetrical: on ChatGPT Enterprise, the proliferation of unmaintained assistants; on Claude Enterprise, under-fed Projects that disappoint for lack of a carefully curated corpus. In both cases, the antidote is a well-equipped internal product owner.
At 90 days, the right question is not 'how many licences did we buy?' but 'how many employees open the tool each week, and for which tasks?'. Set this active-usage target from the first wave, track it in the administration console and compare it with the ambitions announced to the committee. When it stalls, the cause is almost never the vendor: it is the enablement — vague use cases, missing training, no business champions. That is precisely what our training and consulting offerings address.
Should you deploy both plans in parallel?
The question is no longer theoretical: in large organisations, the dual-vendor scenario comes up more and more often at architecture committees, and some have already adopted it. The arguments for: each population gets the tool where it is most productive — support functions on ChatGPT, technical and document-heavy teams on Claude; internal dual expertise reduces dependence on a single vendor in a fast-moving market; and permanent competition weighs in the annual renegotiations.
The arguments against are real: two contracts, two consoles, two usage policies to keep aligned, a user-support desk that must know both tools, and a potentially doubled licence cost for users who would demand both. This last point is manageable: in our deployments, few employees genuinely justify dual equipment; the great majority split clearly by usage profile, and a single well-chosen licence is enough for them.
Our decision grid: below 200 users, choose one — simplicity wins. Between 200 and 500, a six-week cross-over pilot on two contrasting business functions will give you a factual answer. Above 500, the dual-vendor scenario deserves serious study: it is often the one that maximises value, provided you unify governance — one common framework, two tools.
| Critère | ChatGPT Enterprise | Claude Enterprise |
|---|---|---|
| SSO (SAML / OIDC) | Yes — Entra ID, Okta, Google Workspace | Yes — Entra ID, Okta, Google Workspace |
| SCIM provisioning | Yes, with group-based assignment | Yes, with group-based assignment |
| Training on your data | Excluded by default, contractual commitment | Excluded by default, contractual commitment |
| Conversation retention | Configurable by the administrator | Configurable by the administrator |
| Audit logs | Yes, exportable | Yes, particularly detailed, exportable |
| Certifications | SOC 2 Type II, GDPR commitments | SOC 2 Type II, GDPR commitments |
| Distinctive governance | Catalogue of approved internal GPTs | Policies on MCP connectors and Projects |
| Critère | ChatGPT Enterprise | Claude Enterprise |
|---|---|---|
| Context window | On the order of 128k to 200k tokens depending on model | Extended up to 500k tokens |
| Functional strengths | GPTs, agent mode, images, web search | Projects, Claude Code, MCP, GitHub integration |
| Profiles that excel with it | Support functions, marketing, operations | Engineering, legal, document analysis |
| Pricing | By quote — tens of $ / user / month, volume discounts | By quote — comparable range, seat minimum |
| Mid-tier plan | ChatGPT Business, ~$25–30 / user / month | Claude Team, ~$25–30 / user / month |
| Typical commitment | Annual | Annual |
When to choose ChatGPT Enterprise or Claude Enterprise?
Choose ChatGPT Enterprise if:
- Your dominant population is in support functions and wants a versatile assistant
- You want to industrialise a catalogue of internal GPTs governed by IT
- Your IT estate and contracts are anchored in Microsoft
- Mass, fast adoption is your primary success indicator
Choose Claude Enterprise if:
- Your teams work on long files: legal, audit, risk, engineering
- Your developers are asking for Claude Code and GitHub integration
- You want to connect the assistant to your IT estate through the open MCP standard
- Fine-grained traceability of data flows is your primary governance criterion
In summary
Our verdict
On security, identity and compliance, both plans are on par and neither disqualifies the other: let no one sell you fear. The decision is made on usage. Choose ChatGPT Enterprise if your value lies in broad adoption across support functions and a catalogue of internal assistants; choose Claude Enterprise if it lies in long files, code and deep IT integration via MCP. Above 500 users, seriously study the dual-vendor scenario under unified governance: it is often the one that maximises value by profile. And whatever the choice, budget for enablement: it is enablement, not the platform, that decides how many employees actually open the tool each week, six months after signing.
Frequently asked
What is the main difference between Claude Enterprise and ChatGPT Enterprise?
The security and administration foundation is equivalent. The decisive differences are functional: an extended context window of up to 500,000 tokens, Projects, Claude Code and MCP on the Claude Enterprise side; internal GPTs, agent mode, image generation and the Microsoft ecosystem on the ChatGPT Enterprise side. The choice follows your dominant use cases.
How much do these Enterprise plans cost?
Both are quote-based: no public price exists. The market reports orders of magnitude of several tens of dollars per user per month, tapering with volume and commitment, with a seat minimum at Anthropic. The mid-tier plans — ChatGPT Business, Claude Team — are listed at around US$25 to US$30 per user per month.
Is my data used to train the models?
No: on the Enterprise plans, both OpenAI and Anthropic commit contractually not to use your data for training, by default. Conversations are encrypted, retention is configurable by your administrator, and both vendors document these commitments in their trust portals and commercial terms.
Which plan has the largest context window?
Claude Enterprise, with an extended window advertised at up to 500,000 tokens — on the order of a thousand pages — against windows on the order of 128,000 to 200,000 tokens on ChatGPT Enterprise depending on the model. The gap matters above all for roles that analyse bulky files in one go: legal, audit, risk.
Can Enterprise pricing be negotiated?
Yes. The effective levers: volume and commitment term, blending the Enterprise plan and the mid-tier plan by profile — which often noticeably lightens the bill — and a gradual ramp-up rather than mass equipping from signing. Also negotiate the API terms if you anticipate application use cases. Only the vendor's quote is authoritative.
Are SSO and SCIM included in both plans?
Yes, in both cases: SSO via SAML 2.0 or OIDC and SCIM provisioning are part of the Enterprise foundation, with Entra ID, Okta and Google Workspace compatibility. Allow two to five days of integration if your directory is clean. It is a hygiene prerequisite, not a differentiating criterion between the two plans.
Sources
- ChatGPT Enterprise — official OpenAI page — accessed July 2026
- Claude Enterprise — official Anthropic page — accessed July 2026
- Enterprise privacy commitments — OpenAI — accessed July 2026
- Anthropic Trust Center — accessed July 2026
- Claude pricing — anthropic.com — accessed July 2026
- ChatGPT pricing — openai.com — accessed July 2026
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