September 2026 · 8 min read
The model picker shipped.
The approval unit did not

Key Definitions
Multi-model harness The ability to select different models per task inside one execution framework. Models become replaceable parts while the framework keeps context, tool calls, permissions and cost accounting, so what gets procured is the model-plus-framework combination.
Per-model data retention Different models in the same product carry different retention terms: some require prompts and responses to be retained by the model provider, others do not. It turns retention from a supplier-level contract question into a model-level configuration item.
Tenant-wide control A switch whose smallest unit is the whole organisation. It can only be all-on or all-off, and cannot be scoped to named users, roles or task types — so enabling a frontier model for a small subset of staff is not expressible.
A multi-model harness is no longer a roadmap claim; it is a row of options in the enterprise console. Copilot Cowork’s model picker spans OpenAI, Anthropic and Microsoft-hosted models, and the docs state plainly that models carrying a data-retention requirement (Claude Fable 5) are off by default, need an admin to enable them in the Microsoft 365 admin center, and show a persistent banner while selected. The same docs contain the line buyers should read twice: turning the Anthropic family off is a tenant-wide setting and cannot be used to grant those models to a subset of users. Our judgment: models are now replaceable per task, while who may use which model — and at what data-retention cost — is still approved at the level of the whole organisation. The missing piece is not the model list. It is the approval unit.
1. A multi-model picker inside the enterprise console
Microsoft Learn’s “Choose a model for Copilot Cowork” page (self-labelled last updated 2026-09-14) lists the picker options: Auto (default), GPT 5.5 (Frontier, hosted in Azure AI Foundry), GPT 5.6 Sol, GPT 5.6 Terra, GPT 6 Astra, Opus 5, Claude Sonnet 5 and Claude Fable 5.1, with OpenAI and Anthropic models provided as subprocessors. The available list reflects the models the organisation enables. Source: learn.microsoft.com/en-us/microsoft-365/copilot/cowork/cowork-models.
The same page tells admins they can turn off the Anthropic model family under Copilot settings in the Microsoft 365 admin center, that this model control has no effect on whether a user can access Cowork, and that the setting is tenant-wide, so it can’t be used to grant Anthropic models to a subset of users. Cowork also shows a model badge in the conversation so users can see which model produced a response, and reasoning effort levels (Light, Medium as default, High, Extra High, Max) sit next to the picker — higher effort is slower and consumes more credits.
Our judgment: the model list is not the news. What matters is the shape — models turned into a row of replaceable options inside one execution framework, with each one’s cost (speed, depth, consumption, retention) labelled separately. This is the moment a multi-model harness moves from concept to configuration screen, and the unit of selection moves from the model to the model-plus-framework combination.
2. Data retention now attaches to individual models
The Cowork FAQ page (also self-labelled last updated 2026-09-14) is blunter: Claude Fable 5 (Preview) is off by default, so it appears only after an admin turns it on under Copilot settings in the Microsoft 365 admin center; and some models, such as Claude Fable 5, require data retention, with Cowork showing a note in the picker and a banner while the model is selected. Source: learn.microsoft.com/en-us/microsoft-365/copilot/cowork/cowork-faq.
Three further constraints sit on the same page: access to Anthropic models through Microsoft services is limited to regions Anthropic supports; Cowork bills on usage, counting model responses, tool and skill calls, image generation and browser tasks; and sensitive actions such as sending mail or posting trigger an approval prompt, with session-level options to skip repeat prompts by specific recipient, domain or action.
Our judgment: data retention has moved from a supplier-level contract question to a model-level configuration item. That is good news and a new burden. Good news, because it is verifiable, screenshot-able and can be written into acceptance criteria. A burden, because the question of which model touches which data moves out of legal’s drawer and onto the architecture review agenda.
3. Our judgment: the approval unit is in the wrong place
Put those facts side by side and an asymmetry appears. Actions are granular down to a recipient: an approval prompt before sending, with session-level skips scoped to a specific recipient, a domain or a single action. Models and retention are not granular at all: the docs state that turning off the Anthropic family cannot be used to grant those models to a subset of users.
In other words, an enterprise can decide that mail to one domain no longer needs confirmation, but cannot decide that only twenty named engineers, on a defined class of tasks, may use a retention-bearing frontier model. The first is fine-grained authorization; the second is an organisation-wide switch.
Our judgment: this is not a missing feature, it is a design misalignment in the control plane — actions are treated as first-class, models and data retention as configuration. Once models become replaceable, what buyers actually need to purchase is the judgment layer: who is allowed to use which model, within what scope, at what cost. Today that layer has exactly one setting: the whole tenant.
4. The analyst view: harness and model can be two things
On 21 September 2026, Ben Thompson reversed his own November 2025 position in Stratechery’s “Frontier Overhangs”. He had argued that the integration point of model and harness was where agent differentiation lived, which would make frontier labs more profitable than they appeared. He now writes that harness and model can be two things, citing Nadella’s account of a multi-model harness and the shipped Cowork picker, and lands the argument on Christensen: after performance surplus, architectures slide from proprietary interlock toward modularity. Source: stratechery.com/2026/frontier-overhangs.
Citation discipline: this is an analyst revising a position, not a finding of fact, and the evidence base is one vendor’s one product plus one analyst. What can be taken is the direction — after decoupling, the layer that persists is the control plane of policy, authorization, audit and cost — not the conclusion that the industry has finished decoupling.
5. A procurement and acceptance checklist
Put the approval unit in the contract
Require that enablement of retention-bearing models is scoped by role, not by tenant. If the vendor only offers a tenant-wide switch, budget for a dedicated tenant or separate environment.
Define the data boundary
State which data classes must never enter retention-bearing models, and make it a checkable configuration rather than a verbal agreement.
Choose models by task, not by preference
Bind model choice to task type: default for routine work, upgrade only for high-value work. Reasoning effort levels consume credits and are a cost variable.
Demand exportable usage records
An in-conversation model badge is for humans. Acceptance needs an export: who, when, which model, on what data.
Front-load regional constraints
Anthropic models are limited to supported regions. Multi-country deployments should put that in architectural assumptions before go-live, not discover it after.
Procure the model-plus-framework combination
The same model behaves differently across frameworks, so acceptance and evaluation target the combination, not a model name.
6. Conclusion and this month’s smallest move
Our judgment: a multi-model harness has already turned which model into a daily operating choice. What remains unsolved is who approves which model and who owns the retention cost. If the control plane cannot push the authorization unit from the organisation down to identity and task, enterprises are left choosing between two bad options: enable the strongest retention-bearing model for everyone, or switch it off for everyone.
Smallest move this month: list the multi-model products you run and fill four columns for each — the model list, the retention terms per model, the authorization unit of the switch (tenant or role), and whether usage records can be exported. Then answer one question: if legal asked tomorrow that the strongest model be available to twenty people only, could your current configuration execute it?
OOMeta AI
OOMeta treats the control plane as the layer that persists after models decouple: policy, authorization, audit and cost have to land at the level of identity and task, not stop at an organisation-wide switch. When we evaluate agent platforms, the authorization unit and the exportability of usage records carry the same weight as model capability.
Book a diagnostic sessionReferences: Microsoft Learn, “Choose a model for Copilot Cowork”, self-labelled last updated 2026-09-14, curl HTTP 200; model list, tenant-wide model control, reasoning effort levels and usage framing are that document’s account. learn.microsoft.com/en-us/microsoft-365/copilot/cowork/cowork-models | Microsoft Learn, “Copilot Cowork FAQ”, self-labelled last updated 2026-09-14, curl HTTP 200; Fable 5 default-off and retention banner, usage-based billing, approval granularity and Anthropic regional limits are that document’s account. learn.microsoft.com/en-us/microsoft-365/copilot/cowork/cowork-faq | Stratechery, “Frontier Overhangs”, 21 September 2026, curl HTTP 200; analyst opinion and a revised position, not a finding of fact. stratechery.com/2026/frontier-overhangs.
FAQ
Which models are in the Copilot Cowork picker?+
The docs list Auto (the default), GPT 5.5 (Frontier, hosted in Azure AI Foundry), GPT 5.6 Sol, GPT 5.6 Terra, GPT 6 Astra, Opus 5, Claude Sonnet 5 and Claude Fable 5.1, with OpenAI and Anthropic models provided as subprocessors. The available list reflects the models an organisation enables.
Which models carry a data-retention requirement?+
The documentation says some models, such as Claude Fable 5, require data retention, and that Cowork shows a note in the picker plus a banner while that model is selected. Claude Fable 5 (Preview) is off by default and appears only after an admin turns it on under Copilot settings in the Microsoft 365 admin center.
Why is authorization granularity the gap?+
The same documentation states that admins can disable the Anthropic model family, but the setting is tenant-wide and cannot be used to grant those models to a subset of users. If you want a handful of high-risk roles to use a retention-bearing frontier model, the current configuration cannot express it — it is all-on or all-off.
Action-level approvals already exist. Why does that matter?+
Cowork prompts for approval before sensitive actions such as sending mail or posting, and lets users skip repeat prompts within a session by specific recipient, domain or action. The same product can be granular down to a recipient for actions, yet stays tenant-wide for models and retention — an asymmetry that shows which layer the control plane treats as first-class.
What drives cost?+
Per the docs, Cowork bills on usage: model responses, tool and skill calls, image generation and browser tasks all count toward consumption. Higher reasoning effort levels (Light through Max) are slower and consume more credits, and usage depends on model choice, context volume, orchestration and tools used.
What should procurement and acceptance add?+
Four items: who may enable retention-bearing models (role, not tenant), the permitted scope (roles and task types), which data classes must never enter those models, and whether the vendor can export a record of who used which model on what data. The first three are configuration capability; the fourth is evidence capability.
Related Articles
Open weights: verify the licence and the host
Qwen-Image-2.1 switched from Apache 2.0 to non-commercial; a mirror re-hosts 669k HF models. Diligence open weights per version, for rights and uptime.
Buy runtime evidence, not control planes
WSO2 Agent Manager GA (Sep 15): control planes are now a commodity. Vendor evals stay in-stack self-report; the scarce layer is independent runtime evidence.
MCP's enterprise gate is auth, not the protocol
MCP's EMA (Jul 2026) moves connection auth into the IdP — not action auth. 100-server test: median 71% pass, worst 47%. Two control planes: build two gates.
OpenAI just commoditized the agent harness
OpenAI turned its Codex harness into a managed API at no extra fee. When the vendor runs the loop, the boundary is where value and risk live.