Models and compute

Hyperagent supports a wide range of models, from fast, efficient models for routine work to frontier models built for deep reasoning. That range lets you choose the right level of thinking for the size and stakes of the job instead of giving every agent the same model.

Match the model to the job

Model choice changes the quality, latency, and cost of every run. Start with the work the agent owns, then choose enough capability to do that work reliably.

Where you choose a model

The same model choice appears in four places. Choose the surface that matches the scope you want to change.

Set the usual model, change it when needed

The model saved on an agent is the starting point for its work. You can change the model for one conversation without changing what the agent uses everywhere else.

A home thread that is not connected to a named agent starts with your Agent defaults. Once any thread begins using a model, changing the agent's usual model does not rewrite that existing conversation.

Latest aliases and pinned versions

The picker opens in two sections, and which one you choose decides whether the agent moves with upgrades or stays put.

When an alias is selected, the model chip carries a Latest badge. Experimental models carry an Experimental badge, with the tooltip Early access, behavior and availability may change.

Supported model catalog

Hyperagent supports models from several labs, so you can match the model to the agent's job instead of rebuilding the agent around one provider. The catalog changes as models are validated; the picker is the source of truth for what is available to your account.

Anthropic

OpenAI and Google

Open and specialist models

If a model you need is missing, share both the model and the job you want it to perform with the Hyperagent team. The use case helps the team evaluate quality, runtime compatibility, and demand.

Model & compute controls

Inside a thread, Model & compute gathers the model and the two dials that shape how it runs. Reasoning effort decides how hard the model thinks; Fast inference trades cost for speed on the models that support it.

Reasoning effort

Reasoning effort sets how much thinking a model does before it answers. More effort buys deeper reasoning on hard problems; less effort returns faster and costs less. The default is Medium, shown as Balanced.

Fast inference

Fast inference runs an eligible model at higher throughput. Its helper text states the tradeoff plainly: Faster output, billed at 2x token cost. Reach for it on interactive replies and tight loops where latency matters, and leave it off for deep analysis and final writing where standard speed is the better fit.

The toggle appears only on the models that expose it, currently Opus 4.8 and Opus 5. If you don't see the toggle on the model you're using, that model doesn't offer Fast inference in Hyperagent.

Subagent model

Subagents are short-lived workers a run spins up to handle parallel legwork on its own. Subagent model sets the default model those workers use.

Runtime

The Runtime control on the agent's Model and limits card decides which execution runtime serves the model. It defaults to Default chooses the runtime from the selected model, which is the right setting unless you have a specific reason to change it.

Turn timeout

Turn timeout caps how long one turn may keep running. It applies to the current turn, not the lifetime of the thread: later turns can continue in the same conversation.

Use a longer timeout for deep research, browser work, coding, or other jobs that may need sustained tool use. A shorter timeout gives routine or unattended work a firmer stop. If the limit is reached, Hyperagent ends that turn rather than letting it run indefinitely, and the thread keeps the record of where it stopped.

Auto-compact threshold

Every model has a finite context window. Auto-compact threshold decides how full a thread's active context may become before Hyperagent compacts it automatically. Compaction summarizes older conversation so the agent has room to continue; it does not remove the transcript from the thread.

A lower threshold compacts sooner and leaves more headroom for upcoming work. A higher threshold keeps more of the raw conversation active before compacting. For a one-time compaction, open the model menu in the thread and choose Compact context now, or enter /compact.

The thread's Thread Context Document survives compaction and keeps the facts, corrections, decisions, and plan tasks the agent recorded for the current job. See how a thread keeps working context.

Live Mode model

Live Mode runs each scheduled check on its own heartbeat model, chosen separately from the thread and defaulting to the latest Sonnet. This lets a frequent check use a faster, less expensive model without changing the model used for the agent's other work.

When a model becomes unavailable

If a model is removed from your account, it leaves the picker and any new run that resolves to it errors with not available for your account. Latest aliases migrate gracefully: when a family is retired behind an alias, stored selections resolve to the model now serving that family, and the thread shows the model actually in use.

FAQs