Build

Frames F1

Frames' research model, and when to choose it over MCP.

Frames F1 is our research model. Give it a task and a budget, and it does the whole job: it plans the research, buys data from several sources, checks what it bought and returns a finished answer with sources, receipts and a confidence score.

What F1 does on each run#

  1. 1

    Plans the task and picks sources from the catalog.

  2. 2

    Buys the data it needs, never going over the run's budget.

  3. 3

    Checks each paid response and takes another pass when results are thin.

  4. 4

    Writes the answer, as text or as JSON in a shape you define.

  5. 5

    Returns receipts, a cost breakdown and a confidence score.

MCP or F1?#

Choose MCP whenChoose F1 when
You work in Claude, ChatGPT, Cursor or another AI appYou're building an app, a pipeline or a scheduled job
Your own AI can do the reasoningYou want Frames to do the reasoning
You want the lowest costYou want a finished, checked answer from one request

How to reach F1#

  • From code: model: "frames-f1" on the OpenAI-compatible endpoints, or POST /v1/runs. See the API page.
  • From an MCP app: the frames_run_capability tool. See MCP tools.

Cost#

F1 is the premium path through Frames, and its price reflects what it does. Over MCP your own AI does the thinking and Frames bills only the data that arrived, plus 15%. With F1 the model does the thinking too: it plans the run, chooses the sources, reads every paid response and writes the answer, so its model work is billed on top of the data, with the same 15% on both. In return you get a finished, checked answer from one request instead of raw responses to assemble yourself.

A run never costs more than its budget: Frames sets the budget aside before the run starts and returns whatever was not used. A run that fails costs nothing, a completed run that bought no data and ended without a confident answer costs nothing, and paid calls that fail the delivery check are never charged. Every run reports its own split in usage (model_cost_usd, tool_cost_usd, frames_fee_usd, total_cost_usd), and the chat shows the same split under each answer. Credits, pricing and receipts has the full breakdown.