pplx-decider vs Strands Decider 2B

pplx-decider

The hosted Decisions API has an OpenAPI description, typed questions, documented limits and a published price of $0.02 per million input tokens, and the v1.1 weights are public under Apache-2.0. The API is days old, the official Python SDK has no method for it, and the governing terms and privacy notice could not be read on 9 October 2026.

Strands Decider 2B

A 1.9B-parameter Apache-2.0 decision model that runs on a laptop GPU, an Apple silicon Mac or a CPU, with its training data, recipe and per-version results published. It's an experimental 0.1.0 release with a 4,096-token window that cuts long states by default, and its local server has no authentication.

pplx-decider

1. Send Authorization: Bearer. A key in x-api-key returns 401 on this endpoint.
2. Set model on every request. A missing or unknown model returns 400, and the guide shows a -latest name rejected.
3. Resize images to 2,048 tiles of 32 by 32 pixels or fewer before sending. Larger images end in a 504 after about a minute.
4. Check the HTTP status before parsing an error. 404 and 405 have empty bodies, 504 can be HTML, and error.code changes type.
5. For the weights, use the repository's autojev code. The card says default causal inference does not reproduce the evaluated setup.

Strands Decider 2B

1. Pin the checkpoint by its full name, such as StrandsAgents/strands-decider-2B-hobson-v21, since each version is a separate Hugging Face repository
2. Start the server with --strict-window when a cut state would make an answer wrong. It then returns 422 naming the window
3. Ask every question about one state in one request. The state is read once and each question adds only its own tokens
4. Keep the server on 127.0.0.1 or put an authenticating proxy in front. It has no key option
5. Measure thresholds on your own traffic before acting automatically. The card says confidence bands hold for short classification only