Decider vs GLiClass

Decider

An Apache-2.0 decision model family with a dated changelog, passing CI, 21 package releases since 22 September 2026 and model cards that list measured regressions. One person maintains it, the local server has no authentication option, states over 32,768 tokens are cut without an error, and no security policy is published.

GLiClass

An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed.

Decider

1. Pin weights by Hub tag (v10, v2) when results must repeat. The main branch of each model repository changes with new versions
2. Read x_p_max for the top probability. Since 1.3.0 confidence on choice and score answers follows TypeSafe's rescaled definition, not the top probability
3. Keep the server on 127.0.0.1 or put an authenticating proxy in front. It has no key option
4. Count state tokens before sending. Over 32,768 the state is cut silently, and /decide caps context at 1,536 tokens
5. Split multi-step arithmetic or multi-hop judgements into several questions, and don't write long rules into a question. The README says both fail

GLiClass

1. Pass --host 127.0.0.1 to python -m gliclass.serve, or put the port behind your own gateway. The server checks no credential
2. On a machine without a GPU add --device cpu --dtype float32 --num-gpus-per-replica 0. The default configuration expects CUDA
3. Send one text a request to POST /gliclass. An array in texts is cut to its first item without an error
4. Set multi_label to false for one label from a set. The default scores each label independently, so scores do not sum to 1
5. Keep text plus labels under the pipeline's 1,024-token max_length, or use ZeroShotClassificationWithChunkingPipeline. Longer input is truncated silently