GLiClass vs OpenAI Decisions API

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.

OpenAI Decisions API

Typed predicate, choice and score answers with probabilities, up to 200 questions a call, at $0.10 per million input tokens with no output charge. It is a public beta released on 6 October 2026, with one model alias, no dated snapshot, no Batch route and no SLA found.

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

OpenAI Decisions API

1. Put independent questions about one input in a single questions array. Send a separate request when a question depends on an earlier answer
2. Check each answer's type before reading it. A question can come back as refusal while the others in the same request are answered
3. Encode images as base64 data URLs. Hosted URLs and file_id inputs are rejected
4. Give a choice question a fallback value such as other, and set thresholds from your own labelled examples
5. Use Python SDK 3.26.0 or JavaScript 7.30.0 or later, and follow Retry-After on 429 and 503