Clef vs GLiClass

Clef

Apache-2.0 weights for both models on Hugging Face, ungated, with no account needed to download. Released on 1 October 2026, with no service record and no entry in the Workers AI changelog we read.

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.

Clef

1. Send model as clef or clef-flash in the body as well as the model in the URL. The input schema requires it
2. Ask every independent question about one state in one call. Up to 64 are allowed
3. Try clef-flash first for text-only triage. It costs $0.09 per million input tokens against $0.24
4. Read the internal code on a 429. 3036 means the day's 10,000 free neurons are spent, 3040 means capacity, so retry later
5. Treat an answer about user-supplied text as a judgement that hostile input can steer. Cloudflare publishes no guidance on it

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