GLiClass vs Liquid d1

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.

Liquid d1

The hosted API costs $0.04 per million input tokens and takes TypeSafe's System One request, and the d1-3B weights run locally through Transformers or llama.cpp. Liquid's terms allow it to train on API content, and no status page, rate limits or error reference were found. The family launched between 22 September and 7 October 2026.

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

Liquid d1

1. POST to https://api.liquid.ai/decisions/v1/systemone with a liquid_ key as a Bearer header. This is not a chat-completions endpoint
2. Use d1 for images. d1:free is text-only and answers that it does not accept images
3. Send images as Base64 data URLs in images, at most 8, with the whole JSON body under 4.5 MB. Remote image URLs are refused
4. Budget tokens per question. Each question is billed as its own prompt, with its text and every image counted again
5. Don't send confidential content to the hosted API, since the terms allow training on it. Run d1-3B locally for that data