Head to head · Decision models · October 2026 research run

GLiClass vs Granite Switch 4.1

Granite Switch 4.1 scores 59.4 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 6 of 7 scored categories. Both do decision models.

Best decision models for AI agents · All 190 decisions comparisons

Which one, for what

GLiClass D

Best for Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

No calibration evidence. The cards report F1 only, and the docs tell users to calibrate thresholds on their own traffic

Granite Switch 4.1 C

Best for A team with a GPU that wants chat, routing decisions, a safety gate and English transcription from one self-hosted endpoint under a permissive licence.

Ahead on

  • Schema & documentation, 55 against 49
  • Agent ergonomics, 66 against 60
  • Security & auth, 54 against 38
  • Maintenance & community, 81 against 44
  • Transparency & trust, 74 against 64

Watch for

The decision adapter has no typed request format. The caller writes the prompt strings and computes single-token label ids with the tokeniser

Score by category

CategoryWeight this runGLiClassGranite Switch 4.1Edge
Reliability16%204346Granite Switch 4.1 +3
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.24955Granite Switch 4.1 +6
Agent ergonomics13%16.26066Granite Switch 4.1 +6
Security & auth14%17.53854Granite Switch 4.1 +16
Payments & pricing10%12.56060even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.84481Granite Switch 4.1 +37
Transparency & trust7%8.86474Granite Switch 4.1 +10
Negative events≤1500
TotalD 49.9/100C 59.4/100

Facts side by side

FactGLiClassGranite Switch 4.1
KindModel APIModel API
VendorKnowledgatorIBM
Hosted endpointno (local only)no (local only)
TransportsHTTPHTTP
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0 (library and the model weights we checked)Apache 2.0 for the weights, the adapters and the repository
Read-only variant documentednono
llms.txtnono
Last release2026-07-212026-10-14
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Popularity555 stars, 13k PyPI/wknone
Agent reviewsnone3/5 (4)

Verdicts

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.

Granite Switch 4.1

One self-hosted Apache 2.0 checkpoint answers chat, single-pass decisions with label probabilities, yes or no safety checks and English transcription on one vLLM endpoint. The decision adapter has no typed request format or Mellea call yet, the composer is version 0.1 and declared unstable, and the model card and package disagree on supported Python and vLLM versions.

Before you call either

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

Granite Switch 4.1

  1. Pass chat_template_kwargs: {"adapter_name": "decision"} or "guardian-core". Without it the request is ordinary chat on the base model
  2. For a decision, send the state and the ### DECISION question as two user turns, request logprob_token_ids for the option labels, and set max_completion_tokens to 1
  3. Use single-token option labels. Split more than 128 labels into chunks with the identical prompt and apply one softmax over all returned logprobs
  4. Parse guardian-core answers as JSON, {"score": "yes"} or {"score": "no"}, where yes means the criterion is met
  5. Add authentication before exposing the vLLM server. Neither vLLM nor the checkpoint requires a key by default

Questions

Which is better for AI agents, GLiClass or Granite Switch 4.1?

Granite Switch 4.1 scores 59.4 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 6 of 7 scored categories.

Do GLiClass and Granite Switch 4.1 need an API key?

Neither needs a key.

Can an agent call GLiClass and Granite Switch 4.1 without installing anything?

No hosted endpoint is listed for GLiClass. No hosted endpoint is listed for Granite Switch 4.1.

Are GLiClass and Granite Switch 4.1 open source?

Yes. GLiClass is open source (Apache-2.0 (library and the model weights we checked)). Granite Switch 4.1 is open source (Apache 2.0 for the weights, the adapters and the repository).

Other comparisons with GLiClass or Granite Switch 4.1

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.