---
title: gliclass instruct large v1.0 on SIE | Superlinked
description: SIE, an open-source inference engine, serves gliclass instruct large v1.0, an open zero-shot classifier that labels text with the categories you name.
canonical_url: https://superlinked.com/models/knowledgator-gliclass-instruct-large-v1-0
last_updated: 2026-11-07
---

# gliclass instruct large v1.0

An open zero-shot classifier that labels text with the categories you name.

Model id: `knowledgator/gliclass-instruct-large-v1.0` · Endpoint: `extract` · [Hugging Face](https://huggingface.co/knowledgator/gliclass-instruct-large-v1.0)

Listed under [Classify text](/models#classify) on [the model index](/models.md).

| Accuracy | Time per request | Serverless price |
|---|---|---|
| 76.9% (AG News, labelled correctly) | – (Not timed one request at a time) | $0.19 per 1M tokens |

## Call it

On SIE Serverless:

```python
from sie_sdk import SIEClient

client = SIEClient(api_key="sk-sie-…")

result = client.extract(
    "knowledgator/gliclass-instruct-large-v1.0",
    {"text": "The renewal charged me twice but the receipt link is broken."},
    labels=[
        "billing",
        "bug report",
        "feature request",
        "account access",
    ],
)
print(result["classifications"])
```

## Next to the other zero-shot classifiers

| Model | AG News, labelled correctly | Time per request | Serverless price | Size |
|---|---|---|---|---|
| [gliclass instruct large v1.0](/models/knowledgator-gliclass-instruct-large-v1-0.md) `knowledgator/gliclass-instruct-large-v1.0` | 76.9% | – | $0.19 per 1M tokens | 439M |
| [GLiNER2.5 Decide](/models/fastino-gliner2-5-decide.md) `fastino/GLiNER2.5-Decide` | – | 13.1 ms per message | $0.28 per 1M tokens | 486M |
| [DeBERTa v3 large zero-shot v2.0](/models/moritzlaurer-deberta-v3-large-zeroshot-v2-0.md) `MoritzLaurer/deberta-v3-large-zeroshot-v2.0` | 88.6% | – | $0.129 per 1M tokens | 435M |
| [deberta v3 base zeroshot v2.0](/models/moritzlaurer-deberta-v3-base-zeroshot-v2-0.md) `MoritzLaurer/deberta-v3-base-zeroshot-v2.0` | 88.1% | – | Not on Serverless (run in your cloud) | 184M |
| [ModernBERT base zeroshot v2.0](/models/moritzlaurer-modernbert-base-zeroshot-v2-0.md) `MoritzLaurer/ModernBERT-base-zeroshot-v2.0` | 84.3% | – | Not on Serverless (run in your cloud) | 150M |

– = not measured / not timed / not listed.

[All 11 zero-shot classifiers](/models#classify)

## Measured on public datasets

| Dataset | Metric | Value |
|---|---|---|
| AG News (decision) | Accuracy | 80.8% |
| AG News | Accuracy | 76.9% |
| Banking77 | Accuracy | 67.5% |
| emotion | Accuracy | 51.5% |

## Facts

- Size: 439M parameters
- License: Apache-2.0
- Reads: Text, up to 1,024 tokens
- Endpoint: extract

## Evaluation

- Accuracy: Run on SIE against the public dataset named in each figure
- Time: SIE timed on a GPU server with the client alongside, one request at a time
- Prices: Serverless list prices