GLiNER2.5 Decide

An open classifier that scores every category you send in one call, with no prompt to write. fastino/GLiNER2.5-Decide

Get started Run in your cloud Hugging Face

Accuracy

AG News, labelled correctly · not measured
Time per request

13.1 ms

per message
Serverless price

$0.28

per 1M tokens

Classify text 74x faster than GPT‑6 Luna, 85% cheaper

87.5%

Labelled correctly · 400 messages

13.1 ms

Time per message

$0.0035

Cost per 1,000 messages

See the comparison on Classify text

Call it

from sie_sdk import SIEClient
client = SIEClient(api_key="sk-sie-…")
result = client.extract(
    "modelfastino/GLiNER2.5-Decide",
    {"text": "textI've been charged more than once for the same transaction"},
    labels=[
        "labelpending card payment",
        "labelreverted card payment",
        "labelrequest a card payment refund",
        "labelduplicate card payment",
        "labelcard payment fee",
        "labelunrecognized card payment",
        "labeldeclined card payment",
        "labelcard not working",
    ],
)
print(result["classifications"])

import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const result = await client.extract(
  'fastino/GLiNER2.5-Decide',
  { text: "I've been charged more than once for the same transaction" },
  { labels: ["pending card payment","reverted card payment","request a card payment refund","duplicate card payment","card payment fee","unrecognized card payment","declined card payment","card not working"] },
);
console.log(result.classifications);

curl https://api.superlinked.com/v1/extract/fastino%2FGLiNER2.5-Decide \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"text\":\"I've been charged more than once for the same transaction\"}],\"params\":{\"labels\":[\"pending card payment\",\"reverted card payment\",\"request a card payment refund\",\"duplicate card payment\",\"card payment fee\",\"unrecognized card payment\",\"declined card payment\",\"card not working\"]}}"

Build the "Classify text" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: fastino/GLiNER2.5-Decide (SIE primitive: /extract). Keep the model id configurable.

Task
- Input: a block of text plus a set of candidate labels.
- Behaviour: return the best-matching label(s) with native scores
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Next to the other zero-shot classifiers

Next to the other zero-shot classifiers
Model	AG News, labelled correctly	Time per request	Serverless price	Size
GLiNER2.5 Decide fastino	Not measured	13.1 ms per message	$0.28 per 1M tokens	486M
DeBERTa v3 large zero-shot v2.0 MoritzLaurer	88.6%	Not timed	$0.129 per 1M tokens	435M
deberta v3 base zeroshot v2.0 MoritzLaurer	88.1%	Not timed	Run in your cloud	184M
ModernBERT base zeroshot v2.0 MoritzLaurer	84.3%	Not timed	Run in your cloud	150M
gliclass instruct large v1.0 knowledgator	76.9%	Not timed	$0.19 per 1M tokens	439M

All 11 zero-shot classifiers

Facts

Size: 486M parameters
License: Apache-2.0
Reads: Text, up to 512 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
Sources: Task page runs and their inputs

Run GLiNER2.5 Decide on Serverless or in your own cloud

SIE Serverless

We host the models; you call one API.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Self-host the same open-source engine
SOC 2 Type 2, US or EU data residency
Create an account to try Serverless

Get started

SIE Cluster

Kubernetes in your own cloud, backed by our engineers.

Terraform modules for your cloud
Apache-2.0, same engine as Serverless
GPU workers scale to zero between jobs
Separate worker pools for each tenant

Agent prompt Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment.

Talk to us Deploy guide

SIE Local

The open-source server on your own machine.

NVIDIA GPU
One container, no cluster
Process your data fully offline
Same SDK and model IDs as Serverless

docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-transformers5

Quickstart

Classify text 74x faster than GPT‑6 Luna, 85% cheaper

Call it

Next to the other zero-shot classifiers

Facts

Run GLiNER2.5 Decide on Serverless or in your own cloud