GLiNER-2-XL
GLiNER-2 Pricing
GLiNER 2 delivers enterprise-grade information extraction at a fraction of the cost of large language models — optimized for real-time inference and large-scale deployment.
Overview
GLiNER 2 is built for production workloads where efficiency, cost control, and predictable performance matter.
Its CPU-optimized architecture enables low-latency Named Entity Recognition (NER), Text Classification, and Structured Extraction at half the cost of comparable LLM-based solutions.
Efficiency at Scale
Metric | Value | Description |
|---|---|---|
Price | $0.625 per 1M tokens | Run enterprise-grade NER and classification at full scale — 50% lower cost than standard LLM inference. |
Average Latency | ≈130 ms per request | Built for real-time pipelines and streaming applications. |
Throughput | >1,500 req/sec | Horizontally scalable. |
Model Size | 1B parameters | Compact, high-accuracy transformer model optimized for low latency. |
💡 Billing granularity: Usage is measured per 1 M processed tokens (input + output combined).
Example Cost Scenarios
Use Case | Volume / Month | Estimated Cost |
|---|---|---|
Customer support entity extraction | 25 M tokens | ≈ $11.25 |
Document classification pipeline | 80 M tokens | ≈ $36.00 |
Performance Benchmark
GLiNER 2 achieves state-of-the-art efficiency compared to general-purpose LLMs:
Model | Avg Latency | Cost per 1M Tokens |
|---|---|---|
GLiNER-2-XL | 130 ms | $0.625 |
GPT-4-Turbo (LLM) | 500 – 900 ms | $1.25 – $3.00 |
GPT-5 | 7000-28000 ms | $1.25 – $3.00 |
Claude 3 Haiku | 250 – 400 ms | $0.80 – $1.00 |
Included Features
All tiers include:
Access to the
/gliner-2hosted inference APISchema-based multi-task extraction (NER + classification + structured parsing)
CPU-optimized real-time inference (no GPU needed)
Usage dashboard and token analytics
Fastino support and model updates
Summary
GLiNER 2 delivers half-price, full-scale extraction with 130 ms latency — purpose-built for enterprise information pipelines, real-time analytics, and cost-sensitive applications.
It’s the most efficient way to bring schema-driven intelligence into your workflow.
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