GTE-Large

About GTE-Large

The gte-large embedding model converts English sentences, paragraphs and moderate-length documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for information retrieval, semantic textual similarity, reranking and clustering tasks. Trained via multi-stage contrastive learning on a large domain-diverse relevance corpus, it offers excellent performance across general-purpose embedding use-cases.

Model Specifications

💰 Real-World Cost Examples

Estimated monthly costs for common use cases

Other Model Lineup

Compare all models from Other to find the best fit

Similar Models from Other Providers

Cross-brand alternatives with similar capabilities

Qwen3 Embedding 8B

Alibaba Qwen

Input: $0.01

Output: Free

Context: 32k

Qwen3 Embedding 8B

Alibaba Qwen

Input: $0.01

Output: Free

Context: 32k

Text Embedding 3 Small

OpenAI

Input: $0.02

Output: Free

Context: 8k

Qwen3 Embedding 4B

Alibaba Qwen

Input: $0.02

Output: Free

Context: 33k

💡 Cheaper Alternatives

Same Brand (Other)

🚀 Quick Start

Get started with GTE-Large API

from openai import OpenAI

client = OpenAI(
    base_url="https://api.provider.com/v1",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="thenlper/gte-large-20251117",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)

Continue Your Decision