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GTE-Base

GTE-Base by Thenlper is an English text embedding model that encodes sentences and paragraphs into 768-dimensional vectors for efficient semantic similarity and retrieval tasks.

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What is GTE-Base?

GTE-Base is a BERT-based General Text Embeddings (GTE) model that maps English text into 768-dimensional dense vectors optimized for semantic representations. It is mainly used for semantic search and information retrieval, where it provides high-quality embeddings for matching queries with relevant documents. It is also widely applied to tasks such as clustering, reranking, and semantic textual similarity across diverse domains. GTE-Base belongs to the GTE model family (alongside GTE-Small and GTE-Large) introduced in the “Towards General Text Embeddings with Multi-stage Contrastive Learning” work.

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

Provider Input Output Cache read /M Latency Throughput Uptime
Thenlper (Original Hosting) ~$0.10 ~$0.10 — ~250ms ~300 tps ~99.5%
OpenAI (text-embedding-3-small Equivalent) ~$0.10 ~$0.10 — ~180ms ~500 tps 99.9%
Cohere Cohere (embed-english-light-v3) ~$0.08 ~$0.08 — ~200ms ~400 tps 99.9%
Azure OpenAI (text-embedding-3-small) ~$0.11 ~$0.11 — ~190ms ~450 tps 99.9%

Try this model

Test GTE-Base right here — free to start.

GTE-Base
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Code snippet

Call the model through the OpenAI-compatible API.

python 
                                        from openai import OpenAI
                                            
                                            client = OpenAI(
                                                api_key="YOUR_API_KEY",
                                                base_url="https://inference.example.com/v1"
                                            )
                                            
                                            response = client.chat.completions.create(
                                                model="thenlper/gte-base",
                                                messages=[
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ],
                                            )
                                            
                                            print(response.to_json())
                                        
                                    
                                        {
                                                "model": "thenlper/gte-base",
                                                "messages": [
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ]
                                            }

5 Core Capabilities

  • Text Embedding

    Encodes English sentences and paragraphs into 768-dimensional dense vectors optimized for general-purpose semantic representation and downstream tasks.

  • Semantic Search

    Supports efficient semantic search by embedding queries and documents into a shared space to retrieve meaningfully related results.

  • Sentence Similarity

    Computes similarity between sentences or paragraphs using cosine distance in the embedding space for clustering and comparison.

  • Text Reranking

    Improves ranking of candidate texts, leveraging relevance-focused embeddings to reorder search or retrieval results more accurately.

  • Classification Support

    Provides embeddings suitable as input features for various text classification tasks across diverse domains and benchmarks.

6 Most Valuable Use Cases

  • Semantic Text Search
  • Document Clustering
  • Text Reranking
  • Duplicate Detection
  • Recommendation Matching
  • Sentence Similarity Scoring

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Unified AI Routing

    Automatically route each request to the best model across providers based on cost, latency, and quality—without changing your integration or redeploying code.

    One endpoint. Any model.

  • Cost-Aware Controls

    Enforce per-model and per-project budgets with smart price-aware routing and guardrails so you never blow through spend while still meeting performance targets.

    Predictable spend at scale.

  • Resilient Fallbacks

    Recover from provider outages or rate limits automatically with configurable fallback chains that keep your application online, responsive, and consistent under failure.

    Never go dark again.

  • Deep Observability

    Get full visibility into every call—latency, cost, provider, model, and errors—with searchable traces and metrics that plug into your existing monitoring stack.

    Trace every token.

  • Task-Aware Orchestration

    Define high-level tasks—chat, retrieval, tools, vision—and let LLM.API pick and orchestrate the right models and parameters for each use case.

    Describe intent, not models.

  • High-Throughput Batch

    Process millions of requests efficiently with server-side batching, automatic chunking, and retry logic that slashes costs and squeezes maximum throughput from every provider.

    Scale to millions safely.

When to Use — When NOT to Use

Use it if...

  • You need a lightweight general-purpose text embedding model for semantic similarity tasks.
  • You need to power semantic search over short to medium-length English text snippets.
  • You need efficient embedding generation for clustering or topic modeling at scale.
  • Your use case involves intent matching or FAQ retrieval with modest accuracy requirements.
  • Your use case involves building recommendation features based on textual content similarity.
  • You need a small, open-source embedding model that is easy to self-host.

Avoid if...

  • You need state-of-the-art embedding performance across many languages and specialized domains.
  • You need robust embeddings for very long documents or multi-page contexts.
  • Your workload requires task-specific embeddings tuned for code or mathematical reasoning.
  • You need production-grade support, SLAs, and monitoring from a major cloud provider.
  • Your workload requires real-time personalization using extremely high-precision semantic representations.
  • You need multimodal embeddings that jointly represent text with images, audio, or video.

Frequently Asked Questions

  • What is GTE-Base?

    GTE-Base is a sentence embedding model by Thenlper, optimized for generating dense text embeddings for retrieval, clustering, and semantic similarity tasks.

  • What is GTE-Base best suited for?

    GTE-Base is best for semantic search, question answering over documents, duplicate detection, and other tasks requiring high-quality sentence or passage embeddings.

  • What modalities does GTE-Base support via LLM.API?

    GTE-Base is text-only and supports embedding text inputs; it does not process images, audio, or video.

  • How does pricing for GTE-Base work on LLM.API?

    On LLM.API, GTE-Base is billed per input token processed for embeddings; check your LLM.API pricing dashboard for the current rate.

  • What is the context window of GTE-Base on LLM.API?

    GTE-Base typically supports input lengths around a few thousand tokens; very long documents should be chunked before embedding.

  • How is the latency and speed of GTE-Base through LLM.API?

    GTE-Base is lightweight and generally returns embeddings with low latency, suitable for real-time or interactive semantic search applications.

  • How do I call GTE-Base through the LLM.API platform?

    Use the LLM.API embeddings endpoint, specifying the provider as Thenlper and the model name as GTE-Base in your request parameters.

  • How does GTE-Base compare to larger embedding models?

    GTE-Base is smaller and faster than many large embedding models, often with slightly lower accuracy but significantly lower cost and latency.

  • Can I use GTE-Base for code, tables, or other non-natural-language content?

    GTE-Base is primarily trained for natural language text, so embeddings for code or highly structured data may be less accurate.

  • What are the main limitations of GTE-Base?

    GTE-Base may underperform on very specialized domains, extremely long documents, or tasks requiring deep logical reasoning beyond surface semantic similarity.

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