Thenlper: GTE-Base
thenlper/gte-base
Model weightsThe gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.
Providers
This model is hosted by one provider. OpenRouter forwards every request without routing.
| $0.005 | 1.15s |
Input $0.0051.15s latency100.00% uptime
Pricing
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Weighted average
GTE-Base — Price history
| $0.00491 | $0.00 | 0.0% | 100.0% |
- Effective in $0.00491 / Effective out $0.00Token share100.0%
Performance
Latency is how long it takes to return the full response (lower is better).
P50, best provider
Uptime
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Availability over the last 3 days
Last 72 hoursAvailability over the last 24 hours
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.
Apps
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Activity
Token volume and request traffic to this model over time.
API
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
Not currently available through EU in-region routing. In-region routing docs
Frequently asked questions
The gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.
GTE-Base costs $0.005/M tokens.
GTE-Base accepts up to 512 tokens per input. Longer text has to be split into multiple inputs.
GTE-Base accepts text as input and returns embedding vectors.
GTE-Base is served by 1 provider on OpenRouter: DeepInfra.
GTE-Large is another embedding model from Thenlper.
GTE-Base was released on November 18, 2025.