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...
Cheapest input
$0.01
$/MTok · OpenRouter
Cheapest output
$0
$/MTok
Context
512
tokens
Sources
2
4 listings
Sources
Same model, different sources. Our first-party rate is listed first, then router and aggregator rows stay separate so you can see the spread instead of a single blended number.
| Source | Provider | Input | Output | Cache read | Context |
|---|---|---|---|---|---|
| OpenRoutercheapestthenlper/gte-large | OpenRouter | $0.01 | $0 | $0 | 512 |
| LiteLLMfireworks_ai/thenlper/gte-large | N/A | $0.016 | $0 | $0.0016 | N/A |
| LiteLLMthenlper/gte-large | N/A | $0.016 | $0 | $0.0016 | N/A |
| LiteLLMgte-large | N/A | $0.016 | $0 | $0.0016 | N/A |
Capabilities
- In: text
- Out: embeddings
Run this model in a real workspace
Launch agents from the project they work in, use remote management to reach another machine, and see this model's sessions and token usage in your own history. Free to use, runs on your device.
Data and list-price estimates may contain mistakes. Not investment advice.