thenlper
Thenlper: GTE-Large
thenlper/gte-large
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
thenlper/gte-large is an English-focused text embedding model that, per its OpenRouter catalog entry, maps sentences, paragraphs, and moderate-length documents into a 1024-dimensional dense vector space suited to semantic search, similarity, and reranking. Its model card reports MTEB evaluation results across retrieval, classification, clustering, reranking, and semantic textual similarity tasks. The catalog lists a 512-token context length and a text-to-embeddings modality. Licensing and hardware requirements are not established in the supplied evidence.[1][2]
Strengths
- Model card reports MTEB evaluation results spanning retrieval, classification, clustering, reranking, and STS datasets, indicating broad task coverage.[2]
- Catalog describes 1024-dimensional dense embeddings positioned for information retrieval, semantic textual similarity, and reranking.[1]
- Model card tags indicate sentence-transformers and sentence-similarity ecosystem compatibility.[2]
Limitations
- Documented as an English embedding model; multilingual capability is not established in the supplied evidence.[1][2]
- Listed context length is 512 tokens, so longer documents would need chunking or a different model.[1]
- License terms, hardware needs, and throughput are undocumented; card-reported scores should be validated on your own workload.[1][2]
Where to use it
- Candidate for semantic search and passage retrieval over English content, supported by reported MTEB retrieval results.[1][2]
- May suit duplicate-question detection and reranking pipelines, given reported reranking and STS evaluations.[1][2]
- Plausible option for embedding-based clustering and text classification prototypes.[2]
Sources
- [1] OpenRouter catalog · Thenlper: GTE-LargeSources checked: Nov 13, 2026
- [2] Model documentation · Thenlper: GTE-LargeSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
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...
Specifications
- Model ID
- thenlper/gte-large
- Canonical ID
- thenlper/gte-large-20251117
- Creator
- thenlper
- Added to OpenRouter
- 18.11.2025
- Context length
- 512
- Modality
- text->embeddings
- Input modalities
- text
- Output modalities
- embeddings
- Token pricing
- USD per 1M tokens: in $0.010 / out $0
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | — |
| Coding | No measurement | — |
| Agents | No measurement | — |
| Writing | No measurement | — |
| Reasoning | No measurement | — |
| Document analysis | No measurement | — |
| Vision | No measurement | — |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | output: embeddings |
Evaluations
No evaluations are supplied for this model.
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| DeepInfra | 512 | $0.010 | $0 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.