thenlper
Thenlper: GTE-Base
thenlper/gte-base
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
thenlper/gte-base is a text-to-embeddings model listed by OpenRouter for converting English sentences and paragraphs into dense vectors with 768 dimensions and a 512-token context limit. The catalog positions it for semantic similarity, search, and clustering. Its model documentation includes MTEB results spanning classification, retrieval, clustering, reranking, and semantic textual similarity, but this source pack does not provide broader deployment, licensing, or workload-specific validation.[1][2]
Strengths
- The catalog reports dense 768-dimensional embeddings for English sentences and paragraphs.[1]
- The model documentation reports measured MTEB results for classification, retrieval, clustering, reranking, and semantic textual similarity.[2]
- A stated 512-token context limit gives implementers a clear boundary for input sizing.[1]
Limitations
- The catalog describes a text-input, embeddings-output contract; other input types and generative responses are outside the documented interface.[1]
- The stated 512-token context length requires an external strategy for longer inputs, which is not specified in the supplied evidence.[1][2]
- Evidence is limited to catalog metadata and MTEB results; licensing, deployment requirements, and behavior on private or non-English workloads remain undocumented, so workload validation is needed.[1][2]
Where to use it
- Semantic search over English sentence- and paragraph-length text, after corpus-specific evaluation.[1]
- Clustering or grouping English content when inputs can be kept within the context limit or chunked.[1][2]
- Embedding-based retrieval or candidate reranking pipelines that are benchmarked on the target domain.[1][2]
Sources
- [1] OpenRouter catalog · Thenlper: GTE-BaseSources checked: Nov 13, 2026
- [2] Model documentation · Thenlper: GTE-BaseSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
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.
Specifications
- Model ID
- thenlper/gte-base
- Canonical ID
- thenlper/gte-base-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 $0.0050 / 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 | $0.0050 | $0 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.