baai
BAAI: bge-base-en-v1.5
baai/bge-base-en-v1.5
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
baai/bge-base-en-v1.5 is an English text embedding model from BAAI that, according to the OpenRouter catalog, turns sentences and paragraphs into 768-dimensional dense vectors for semantic search, retrieval, and document matching. Its model documentation reports results across many MTEB tasks, including classification, retrieval, clustering, semantic textual similarity, and reranking. The catalog lists a 512-token context length and text-in, embeddings-out modality. Evidence here is incomplete: the documentation excerpt is truncated and no license or hardware details are supplied.[1][2]
Strengths
- The catalog describes a compact 768-dimensional dense embedding aimed at retrieval and document-matching workloads.[1]
- Model documentation reports measured results spanning a broad set of MTEB task types, including retrieval, classification, clustering, and semantic similarity.[2]
- Available through OpenRouter with a linked Hugging Face repository, supporting straightforward discovery and integration.[1]
Limitations
Where to use it
- Suggested for English semantic search or document retrieval pipelines whose inputs fit within the 512-token context.[1]
- A reasonable candidate to evaluate for sentence- and paragraph-level similarity or clustering tasks, given reported MTEB coverage.[2]
- Could serve as the embedding stage in retrieval-augmented or reranking workflows, subject to workload validation.[1][2]
Sources
- [1] OpenRouter catalog · BAAI: bge-base-en-v1.5Sources checked: Nov 13, 2026
- [2] Model documentation · BAAI: bge-base-en-v1.5Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
The bge-base-en-v1.5 embedding model converts English sentences and paragraphs into 768-dimensional dense vectors, delivering efficient, high-quality semantic embeddings optimized for retrieval, semantic search, and document-matching workflows. This version (v1.5) features...
Specifications
- Model ID
- baai/bge-base-en-v1.5
- Canonical ID
- baai/bge-base-en-v1.5-20251117
- Creator
- baai
- 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 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.