BAAIBGE Large En
Bge Large En is an English text embedding model developed by BAAI. It maps text to dense vectors for tasks like retrieval, classification, clustering, and semantic search, and can be integrated into vector databases for LLMs. The model achieved first rank on the MTEB benchmark at its release.
Input
Output
Context
512
Max Output
n/a
Parameters
335.1M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Estimated GPU requirements for BGE Large En
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
More models by BAAI
The creator's other models in the catalog, with their context, size and license where published.
Compare it, or keep browsing
Put this model beside its alternatives on the same evidence, or go back to the full catalog.
Frequently asked questions about BGE Large En
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
BGE Large En advertises a context window of 512 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. BGE Large En is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
BGE Large En accepts Text and produces Embedding. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for BGE Large En is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; BGE Large En is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 0.7 GB at INT8 for the weights and a default context, from the catalog's 335.1M parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 7, 2026
Put intelligence in the middle.
One gateway in front of every model, with your policies applied and every decision on record. Start on the free plan with 5,000 requests a month, no card required.