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GTE Large English v1.5

434M-parameter Apache 2.0 encoder with an 8K context window

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About

GTE-large-en-v1.5 is Alibaba's workhorse English embedding model: a 434M-parameter encoder producing 1024-dimensional vectors over an 8192-token window — a long context for its size class. Alibaba reports an MTEB average of 65.39, ahead of its BGE and E5 contemporaries at similar scale, and it is Apache 2.0 licensed. It is a pragmatic middle ground: far cheaper to serve than 7B LLM embedders, more capable than 335M models, and the long window reduces chunking headaches. The honest limits: it needs no instruction prefixes but also gains nothing from them, and it has been eclipsed on raw quality by the newer LLM-based GTE models from the same lab.

Specs

Context window

8,192 tokens

Pricing

Free (open weights)

Free quota

None (self-hosted model)

API access

No official API; self-host via sentence-transformers

Weights

Open weights

Released

January 9, 2024

Benchmarks

MTEB average 65.39 (Alibaba-reported)

Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.

Use cases

semantic searchlong-context RAGcost-efficient embeddingsApache-licensed pipelines

Trust score — 7/10

/10 trustVerified

Scored 7/10 (verified tier, metrics-v1). Evidence: 434M params, 1024 dims, 8192 context, Apache 2.0, and Jan 9, 2024 release corroborated in a research comparison table and an independent RAG-stack synthesis read this session.; MTEB 65.39 is Alibaba-reported.; Eclipsed by Alibaba's own LLM-based GTE models — noted as a limitation..

Sources