text-embedding-005
Google's Gecko-family English embedding on Vertex AI: compact 768-dim vectors with Matryoshka truncation.
About
text-embedding-005 is the English text embedding model in Google's Gecko family, served on Vertex AI and the default model for the Vertex RAG Engine. It produces 768-dimensional dense vectors, supports Matryoshka-style truncation down to smaller sizes, and was built for English and code retrieval with task_type controls including CODE_RETRIEVAL_QUERY.
Google lists it as the drop-in successor to text-embedding-004, and its model-versions page dates the release to November 2024 with GA in May 2025. One caveat to plan around: Google has scheduled its retirement for April 1, 2027, steering new projects toward gemini-embedding-001, so treat 005 as a stable but time-boxed choice.
Specs
Context window
Pricing
Free quota
API access
Weights
Released
Benchmarks
No published MTEB/BEIR headline figures; Matryoshka truncation quality documented in Vertex docs
Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.
Use cases
Trust score — 7/10
Scored 7/10 (verified tier, metrics-v1). Evidence: release (2024-11-18) and retirement (2027-04-01) dates taken from Google's official model-versions doc; 768 dims and 2,048-token limit confirmed across Vertex docs and provider integrations. Strengths: first-party Google support, RAG Engine default, MRL truncation. Docked for: (1) no published benchmark figures; (2) scheduled retirement in April 2027 makes it a depreciating choice for new builds.
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 7.0/10
- Maintenance · recency, releases, issue load
- 7.0/10
- Openness · license + docs
- 3.0/10
- Manual review · sanity read + red-flag check
- 7.0/10
- · Release (2024-11-18) and scheduled retirement (2027-04-01) from Google's official model-versions doc
- · 768 dims, 2,048-token limit, English + code; GA announced May 2025
- · No published MTEB/BEIR figures; priced per 1K characters ($0.000025 online)