[Google](/ai-gateway/models/labs/google) # Text Embedding 005 Text Embedding 005 is an English-language text embedding model with a 66.31% average Massive Text Embedding Benchmark (MTEB) score at 768 dimensions, supporting dynamic embedding sizes down to 256 dimensions to reduce storage and compute costs with minor performance tradeoffs. Your use is subject to Google's [Terms](https://policies.google.com/terms/generative-ai) & [Privacy](https://policies.google.com/privacy) Policies. [Use with AI Gateway](https://vercel.com/d?to=%2F%5Bteam%5D%2F%7E%2Fai%3Futm_source%3Dgateway-model-page%26utm_campaign%3Dai-gateway-models&title=Get+Started+with+Vercel+AI+Gateway) [View docs](https://vercel.com/docs/ai-gateway) ``` 1import { embed } from 'ai'; 2 3const result = await embed({ 4 model: 'google/text-embedding-005', 5 value: 'Sunny day at the beach', 6}) ``` [Read docs](https://vercel.com/docs/ai-gateway/sdks-and-apis/ai-sdk) [Overview](/ai-gateway/models/text-embedding-005) [About](/ai-gateway/models/text-embedding-005/about) [Providers](/ai-gateway/models/text-embedding-005/providers) [Similar](/ai-gateway/models/text-embedding-005/similar) [FAQ](/ai-gateway/models/text-embedding-005/faq) ## [Copy link to heading](#providers)Providers Route requests across multiple providers. Copy a provider slug to set your preference. Visit the [docs](/docs/ai-gateway/provider-options) for more info. Using a provider means you agree to their terms, listed under Legal. | Provider | | --- | | Context | Input | Capabilities | ZDR | No Training | Free Tier | Release Date | | | --- | --- | --- | --- | --- | --- | --- | --- | | ![vertex logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvertex%2520ai.png&w=48&q=75) [Google Vertex AI](/ai-gateway/models/providers/vertex) Legal:[Terms](https://cloud.google.com/terms/service-terms)•[Privacy](https://cloud.google.com/privacy) | | $0.03/M | | | | | 08/01/2024 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | ## [Copy link to heading](#more-models-by-google)More models by Google All Text Code Video Input | Model | | --- | | Context | Latency | Throughput | Input | Output | Cache | Web Search | Capabilities | Providers | ZDR | No Training | Free Tier | Release Date | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) [google/gemini-3.7-flash](/ai-gateway/models/gemini-3.7-flash) | 1M | 1.9s | 254tps | $1.50/M$0.75/M | $7.50/M$3.75/M | Read:$0.15/M$0.08/M Write:— | — | +3 | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) ![vertex logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvertex%2520ai.png&w=48&q=75) | | | | 08/13/2026 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) [google/gemini-3.5-flash-lite](/ai-gateway/models/gemini-3.5-flash-lite) | 1M | 0.5s | 435tps | $0.30/M | $2.50/M | Read:$0.03/M Write:— | $14/K+1 more \+ input costs | +3 | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) ![vertex logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvertex%2520ai.png&w=48&q=75) | | | | 07/21/2026 | | | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) [google/gemini-3.5-flash](/ai-gateway/models/gemini-3.5-flash) | 1M | 1.2s | 256tps | $1.50/M | $9/M | Read:$0.15/M Write:— | $14/K+1 more \+ input costs | +3 | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) ![vertex logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvertex%2520ai.png&w=48&q=75) | | | | 05/19/2026 | | | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) [google/gemini-3.1-flash-lite](/ai-gateway/models/gemini-3.1-flash-lite) | 1M | 1.3s | 311tps | $0.25/M | $1.50/M | Read:$0.03/M Write:— | $14/K+1 more \+ input costs | +3 | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) ![vertex logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvertex%2520ai.png&w=48&q=75) | | | | 05/07/2026 | | | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) [google/gemini\-3-flash](/ai-gateway/models/gemini-3-flash) | 1M | 0.7s | 168tps | $0.50/M+1 more | $3/M+1 more | Read: $0.05/M+1 more Write: — | $14/K+1 more \+ input costs | +3 | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) ![vertex logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvertex%2520ai.png&w=48&q=75) | | | | 12/17/2025 | | | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) [google/gemini-2.5-flash-lite](/ai-gateway/models/gemini-2.5-flash-lite) | 1M | 0.3s | 565tps | $0.10/M | $0.40/M | Read:$0.01/M Write:— | $35/K+1 more \+ input costs | +3 | ![google logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fgoogle.png&w=48&q=75) ![vertex logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvertex%2520ai.png&w=48&q=75) | | | | 06/17/2025 | | ## [Copy link to heading](#about-text-embedding-005)About Text Embedding 005 Text Embedding 005 is Google's English-language text embedding model built on the Gecko architecture, which uses knowledge distillation from large language models (LLMs) to achieve competitive downstream task performance at a compact embedding size. At its full 768-dimension output, the model scores 66.31% on the MTEB benchmark, a standard evaluation suite covering eight categories including retrieval, reranking, clustering, classification, and semantic similarity. This means Text Embedding 005 delivers competitive retrieval and similarity quality without requiring high-dimensional vector indices. Dynamic embedding sizes are supported through Matryoshka Representation Learning (MRL), which trains the model to produce accurate representations at multiple dimension levels from a single pass. At 256 dimensions, it scores 64.37% on MTEB, a two-point reduction that may be an acceptable tradeoff when vector storage costs at scale are significant. This flexibility is built into the model architecture, not post-hoc dimension reduction. ## [Copy link to heading](#what-to-consider-when-choosing-a-provider)What To Consider When Choosing a Provider - Configuration: When choosing between 256- and 768-dimension outputs, Text Embedding 005 shows a performance difference of approximately 2 percentage points on MTEB (64.37% vs 66.31%). For cost-sensitive applications with high vector storage volume, 256 dimensions can meaningfully reduce infrastructure costs with a modest quality tradeoff. - Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the [documentation](https://vercel.com/docs/ai-gateway/security-and-compliance/zdr) for details. - Authentication: AI Gateway authenticates requests using an [API key](https://vercel.com/docs/ai-gateway/authentication-and-byok#api-key-authentication) or [OIDC token](https://vercel.com/docs/ai-gateway/authentication-and-byok#oidc-token-authentication). You do not need to manage provider credentials directly. ## [Copy link to heading](#when-to-use-text-embedding-005)When to Use Text Embedding 005 ### Best for - English semantic search: Document retrieval over English corpora, with a 66.31% MTEB score at the full 768-dimension output - English clustering and classification: Grouping and labeling tasks over English text corpora at 768 dimensions - 768-dimension vector similarity: Applications where 768-dimension indices are the standard infrastructure choice - Cost-sensitive deployments: Use 256-dimension output to reduce storage and compute - Google cloud integrations: Applications on BigQuery or Vertex AI Search that are already in the Gecko model family ### Consider alternatives when - Multilingual content: Your corpus includes non-English text or mixed languages (consider `google/text-multilingual-embedding-002`) - Higher dimensions needed: You need more than 768 dimensions for maximum theoretical discriminative power ## [Copy link to heading](#conclusion)Conclusion Text Embedding 005 delivers strong English retrieval and semantic similarity quality on MTEB at a compact 768-dimension size, with the flexibility to operate at 256 dimensions for storage-sensitive deployments. For English-language semantic search, clustering, and similarity applications, it provides strong performance per dimension relative to larger models.