Gemini Embedding 001

Gemini Embedding 001 is a large language model from Google, first released on 1 March 2025. It is proprietary (closed-weights) and sits in the embeddings, google, and enterprise categories of our catalog. Google’s general-purpose embedding model for Vertex AI and Gemini API. This page covers Gemini Embedding 001 pricing, benchmarks, API limits, speed, modalities, best use cases, and how it compares with similar models — so you can decide whether it belongs in your stack in 2026.

As a language model, Gemini Embedding 001 is evaluated on reasoning quality, coding ability, latency, context window size, and dollars-per-million-tokens. The context window is 2k tokens (about 2k words), which determines how much prompt, document, and conversation history you can send in one request. At a typical 3:1 input-to-output mix, the blended API price is about $0.15 per 1M tokens.

Teams usually shortlist Gemini Embedding 001 when they need a dependable Google option for production chat, agents, retrieval-augmented generation, or coding copilots. Common fits include gcp rag stacks and multilingual search. Reviewers consistently call out google cloud integration, multilingual, and solid retrieval quality as standout strengths. Trade-offs to weigh include shorter default context than voyage. The sections below break down pricing tables, benchmark charts, token limits, input/output modalities, and head-to-head comparisons so long-tail queries — from “Gemini Embedding 001 API pricing” to “Gemini Embedding 001 vs Gemini 2.5 Pro” — are answered on this page.

If you are migrating from an older Google model or switching labs entirely, treat this page as a decision brief: skim the overview stats, confirm API pricing fits your volume, check whether the context window covers your longest documents, then validate quality on a golden set of prompts. Benchmarks and charts help shortlist; your own evals decide. We refresh catalog numbers periodically (last update 2026-06) so figures stay useful through the year.

Detailed academic benchmark numbers are not yet published for Gemini Embedding 001 in our catalog. We still surface intelligence index and qualitative strengths where available — run task-specific evals before migrating production traffic.

Gemini Embedding 001 offers a 2k-token context window — roughly about 2k words of English text. Everything in a single API call counts against that budget: system instructions, chat history, retrieved documents, tool schemas, and the model’s reply. Exceeding the window truncates or errors depending on the provider.

Large windows help with long PDFs, multi-file code reviews, and multi-hour agent traces, but bigger contexts also cost more tokens and can add latency. Prefer retrieval that stuffs only relevant chunks, summarize old turns, and reserve headroom for the model’s maximum completion length.

Token limits define how much Gemini Embedding 001 can read and write per request. Total context is capped at 2k tokens. Providers may also enforce organization-level rate limits (RPM/TPM) separate from these per-request caps.

Practical tip: set max_tokens intentionally. Leaving it unbounded wastes budget on verbose answers; setting it too low truncates JSON or code. For structured outputs, prefer schemas/tool calls and keep completions tight.

The charts on this page visualize Gemini Embedding 001 against catalog peers. Benchmark bars show academic scores versus the current leader; the similar-models comparison table plots intelligence, speed, and blended price so you can see trade-offs at a glance. Use them to answer “is Gemini Embedding 001 fast enough?” and “is the quality jump worth the premium?” without opening a spreadsheet.

Best use cases for Gemini Embedding 001 follow from its strengths, price point, and modality support. Match the model to the job: frontier reasoning for hard planning, fast/cheap tiers for classification, image/video/speech specialists for media pipelines.

Based on catalog notes, Gemini Embedding 001 is a particularly strong fit for gcp rag stacks and multilingual search. Validate with a short bake-off on your real prompts before a full cutover.

Anti-patterns: do not use a frontier-priced model like a generic classifier if a smaller model scores within a point on your eval; do not stuff entire corpora into context when retrieval would be cheaper; and do not skip structured outputs if you plan to parse Gemini Embedding 001 responses in code.