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Deployment Brief

Gemini Embedding 001

Gemini Embedding 001 is a cost-efficient model with standard context support, suited to code retrieval and repository search for enterprise teams.

Try Gemini Embedding 001 with your team

Last reviewed: 2026-09-02

Gemini Embedding 001

Google

Stable
Context Window
20,000
Input / 1M
$0.23
Output / 1M
$0.00

What can you do with Gemini Embedding 001?

Practical ways teams can use Gemini Embedding 001 inside governed AI workflows.

01

Search codebases with Gemini Embedding 001

Embed repositories, snippets, and technical docs so developers can find relevant implementation context with Gemini Embedding 001.

02

Power coding assistants with Gemini Embedding 001

Retrieve related files, APIs, examples, and dependency context for governed developer workflows with Gemini Embedding 001.

03

Index repositories with Gemini Embedding 001

Create searchable vectors for source files, documentation, issues, and engineering knowledge bases with Gemini Embedding 001.

04

Deduplicate code knowledge with Gemini Embedding 001

Cluster similar snippets, docs, tickets, and examples for cleaner engineering support systems with Gemini Embedding 001.

05

Rank technical evidence with Gemini Embedding 001

Surface relevant code, logs, docs, and tickets during incident and compliance reviews with Gemini Embedding 001.

06

Measure code similarity with Gemini Embedding 001

Compare snippets, repositories, and technical records for recommendations or migration planning with Gemini Embedding 001.

Why this model

Gemini Embedding 001 is available in Remova as a standard context option with $0.23 per 1M tokens input pricing, $0.00 per 1M tokens output pricing, and text input to embeddings output support.

  • Gemini Embedding 001 offers standard context capacity for enterprise prompts and documents.
  • Current Remova pricing band is cost-efficient: $0.23 per 1M tokens input and $0.00 per 1M tokens output.
  • Best-fit workloads include: Code retrieval, Repository search, Coding assistant retrieval.
  • Route requests by policy tier so teams do not overuse capability.

At a glance

Model ID
google/gemini-embedding-001
Context Window
20,000 tokens
Modality
Text input to embeddings output
Input Modalities
Text
Output Modalities
Embeddings
Input Price
$0.23 per 1M tokens
Output Price
$0.00 per 1M tokens
Provider
Google
Listing Date
2025-10-31

Strengths

  • Gemini Embedding 001 is suited for code retrieval.
  • Supports standard context for multi-step prompts and larger working sets.
  • Pricing profile is cost-efficient, enabling predictable workload routing decisions.
  • Can be paired with policy guardrails for safer deployment at scale.

Tradeoffs

  • Quality and latency should be benchmarked against your internal prompt set before broad rollout.
  • Standard context limits may require chunking or retrieval strategies for large documents.
  • Low-cost tiers can still underperform on high-consequence decisions without escalation paths.
  • Code retrieval systems need repository access controls, freshness checks, and relevance benchmarks.

Best for

  • Gemini Embedding 001 for codebase retrieval across repositories, docs, issues, and technical records.
  • Gemini Embedding 001 for repository search with access controls and relevance benchmarks.
  • Gemini Embedding 001 for grounding coding assistants in approved repository context.
  • Gemini Embedding 001 for surfacing relevant code, logs, and tickets during engineering reviews.

Rollout checklist

  • Define where Gemini Embedding 001 is default vs. fallback in your routing policy.
  • Enable role-based access and policy checks before opening access broadly.
  • Set spend guardrails by team and monitor weekly token consumption.
  • Define escalation rules to premium models before launch.
  • Re-run quality and cost benchmarks monthly as newer releases appear.

Related models

Explore adjacent model profiles for routing and benchmarking decisions.

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Tuning notes

max_tokens

Set completion limits to avoid unpredictable long-output spend.

response_format

Prefer structured output where responses feed internal systems.

seed

Use this parameter only with tested defaults in production workflows.

temperature

Lower temperature for deterministic policy and compliance tasks.

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Gemini Embedding 001 FAQs

Choose Gemini Embedding 001 when the workload aligns with code retrieval, repository search, coding assistant retrieval and quality targets justify its pricing profile.
It depends on workload mix. Most organizations use routing policies so routine traffic stays on lower-cost tiers.
Validate quality on real internal prompts, token efficiency, latency, and policy compliance behavior.

Deploy This Model With Governance

Use policy controls, role-based access, and budget guardrails before enabling advanced model tiers at scale.

Try Gemini Embedding 001 with your team