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Google: Gemini Embedding 001

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

Try Google: Gemini Embedding 001 with your team

Last reviewed: 2026-06-09

Google: Gemini Embedding 001

Google

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

What can you do with Google: Gemini Embedding 001?

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

01

Search codebases with Google: Gemini Embedding 001

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

02

Power coding assistants with Google: Gemini Embedding 001

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

03

Index repositories with Google: Gemini Embedding 001

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

04

Deduplicate code knowledge with Google: Gemini Embedding 001

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

05

Rank technical evidence with Google: Gemini Embedding 001

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

06

Measure code similarity with Google: Gemini Embedding 001

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

Why this model

Google: 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->embeddings modality support for enterprise AI operations.

  • Google: 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.
  • Keep audit logs enabled for high-impact use cases.

At a glance

Model ID
google/gemini-embedding-001
Context Window
20,000 tokens
Modality
text->embeddings
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

  • Google: 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

  • Prompt standards are still needed to keep output quality consistent across teams.
  • 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

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

Rollout checklist

  • Define where Google: 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.
  • Start with approved teams, then expand in controlled waves.
  • 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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Google: Gemini Embedding 001 FAQs

Choose Google: 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 Google: Gemini Embedding 001 with your team