d'entreprise semantic texte embedding modèle powering récupération, semantic recherche, et RAG pipelines.
Technical Specifications
- Provider
- Category
- lightweight
- Release Date
- 2025-05
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- $20 / month
- Reasoning Effort Control
- Fixed
- Structured Outputs
- No
- Output Modalities
- embedding
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- models/gemini-embedding-001
- Supported SDK & API Protocol
- Google Gemini API (generateContent) + OpenAI-Compatible SDK
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.15/1M in) across full 2K window
- Prompt Cache Write & TTL Policy
- Implicit + Explicit Context Caching at $0/1M with configurable TTL
- Computer Use & Browser GUI Agent
- Text-only completion (No GUI action loop)
- MCP & Parallel Tool Calling
- Prompt-based JSON extraction only
- Web Search Grounding & Code Sandbox
- Built-in Google Search Grounding + Managed Python Code Execution Sandbox
- Fine-Tuning & Distillation Support
- Vertex AI Supervised Adapter Tuning & Context Distillation
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster)
- Minimum GPU VRAM Requirement
- Zero Local VRAM Required (100% Serverless Cloud API)
- Supported Inference Engines
- Managed Google Cloud API
- System Card & Safety Framework
- Google DeepMind Frontier Safety Framework Verified
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Gemini Embedding 001 Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $15.50/mo vs $20 flat plan).
Break-even point: ~2,222 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Gemini Embedding 001:
Benchmark Scores & 6-Axis Capability Radar
- Chatbot Arena Elo
- 1280pts
Strengths
- élevé MTEB récupération leaderboard ranking
- faible coût par millions tokens
- rapide vector génération
Limitations & Trade-offs
- 2K fenêtre de contexte limit
- texte-seulement — no visuel embeddings
Editorial Assessment
standard d'entreprise bourreau de travail pour texte-based RAG architectures.
Best for: d'entreprise vector recherche, document indexing, et semantic clustering
Compare Gemini Embedding 001
Frequently Asked Questions about Gemini Embedding 001
How much does the Gemini Embedding 001 API cost per 1M tokens?+
Gemini Embedding 001 by Google is priced at $0.15 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Gemini Embedding 001 pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Gemini Embedding 001's context window?+
Gemini Embedding 001 features a 2K tokens context window, which equals approximately 1,536 English words or ~4 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 4,096 tokens.
Can I run Gemini Embedding 001 locally on my own GPU (Open Weights)?+
No, Gemini Embedding 001 is a proprietary closed-weights model hosted by Google. It cannot be downloaded for local GPU execution and must be accessed via Google's official API or cloud platform.
Does Google train on data sent to the Gemini Embedding 001 API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (Google Terms). Full commercial ownership of generated outputs. Supported deployment channels include: Google AI Studio API, Google Cloud Vertex AI.
How does Gemini Embedding 001 perform on coding and reasoning benchmarks?+
Gemini Embedding 001 achieves a composite Intelligence Index of 70/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, N/A% on MMLU-Pro, and 1280 pts on Chatbot Arena Elo.
What is Gemini Embedding 001 best used for?+
Gemini Embedding 001 is best suited for: d'entreprise vector recherche, document indexing, et semantic clustering. Assessment: standard d'entreprise bourreau de travail pour texte-based RAG architectures.
Google Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Google's flagship reasoning tier:
Gemini 4 Argon
Gemini 3.8 Flash
Gemini 3.7 Flash
Gemini 3.6 Flash
Gemini 3.5 Flash-Lite
Gemini Omni Flash
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Gemini Embedding 001:
L'unité textuelle de base traitée par un modèle linguistique, représentant généralement 3 à 4 caractères ou environ 0,75 mot. Par exemple, une phrase courante est découpée en plusieurs jetons.
Une suite de tests standardisés permettant d'évaluer et de comparer objectivement les performances des modèles d'IA sur des tâches ciblées (code avec SWE-bench, mathématiques avec AIME, etc.).
Le nombre maximal de jetons qu'un modèle peut traiter simultanément (entrée et sortie comprises). Une fenêtre de 1 000 000 jetons permet d'analyser environ 750 000 mots en une seule requête.
L'architecture de réseau neuronal introduite en 2017 à la base des LLM modernes, utilisant l'auto-attention pour traiter tous les jetons en parallèle plutôt que séquentiellement.
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Google Cloud Vertex AI Embeddings↗