de Google le plus bas-coût multimodal modèle pour élevé-concurrency routage et léger données extraction.
Technical Specifications
- Provider
- Category
- lightweight
- Release Date
- 2025-02
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- $20 / month
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text
- 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-2-0-flash-lite
- 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) · 4,000,000 – 10,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
- Prompt Cache Write & TTL Policy
- Implicit + Explicit Context Caching at $0.01875/1M with configurable TTL
- Computer Use & Browser GUI Agent
- Supported (Screen coordinate grounding, browser & desktop GUI action loops)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- 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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
- 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 2.0 Flash-Lite Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $12.69/mo vs $20 flat plan).
Break-even point: ~1,368 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 2.0 Flash-Lite:
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 72.5%
- HumanEval
- 82%
- Chatbot Arena Elo
- 1305pts
Strengths
- Extremely abordable token tarification
- complet 1M token fenêtre de contexte
- ultra-rapide temps-à-premier-token
Limitations & Trade-offs
- Weaker raisonnement sur complexe STEM et codage queries
- limité multitour nuance sur abstract topics
Editorial Assessment
Maximum débit et economy pour straightforward texte et vision tâches.
Best for: élevé-volume classification, extraction, et périphérie routage
Compare Gemini 2.0 Flash-Lite
Frequently Asked Questions about Gemini 2.0 Flash-Lite
How much does the Gemini 2.0 Flash-Lite API cost per 1M tokens?+
Gemini 2.0 Flash-Lite by Google is priced at $0.075 per 1M input tokens and $0.3 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.01875 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Gemini 2.0 Flash-Lite 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 2.0 Flash-Lite's context window?+
Gemini 2.0 Flash-Lite features a 1.0M tokens context window, which equals approximately 786,432 English words or ~2,097 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Gemini 2.0 Flash-Lite locally on my own GPU (Open Weights)?+
No, Gemini 2.0 Flash-Lite 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 2.0 Flash-Lite 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 2.0 Flash-Lite perform on coding and reasoning benchmarks?+
Gemini 2.0 Flash-Lite achieves a composite Intelligence Index of 48/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 72.5% on MMLU-Pro, and 1305 pts on Chatbot Arena Elo.
What is Gemini 2.0 Flash-Lite best used for?+
Gemini 2.0 Flash-Lite is best suited for: élevé-volume classification, extraction, et périphérie routage. Assessment: Maximum débit et economy pour straightforward texte et vision tâches.
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 2.0 Flash-Lite:
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.
La capacité d'un LLM à déterminer quand une action externe est requise et à générer des arguments structurés pour invoquer des API, calculatrices ou bases de données avant de poursuivre.
Un standard ouvert créé par Anthropic permettant aux agents d'IA de se connecter de façon uniforme aux systèmes de fichiers locaux, aux environnements de développement et aux API tierces.
Une optimisation tarifaire et technique conservant les états d'attention des préfixes de prompts récurrents, réduisant les coûts d'entrée de 50 à 90 % tout en abaissant la latence.
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.