Google의 Gemini 3.5 Flash-Lite 모델은 1.0M 토큰 컨텍스트 윈도우와 입력 $0.4/1M 토큰, 출력 $2/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
Technical Specifications
- Provider
- Category
- lightweight
- Release Date
- 2026-07
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- $20 / month
- Reasoning Effort Control
- Configurable (Low / Med / High)
- 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-3-5-flash-lite
- Supported SDK & API Protocol
- Google Gemini API (generateContent) + OpenAI-Compatible SDK
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- 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 $0.1/1M 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)
- 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 3.5 Flash-Lite Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~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 3.5 Flash-Lite:
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 81.2%
- HumanEval
- 89.5%
- Chatbot Arena Elo
- 1375pts
Strengths
- Google의 첨단 Lightweight efficient multimodal transformer 아키텍처와 최대 1.0M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.4/1M, 출력 $2/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Limitations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Editorial Assessment
Gemini 3.5 Flash-Lite은(는) 성능, 속도, 비용 효율성의 균형이 뛰어난 Google의 핵심 AI 모델입니다.
Best for: 기업 업무 자동화, 소프트웨어 개발, 자율형 AI 에이전트 및 Google 기반 프로덕션 파이프라인
Compare Gemini 3.5 Flash-Lite
Frequently Asked Questions about Gemini 3.5 Flash-Lite
How much does the Gemini 3.5 Flash-Lite API cost per 1M tokens?+
Gemini 3.5 Flash-Lite by Google is priced at $0.4 per 1M input tokens and $2 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.1 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Gemini 3.5 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 3.5 Flash-Lite's context window?+
Gemini 3.5 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 32,768 tokens.
Can I run Gemini 3.5 Flash-Lite locally on my own GPU (Open Weights)?+
No, Gemini 3.5 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 3.5 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 3.5 Flash-Lite perform on coding and reasoning benchmarks?+
Gemini 3.5 Flash-Lite achieves a composite Intelligence Index of 69/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 81.2% on MMLU-Pro, and 1375 pts on Chatbot Arena Elo.
What is Gemini 3.5 Flash-Lite best used for?+
Gemini 3.5 Flash-Lite is best suited for: 기업 업무 자동화, 소프트웨어 개발, 자율형 AI 에이전트 및 Google 기반 프로덕션 파이프라인. Assessment: Gemini 3.5 Flash-Lite은(는) 성능, 속도, 비용 효율성의 균형이 뛰어난 Google의 핵심 AI 모델입니다.
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 Omni Flash
Gemini 3.5 Live Translate
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Gemini 3.5 Flash-Lite:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.