Google의 Gemini Omni Flash 모델은 시네마틱 AI 비디오 생성 및 자연스러운 모션 합성에 최적화된 AI 모델로, 1.0M 컨텍스트 윈도우, 초당 75 토큰의 생성 속도, 종합 지능 지수 86/100점(1M 토큰당 입력 $2 / 출력 $15)을 제공합니다.
Technical Specifications
- Provider
- Category
- flagship
- Release Date
- 2026-06
- Batch API Discount
- 30% off async batch
- Consumer Subscription
- $20 / month
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- video
- 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-omni-flash
- 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 $0.5/1M 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 Omni Flash Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~33 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 Omni Flash:
Benchmark Scores & 6-Axis Capability Radar
- Chatbot Arena Elo
- 1440pts
Strengths
- 1.0M 컨텍스트 윈도우 및 최대 33K 출력 토큰 생성 지원 (Arena Elo 1440)
- 초당 75 토큰의 빠른 처리 속도와 800ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $2 / 출력 $15)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Limitations & Trade-offs
- 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
- 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다
Editorial Assessment
지능 지수 86/100점, 1.0M 컨텍스트, 초당 75 토큰의 속도를 갖춘 Gemini Omni Flash 모델은 Google 생태계에서 성능과 경제성을 모두 만족시키는 검증된 선택입니다.
Best for: 시네마틱 AI 비디오 생성 및 자연스러운 모션 합성, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.
Compare Gemini Omni Flash
Frequently Asked Questions about Gemini Omni Flash
How much does the Gemini Omni Flash API cost per 1M tokens?+
Gemini Omni Flash by Google is priced at $2 per 1M input tokens and $15 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.5 per 1M tokens, and asynchronous Batch API requests receive a 30% discount.
Should I use a $20/month subscription or the Gemini Omni Flash 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 Omni Flash's context window?+
Gemini Omni Flash 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 Omni Flash locally on my own GPU (Open Weights)?+
No, Gemini Omni Flash 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 Omni Flash 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 Omni Flash perform on coding and reasoning benchmarks?+
Gemini Omni Flash achieves a composite Intelligence Index of 86/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, N/A% on MMLU-Pro, and 1440 pts on Chatbot Arena Elo.
What is Gemini Omni Flash best used for?+
Gemini Omni Flash is best suited for: 시네마틱 AI 비디오 생성 및 자연스러운 모션 합성, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.. Assessment: 지능 지수 86/100점, 1.0M 컨텍스트, 초당 75 토큰의 속도를 갖춘 Gemini Omni Flash 모델은 Google 생태계에서 성능과 경제성을 모두 만족시키는 검증된 선택입니다.
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 3.5 Live Translate
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Gemini Omni Flash:
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.