GetInfoUs
Head-to-Head Model Comparison

Gemini Omni Flash vs Veo 3.1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
공유
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini Omni Flash vs Veo 3.1: Benchmarks, Pricing & Speed Comparison

Gemini Omni Flash by Google and Veo 3.1 by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Gemini Omni Flash holds a notable pricing advantage for token ingestion. On output generation, Gemini Omni Flash is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini Omni Flash provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini Omni Flash leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.

1:1 모델 맞대결 비교 (VS)

원하는 AI 모델 선택 및 실시간 비교

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
Google

Gemini Omni Flash

심층 추론 모델

Google의 Gemini Omni Flash 모델은 1.0M 토큰 컨텍스트 윈도우와 입력 $2/1M 토큰, 출력 $15/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index86 / 100
Input Token:

$2.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Google

Veo 3.1

플래그십 프론티어 모델

Google의 Veo 3.1 모델은 4K 토큰 컨텍스트 윈도우와 입력 $7/1M 토큰, 출력 $450/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index88 / 100
Input Token:

$7.00 / 1M

Output Token:

$450.00 / 1M

Context Window:

4.1K tokens

SWE-bench Verified:

N/A

한눈에 보는 핵심 차이점

  • Gemini Omni Flash is 24.1x cheaper for API usage (input + output tokens combined).
  • Gemini Omni Flash has a larger context window (1M vs 4K tokens).
  • Gemini Omni Flash is faster at 75 tok/s vs 14 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini Omni FlashVeo 3.1
Intelligence Index
Composite reasoning & knowledge score (0-100)
8688
Provider
GoogleGoogle
Input Price
Cost per million input tokens
$2.00 / 1M$7.00 / 1M
Output Price
Cost per million output tokens
$15.00 / 1M$450.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.25 / 1M$46.40 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text4.1K tokens~6 A4 pages of text
Max Output Tokens
Per single request
33K4K
Throughput Speed
Tokens generated per second
75 tok/s14 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/A
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.00 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Gemini Omni Flash
24.1x cheaper monthly runtime
Speed
Gemini Omni Flash
+436% faster generation
Context
Gemini Omni Flash
1.05M tokens window

검증된 벤치마크 점수 비교

Official evaluation metrics verified by independent third-party research labs.

Gemini Omni FlashVeo 3.1
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1440ptsvs1450pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs14 tok/s

지연 시간(Latency) 및 응답 속도

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Gemini Omni Flash0.80s
Veo 3.14.20s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini Omni Flash7.47s
Veo 3.139.91s

실시간 API 토큰 비용 시뮬레이터

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Gemini Omni Flash Monthly API Bill
$17.25 / month
$2/1M in · $15/1M out
Veo 3.1 Monthly API Bill
$415.50 / month
$7/1M in · $450/1M out
Gemini Omni Flash saves approximately $398.25 / month (96%) under this workload.24.1x Savings

Gemini Omni Flash · Strengths & Key Advantages

  • Google의 첨단 Unified real-time text-to-video and conversational streaming engine 아키텍처와 최대 1.0M 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $2/1M, 출력 $15/1M)과 빠른 응답 속도
  • 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
  • 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
  • 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Veo 3.1 · Strengths & Key Advantages

  • Google의 첨단 Controllable audio-video generation and in-context video editor 아키텍처와 최대 4K 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $7/1M, 출력 $450/1M)과 빠른 응답 속도
  • 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
  • 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
  • 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricGemini Omni FlashVeo 3.1
Cost per Task
Estimated USD to finish one task
$1.50$3.00
Output Tokens / Task
Tokens generated per task
15K0K
Reasoning Tokens / Task
Thinking tokens burned per task
4K0K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureGemini Omni FlashVeo 3.1
Official API Model ID (SDK)models/gemini-omni-flashmodels/veo-3-1
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2026-062025-10
Knowledge CutoffFebruary 2026July 2025
ArchitectureUnified real-time text-to-video and conversational streaming engineControllable audio-video generation and in-context video editor
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFlat rate ($7/1M in) across full 4K window
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.5/1M with configurable TTLImplicit + Explicit Context Caching at $0/1M with configurable TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaPrompt-based JSON extraction only
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationVertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationGoogle DeepMind Frontier Safety Framework VerifiedGoogle DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiesvideovideo

Gemini Omni Flash vs Veo 3.1: 핵심 요약 문답 (FAQ)

Which is more intelligent, Gemini Omni Flash or Veo 3.1?

Veo 3.1 is more intelligent. It scores 88 compared with 86 on the composite Intelligence Index.

Which model is faster?

Gemini Omni Flash is faster, generating 75 tokens per second compared with 14 tokens per second.

Which model is cheaper to run?

Gemini Omni Flash is cheaper at $2.25 per 1M tokens blended, compared with $46.40 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini Omni Flash has lower latency, with a time to first token of 0.80s compared with 4.20s.

Which model has a larger context window?

Gemini Omni Flash has a larger context window, supporting 1.05M tokens compared with 4.1K tokens.

종합 평가

Verdict & Model Selection Recommendation: Choose Gemini Omni Flash if your priority is Interactive gaming, real-time avatar response, and educational visual explanations. Its performance in demanding tasks justifies its operational token cost. Choose Veo 3.1 if your workflow prioritizes VFX post-production, film editing workflows, and high-end video advertising. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.

자주 묻는 질문 (FAQ)

How does Gemini Omni Flash compare to Veo 3.1 in API token pricing and cost per token?+

Gemini Omni Flash costs $2.00 per 1M input tokens and $15.00 per 1M output tokens, whereas Veo 3.1 costs $7.00 input and $450.00 output. Overall, Gemini Omni Flash is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.

Which model has a larger context window: Gemini Omni Flash or Veo 3.1?+

Gemini Omni Flash supports a context window of 1,049K tokens (~786k words), while Veo 3.1 supports 4K tokens (~3k words). Therefore, Gemini Omni Flash can process more textual data and document context in a single prompt.

Which AI model is better for software engineering and coding: Gemini Omni Flash or Veo 3.1?+

Gemini Omni Flash scores at the frontier of coding benchmarks, while Veo 3.1 scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemini Omni Flash provides robust code generation and tool calling.

What is the difference in reasoning capability and extended thinking between Gemini Omni Flash and Veo 3.1?+

Gemini Omni Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Veo 3.1 focuses on standard fast inference.

How does inference speed (tokens per second) compare between Gemini Omni Flash and Veo 3.1?+

Gemini Omni Flash generates approximately 75 tokens per second, while Veo 3.1 delivers 14 tokens per second. In latency-sensitive production environments, Gemini Omni Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining Gemini Omni Flash and Veo 3.1 to optimize costs?+

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini Omni Flash to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Veo 3.1. This tiered routing pattern cuts overall API bills by up to 70%.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs