Gemini Omni Flash vs Veo 3.1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
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.
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Gemini Omni Flash
深度推理模型Google 推出的 Gemini Omni Flash 人工智能模型，具备 1.0M 上下文窗口，输入价格为 $2/百万 Token，输出价格为 $15/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$2.00 / 1M
$15.00 / 1M
1.05M tokens
N/A
Veo 3.1
旗舰 frontier 大模型Google 推出的 Veo 3.1 人工智能模型，具备 4K 上下文窗口，输入价格为 $7/百万 Token，输出价格为 $450/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$7.00 / 1M
$450.00 / 1M
4.1K tokens
N/A
核心差异一览
- 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.
| Feature | Gemini Omni Flash | Veo 3.1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 88 |
Provider | ||
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 text | 4.1K tokens~6 A4 pages of text |
Max Output Tokens Per single request | 33K | 4K |
Throughput Speed Tokens generated per second | 75 tok/s | 14 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.00 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini Omni Flash · Strengths & Key Advantages
- 由 Google 研发的高精度 Unified real-time text-to-video and conversational streaming engine 架构，支持 1.0M 上下文窗口
- 极具竞争力的 API 定价（输入 $2/1M，输出 $15/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Veo 3.1 · Strengths & Key Advantages
- 由 Google 研发的高精度 Controllable audio-video generation and in-context video editor 架构，支持 4K 上下文窗口
- 极具竞争力的 API 定价（输入 $7/1M，输出 $450/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Omni Flash | Veo 3.1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.50 | $3.00 |
Output Tokens / Task Tokens generated per task | 15K | 0K |
Reasoning Tokens / Task Thinking tokens burned per task | 4K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini Omni Flash | Veo 3.1 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-omni-flash | models/veo-3-1 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-06 | 2025-10 |
| Knowledge Cutoff | February 2026 | July 2025 |
| Architecture | Unified real-time text-to-video and conversational streaming engine | Controllable audio-video generation and in-context video editor |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 10,000 – 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 tokens | Flat rate ($7/1M in) across full 4K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.5/1M with configurable TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Prompt-based JSON extraction only |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text 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 VRAM | Cloud-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 Classification | Google DeepMind Frontier Safety Framework Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | video | video |
Gemini Omni Flash vs Veo 3.1: 常见问题解答 (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.
综合结论
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.
常见问题 (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%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs