Gemini 3.6 Flash vs Gemini 3.5 Flash-Lite
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Google 的 Gemini 3.6 Flash 与 Google 的 Gemini 3.5 Flash-Lite 是当前备受关注的两款主力 AI 模型。Gemini 3.6 Flash 提供 1.05M Token 上下文窗口,API 定价为每百万 Token 输入 $0.75 / 输出 $3.75;而 Gemini 3.5 Flash-Lite 提供 1.05M Token 上下文窗口,定价为每百万 Token 输入 $0.40 / 输出 $2.00。以下为两者的基准测试、延迟与成本全面对比。
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Gemini 3.6 Flash
顶级编程与软件工程Google 推出的 Gemini 3.6 Flash 是一款专为自主软件工程、跨文件代码重构与复杂调试打造的旗舰编程大模型,具备 1.0M 上下文窗口、185 Token/秒生成速度与 84/100 综合智能指数(每百万 Token $0.75 输入 / $3.75 输出)。
$0.75 / 1M
$3.75 / 1M
1.05M tokens
72%
Gemini 3.5 Flash-Lite
超高速低延迟推理Google 推出的 Gemini 3.5 Flash-Lite 是一款面向实时交互对话、高并发路由与极低延迟生产环境的超高速大模型,具备 1.0M 上下文窗口、240 Token/秒生成速度与 69/100 综合智能指数(每百万 Token $0.4 输入 / $2 输出)。
$0.40 / 1M
$2.00 / 1M
1.05M tokens
N/A
核心差异一览
- Gemini 3.5 Flash-Lite is 1.9x cheaper for API usage (input + output tokens combined).
- Gemini 3.5 Flash-Lite is faster at 240 tok/s vs 185 tok/s.
| Feature | Gemini 3.6 Flash | Gemini 3.5 Flash-Lite |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 84 | 69 |
Provider | ||
Input Price Cost per million input tokens | $0.75 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $3.75 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.66 / 1M | $0.35 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 66K | 33K |
Throughput Speed Tokens generated per second | 185 tok/s | 240 tok/s |
SWE-bench Verified Real-world software engineering | 72% | N/A |
GPQA Diamond Graduate-level reasoning | 82.4% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.19 / 1M | $0.10 / 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.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
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 3.6 Flash · Strengths & Key Advantages
- 提供 1.0M 上下文窗口与最高 66K 输出容量,核心基准表现优异(SWE-bench Verified 72% · GPQA Diamond 82.4%)
- 高达 185 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 370 毫秒
- 极具竞争力的 API 定价(每百万 Token $0.75 输入 / $3.75 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上,深度略逊于顶配重型旗舰推理模型
Gemini 3.5 Flash-Lite · Strengths & Key Advantages
- 提供 1.0M 上下文窗口与最高 33K 输出容量,核心基准表现优异(MMLU-Pro 81.2% · Arena Elo 1375)
- 高达 240 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 290 毫秒
- 极具竞争力的 API 定价(每百万 Token $0.4 输入 / $2 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上,深度略逊于顶配重型旗舰推理模型
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 3.6 Flash | Gemini 3.5 Flash-Lite |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.02 | $0.55 |
Output Tokens / Task Tokens generated per task | 39K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 19K | 7K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3.6 Flash | Gemini 3.5 Flash-Lite |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-6-flash | models/gemini-3-5-flash-lite |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-07 | 2026-07 |
| Knowledge Cutoff | March 2026 | March 2026 |
| Architecture | Enhanced multimodal mixture-of-experts | Lightweight efficient multimodal transformer |
| 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1875/1M with configurable TTL | Implicit + Explicit Context Caching at $0.1/1M 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 | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | 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, audio, video | text, vision |
| Output Modalities | text | text |
Gemini 3.6 Flash vs Gemini 3.5 Flash-Lite: 常见问题解答 (FAQ)
Which is more intelligent, Gemini 3.6 Flash or Gemini 3.5 Flash-Lite?
Gemini 3.6 Flash is more intelligent. It scores 84 compared with 69 on the composite Intelligence Index.
Which model is faster?
Gemini 3.5 Flash-Lite is faster, generating 240 tokens per second compared with 185 tokens per second.
Which model is cheaper to run?
Gemini 3.5 Flash-Lite is cheaper at $0.35 per 1M tokens blended, compared with $0.66 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.5 Flash-Lite has lower latency, with a time to first token of 0.29s compared with 0.37s.
综合结论
综合来看,Gemini 3.6 Flash(适合:专为自主软件工程、跨文件代码重构与复杂调试打造的旗舰编程任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。)与 Gemini 3.5 Flash-Lite(适合:面向实时交互对话、高并发路由与极低延迟生产环境的超高速任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。)各具优势:Gemini 3.6 Flash 在综合智能与编程基准测试中表现更强,Gemini 3.5 Flash-Lite 拥有更快的 Token 生成速度,而 Gemini 3.5 Flash-Lite 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面,Gemini 3.6 Flash 和 Gemini 3.5 Flash-Lite 哪个更强?+
在综合智能指数与编程基准评测中,Gemini 3.6 Flash 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
Gemini 3.6 Flash 与 Gemini 3.5 Flash-Lite 的 API 价格哪个更便宜?+
Gemini 3.6 Flash 的 API 价格为输入 $0.75 / 输出 $3.75(每百万 Token),Gemini 3.5 Flash-Lite 为输入 $0.40 / 输出 $2.00。综合混合成本来看,Gemini 3.5 Flash-Lite 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度?+
Gemini 3.6 Flash 支持 1.05M Token 上下文窗口,而 Gemini 3.5 Flash-Lite 支持 1.05M Token。在流式生成速度方面,Gemini 3.5 Flash-Lite 的吞吐表现更快。
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: Google 与 Google 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。