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Head-to-Head Model Comparison

Gemini 3.6 Flash vs Gemini 3.5 Flash-Lite

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini 3.6 Flash vs Gemini 3.5 Flash-Lite: Benchmarks, Pricing & Speed Comparison

Gemini 3.6 Flash by Google and Gemini 3.5 Flash-Lite 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 3.5 Flash-Lite holds a notable pricing advantage for token ingestion. On output generation, Gemini 3.5 Flash-Lite is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 3.5 Flash-Lite 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.

模型直接对比 (VS)

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Google

Gemini 3.6 Flash

深度推理模型

Google 推出的 Gemini 3.6 Flash 人工智能模型，具备 1.0M 上下文窗口，输入价格为 $0.75/百万 Token，输出价格为 $3.75/百万 Token，专为高质量生产环境、推理与自动化工作流打造。

Intelligence Index84 / 100
Input Token:

$0.75 / 1M

Output Token:

$3.75 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

72%

Google

Gemini 3.5 Flash-Lite

深度推理模型

Google 推出的 Gemini 3.5 Flash-Lite 人工智能模型，具备 1.0M 上下文窗口，输入价格为 $0.4/百万 Token，输出价格为 $2/百万 Token，专为高质量生产环境、推理与自动化工作流打造。

Intelligence Index69 / 100
Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

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.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 3.6 FlashGemini 3.5 Flash-Lite
Intelligence Index
Composite reasoning & knowledge score (0-100)
8469
Provider
GoogleGoogle
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 text1.05M tokens~1,573 A4 pages of text
Max Output Tokens
Per single request
66K33K
Throughput Speed
Tokens generated per second
185 tok/s240 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 framework
Coding

Evaluations ongoing

Cost
Gemini 3.5 Flash-Lite
1.9x cheaper monthly runtime
Speed
Gemini 3.5 Flash-Lite
+30% faster generation
Context
Tied
1.05M tokens window

权威基准测试得分对比

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

Gemini 3.6 FlashGemini 3.5 Flash-Lite
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

72%vsN/A
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

82.4%vsN/A
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

87.8%vs81.2%
LMSYS Chatbot Arena Elo

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

1460ptsvs1375pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

185 tok/s vs240 tok/s

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.

Time to First Token (TTFT)Lower is better
Gemini 3.6 Flash0.37s
Gemini 3.5 Flash-Lite0.29s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 3.6 Flash3.07s
Gemini 3.5 Flash-Lite2.37s

实时 Token 成本模拟器

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 3.6 Flash Monthly API Bill
$4.78 / month
$0.75/1M in · $3.75/1M out
Gemini 3.5 Flash-Lite Monthly API Bill
$2.55 / month
$0.4/1M in · $2/1M out
Gemini 3.5 Flash-Lite saves approximately $2.23 / month (47%) under this workload.1.9x Savings

Gemini 3.6 Flash · Strengths & Key Advantages

  • 由 Google 研发的高精度 Enhanced multimodal mixture-of-experts 架构，支持 1.0M 上下文窗口
  • 极具竞争力的 API 定价（输入 $0.75/1M，输出 $3.75/1M），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

Gemini 3.5 Flash-Lite · Strengths & Key Advantages

  • 由 Google 研发的高精度 Lightweight efficient multimodal transformer 架构，支持 1.0M 上下文窗口
  • 极具竞争力的 API 定价（输入 $0.4/1M，输出 $2/1M），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.

MetricGemini 3.6 FlashGemini 3.5 Flash-Lite
Cost per Task
Estimated USD to finish one task
$1.02$0.55
Output Tokens / Task
Tokens generated per task
39K18K
Reasoning Tokens / Task
Thinking tokens burned per task
19K7K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 3.6 FlashGemini 3.5 Flash-Lite
Official API Model ID (SDK)models/gemini-3-6-flashmodels/gemini-3-5-flash-lite
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2026-072026-07
Knowledge CutoffMarch 2026March 2026
ArchitectureEnhanced multimodal mixture-of-expertsLightweight efficient multimodal transformer
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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.1875/1M with configurable TTLImplicit + Explicit Context Caching at $0.1/1M 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 SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
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) · 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 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, vision, audio, videotext, vision
Output Modalitiestexttext

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

Verdict & Model Selection Recommendation: Choose Gemini 3.6 Flash if your priority is Software development assistance, technical video analysis, and responsive web apps. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 3.5 Flash-Lite if your workflow prioritizes Autonomous subagent loops, summarization, and data transformation. 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 3.6 Flash compare to Gemini 3.5 Flash-Lite in API token pricing and cost per token?+

Gemini 3.6 Flash costs $0.75 per 1M input tokens and $3.75 per 1M output tokens, whereas Gemini 3.5 Flash-Lite costs $0.40 input and $2.00 output. Overall, Gemini 3.5 Flash-Lite 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 3.6 Flash or Gemini 3.5 Flash-Lite?+

Both Gemini 3.6 Flash and Gemini 3.5 Flash-Lite offer an identical context window of 1,049K tokens (~786k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.

Which AI model is better for software engineering and coding: Gemini 3.6 Flash or Gemini 3.5 Flash-Lite?+

Gemini 3.6 Flash scores 72% on SWE-bench Verified, while Gemini 3.5 Flash-Lite scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemini 3.6 Flash provides robust code generation and tool calling.

What is the difference in reasoning capability and extended thinking between Gemini 3.6 Flash and Gemini 3.5 Flash-Lite?+

Gemini 3.6 Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 3.5 Flash-Lite provides chain-of-thought reasoning depth for complex math and science.

How does inference speed (tokens per second) compare between Gemini 3.6 Flash and Gemini 3.5 Flash-Lite?+

Gemini 3.6 Flash generates approximately 185 tokens per second, while Gemini 3.5 Flash-Lite delivers 240 tokens per second. In latency-sensitive production environments, Gemini 3.5 Flash-Lite provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining Gemini 3.6 Flash and Gemini 3.5 Flash-Lite to optimize costs?+

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 3.5 Flash-Lite to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 3.6 Flash. 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.

数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs