Gemini 3.5 Flash vs Gemini Deep Research
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Google 的 Gemini 3.5 Flash 与 Google 的 Gemini Deep Research 是当前备受关注的两款主力 AI 模型。Gemini 3.5 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 Deep Research 提供 1.05M Token 上下文窗口，定价为每百万 Token 输入 $10.00 / 输出 $40.00。以下为两者的基准测试、延迟与成本全面对比。
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Gemini 3.5 Flash
旗舰前沿大模型Google 推出的 Gemini 3.5 Flash 是一款面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿大模型，具备 1.0M 上下文窗口、190 Token/秒生成速度与 82/100 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
70.2%
Gemini Deep Research
自主智能体与工具专家Google 推出的 Gemini Deep Research 是一款专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的大模型，具备 1.0M 上下文窗口、60 Token/秒生成速度与 91/100 综合智能指数（每百万 Token $10 输入 / $40 输出）。
$10.00 / 1M
$40.00 / 1M
1.05M tokens
N/A
核心差异一览
- Gemini 3.5 Flash is 11.5x cheaper for API usage (input + output tokens combined).
- Gemini 3.5 Flash is faster at 190 tok/s vs 60 tok/s.
| Feature | Gemini 3.5 Flash | Gemini Deep Research |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 82 | 91 |
Provider | ||
Input Price Cost per million input tokens | $0.75 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $3.75 / 1M | $40.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.66 / 1M | $7.75 / 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 | 66K |
Throughput Speed Tokens generated per second | 190 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | 70.2% | N/A |
GPQA Diamond Graduate-level reasoning | 81% | 85% |
Cached Input Price Prompt caching discount per 1M tokens | $0.19 / 1M | $2.50 / 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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
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 3.5 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 70.2% · GPQA Diamond 81%）
- 高达 190 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 360 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 Deep Research · 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（GPQA Diamond 85% · MMLU-Pro 89.5%）
- 高达 60 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 1500 毫秒
- 极具竞争力的 API 定价（每百万 Token $10 输入 / $40 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
- 在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 3.5 Flash | Gemini Deep Research |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.05 | $8.50 |
Output Tokens / Task Tokens generated per task | 38K | 50K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 35K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3.5 Flash | Gemini Deep Research |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-5-flash | models/gemini-deep-research |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-05 | 2026-04 |
| Knowledge Cutoff | January 2026 | Real-time Google Search Integration |
| Architecture | Optimized Search-Grade Multimodal MoE | Agentic recursive research and synthesis engine |
| 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 $2.5/1M 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 3.5 Flash vs Gemini Deep Research: 常见问题解答 (FAQ)
Which is more intelligent, Gemini 3.5 Flash or Gemini Deep Research?
Gemini Deep Research is more intelligent. It scores 91 compared with 82 on the composite Intelligence Index.
Which model is faster?
Gemini 3.5 Flash is faster, generating 190 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
Gemini 3.5 Flash is cheaper at $0.66 per 1M tokens blended, compared with $7.75 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.5 Flash has lower latency, with a time to first token of 0.36s compared with 1.50s.
综合结论
综合来看，Gemini 3.5 Flash（适合：面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿任务、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 Deep Research（适合：专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的任务、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 Deep Research 在综合智能与编程基准测试中表现更强，Gemini 3.5 Flash 拥有更快的 Token 生成速度，而 Gemini 3.5 Flash 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 3.5 Flash 和 Gemini Deep Research 哪个更强？+
在综合智能指数与编程基准评测中，Gemini Deep Research 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 3.5 Flash 与 Gemini Deep Research 的 API 价格哪个更便宜？+
Gemini 3.5 Flash 的 API 价格为输入 $0.75 / 输出 $3.75（每百万 Token），Gemini Deep Research 为输入 $10.00 / 输出 $40.00。综合混合成本来看，Gemini 3.5 Flash 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 3.5 Flash 支持 1.05M Token 上下文窗口，而 Gemini Deep Research 支持 1.05M Token。在流式生成速度方面，Gemini 3.5 Flash 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)。