Gemini 3.5 Flash vs Gemini Deep Research
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Gemini 3.5 Flash by Google and Gemini Deep Research 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 holds a notable pricing advantage for token ingestion. On output generation, Gemini 3.5 Flash 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 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 3.5 Flash
高度な推論モデルGoogle が提供する Gemini 3.5 Flash は、最大 1.0M トークンのコンテキストウィンドウを備え、入力 $0.75/100万トークン・出力 $3.75/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.75 / 1M
$3.75 / 1M
1.05M tokens
70.2%
Gemini Deep Research
高度な推論モデルGoogle が提供する Gemini Deep Research は、最大 1.0M トークンのコンテキストウィンドウを備え、入力 $10/100万トークン・出力 $40/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$10.00 / 1M
$40.00 / 1M
1.05M tokens
N/A
ひと目でわかる主な違い
- 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 |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 3.5 Flash · Strengths & Key Advantages
- Google の高度な Optimized Search-Grade Multimodal MoE アーキテクチャと 1.0M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.75/1M、出力 $3.75/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Gemini Deep Research · Strengths & Key Advantages
- Google の高度な Agentic recursive research and synthesis engine アーキテクチャと 1.0M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $10/1M、出力 $40/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
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 |
技術仕様・スペック
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: よくある質問 (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.
総合評価
Verdict & Model Selection Recommendation: Choose Gemini 3.5 Flash if your priority is Customer-facing conversational bots, search augmentation, and multimedia analysis. Its performance in demanding tasks justifies its operational token cost. Choose Gemini Deep Research if your workflow prioritizes Financial due diligence, competitive intelligence, and scientific literature reviews. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Gemini 3.5 Flash compare to Gemini Deep Research in API token pricing and cost per token?+
Gemini 3.5 Flash costs $0.75 per 1M input tokens and $3.75 per 1M output tokens, whereas Gemini Deep Research costs $10.00 input and $40.00 output. Overall, Gemini 3.5 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 3.5 Flash or Gemini Deep Research?+
Both Gemini 3.5 Flash and Gemini Deep Research 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 3.5 Flash or Gemini Deep Research?+
Gemini 3.5 Flash scores 70.2% on SWE-bench Verified, while Gemini Deep Research scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemini 3.5 Flash provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemini 3.5 Flash and Gemini Deep Research?+
Gemini 3.5 Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini Deep Research provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemini 3.5 Flash and Gemini Deep Research?+
Gemini 3.5 Flash generates approximately 190 tokens per second, while Gemini Deep Research delivers 60 tokens per second. In latency-sensitive production environments, Gemini 3.5 Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini 3.5 Flash and Gemini Deep Research to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 3.5 Flash to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini Deep Research. 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.
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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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs