Nano Banana vs Imagen 4
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Nano Banana by Google and Imagen 4 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, Nano Banana holds a notable pricing advantage for token ingestion. On output generation, Imagen 4 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nano Banana provides an expanded memory horizon with 33K tokens. In latency-sensitive production environments, Nano Banana 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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Nano Banana
高度な推論モデルGoogle が提供する Nano Banana は、最大 32K トークンのコンテキストウィンドウを備え、入力 $2/100万トークン・出力 $20/100万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。
$2.00 / 1M
$20.00 / 1M
32.8K tokens
N/A
Imagen 4
フラッグシップ最先端モデルGoogle が提供する Imagen 4 は、最大 2K トークンのコンテキストウィンドウを備え、入力 $40/100万トークン・出力 $0/100万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。
$40.00 / 1M
$0.00 / 1M
2K tokens
N/A
ひと目でわかる主な違い
- Nano Banana is 2.8x cheaper for API usage (input + output tokens combined).
- Nano Banana has a larger context window (33K vs 2K tokens).
- Nano Banana is faster at 60 tok/s vs 25 tok/s.
| Feature | Nano Banana | Imagen 4 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 75 | 82 |
Provider | ||
Input Price Cost per million input tokens | $2.00 / 1M | $40.00 / 1M |
Output Price Cost per million output tokens | $20.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.75 / 1M | $8.00 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 2K tokens~3 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 60 tok/s | 25 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 |
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.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
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.
Nano Banana · Strengths & Key Advantages
- Google の高度な Conversational in-context image generation and editing transformer アーキテクチャと 32K 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 $2/1M、出力 $20/1M）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
Imagen 4 · Strengths & Key Advantages
- Google の高度な High-resolution multimodal image diffusion transformer アーキテクチャと 2K 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 $40/1M、出力 $0/1M）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 | Nano Banana | Imagen 4 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $0.04 |
Output Tokens / Task Tokens generated per task | 1K | 0K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Nano Banana | Imagen 4 |
|---|---|---|
| Official API Model ID (SDK) | models/nano-banana | models/imagen-4 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-08 | 2025-05 |
| Knowledge Cutoff | June 2025 | March 2025 |
| Architecture | Conversational in-context image generation and editing transformer | High-resolution multimodal image diffusion transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 33K window | Flat rate ($40/1M in) across full 2K 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) | Text-only completion (No GUI action loop) |
| 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) |
| 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 |
| Output Modalities | image | image |
Nano Banana vs Imagen 4: よくある質問 (FAQ)
Which is more intelligent, Nano Banana or Imagen 4?
Imagen 4 is more intelligent. It scores 82 compared with 75 on the composite Intelligence Index.
Which model is faster?
Nano Banana is faster, generating 60 tokens per second compared with 25 tokens per second.
Which model is cheaper to run?
Nano Banana is cheaper at $2.75 per 1M tokens blended, compared with $8.00 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nano Banana has lower latency, with a time to first token of 0.90s compared with 2.00s.
Which model has a larger context window?
Nano Banana has a larger context window, supporting 32.8K tokens compared with 2K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Nano Banana if your priority is Storyboarding, consistent character illustration, and interactive image tweaking. Its performance in demanding tasks justifies its operational token cost. Choose Imagen 4 if your workflow prioritizes Professional advertising, print-ready media, and studio graphic design. 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 Nano Banana compare to Imagen 4 in API token pricing and cost per token?+
Nano Banana costs $2.00 per 1M input tokens and $20.00 per 1M output tokens, whereas Imagen 4 costs $40.00 input and $0.00 output. Overall, Nano Banana 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: Nano Banana or Imagen 4?+
Nano Banana supports a context window of 33K tokens (~25k words), while Imagen 4 supports 2K tokens (~2k words). Therefore, Nano Banana can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nano Banana or Imagen 4?+
Nano Banana scores at the frontier of coding benchmarks, while Imagen 4 scores competitively for software generation. For complex autonomous debugging and agentic engineering, Nano Banana provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Nano Banana and Imagen 4?+
Nano Banana features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Imagen 4 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Nano Banana and Imagen 4?+
Nano Banana generates approximately 60 tokens per second, while Imagen 4 delivers 25 tokens per second. In latency-sensitive production environments, Nano Banana provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nano Banana and Imagen 4 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nano Banana to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Imagen 4. 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