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.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Nano Banana
심층 추론 모델Google의 Nano Banana 모델은 32K 토큰 컨텍스트 윈도우와 입력 $2/1M 토큰, 출력 $20/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$2.00 / 1M
$20.00 / 1M
32.8K tokens
N/A
Imagen 4
플래그십 프론티어 모델Google의 Imagen 4 모델은 2K 토큰 컨텍스트 윈도우와 입력 $40/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$40.00 / 1M
$0.00 / 1M
2K tokens
N/A
한눈에 보는 핵심 차이점
- 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 |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nano Banana · Strengths & Key Advantages
- Google의 첨단 Conversational in-context image generation and editing transformer 아키텍처와 최대 32K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $2/1M, 출력 $20/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Imagen 4 · Strengths & Key Advantages
- Google의 첨단 High-resolution multimodal image diffusion transformer 아키텍처와 최대 2K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $40/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
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 |
기술 사양 (Specifications)
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: 핵심 요약 문답 (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.
종합 평가
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.
자주 묻는 질문 (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.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs