Nano Banana Pro vs Nano Banana
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Nano Banana Pro by Google and Nano Banana 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, Nano Banana is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nano Banana Pro provides an expanded memory horizon with 66K 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 Pro
深度推理模型Google 推出的 Nano Banana Pro 人工智能模型，具备 65K 上下文窗口，输入价格为 $3.5/百万 Token，输出价格为 $35/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$3.50 / 1M
$35.00 / 1M
65.5K tokens
N/A
Nano Banana
深度推理模型Google 推出的 Nano Banana 人工智能模型，具备 32K 上下文窗口，输入价格为 $2/百万 Token，输出价格为 $20/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$2.00 / 1M
$20.00 / 1M
32.8K tokens
N/A
核心差异一览
- Nano Banana is 1.7x cheaper for API usage (input + output tokens combined).
- Nano Banana Pro has a larger context window (66K vs 33K tokens).
- Nano Banana is faster at 60 tok/s vs 50 tok/s.
| Feature | Nano Banana Pro | Nano Banana |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 83 | 75 |
Provider | ||
Input Price Cost per million input tokens | $3.50 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $35.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $4.76 / 1M | $2.75 / 1M |
Context Window Maximum input + output tokens | 65.5K tokens~98 A4 pages of text | 32.8K tokens~49 A4 pages of text |
Max Output Tokens Per single request | 16K | 8K |
Throughput Speed Tokens generated per second | 50 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.80 / 1M | $0.50 / 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.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
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.
Nano Banana Pro · Strengths & Key Advantages
- 由 Google 研发的高精度 High-fidelity design-grade image generation and compositional editor 架构，支持 65K 上下文窗口
- 极具竞争力的 API 定价（输入 $3.5/1M，输出 $35/1M），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
Nano Banana · Strengths & Key Advantages
- 由 Google 研发的高精度 Conversational in-context image generation and editing transformer 架构，支持 32K 上下文窗口
- 极具竞争力的 API 定价（输入 $2/1M，输出 $20/1M），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.
| Metric | Nano Banana Pro | Nano Banana |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $0.03 |
Output Tokens / Task Tokens generated per task | 2K | 1K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Nano Banana Pro | Nano Banana |
|---|---|---|
| Official API Model ID (SDK) | models/nano-banana-pro | models/nano-banana |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-11 | 2025-08 |
| Knowledge Cutoff | September 2025 | June 2025 |
| Architecture | High-fidelity design-grade image generation and compositional editor | Conversational in-context image generation and editing 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 ($3.5/1M in) across full 66K window | Flat rate ($2/1M in) across full 33K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.8/1M with configurable TTL | Implicit + Explicit Context Caching at $0.5/1M 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) | 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 | text, vision |
| Output Modalities | image | image |
Nano Banana Pro vs Nano Banana: 常见问题解答 (FAQ)
Which is more intelligent, Nano Banana Pro or Nano Banana?
Nano Banana Pro is more intelligent. It scores 83 compared with 75 on the composite Intelligence Index.
Which model is faster?
Nano Banana is faster, generating 60 tokens per second compared with 50 tokens per second.
Which model is cheaper to run?
Nano Banana is cheaper at $2.75 per 1M tokens blended, compared with $4.76 (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 1.20s.
Which model has a larger context window?
Nano Banana Pro has a larger context window, supporting 65.5K tokens compared with 32.8K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Nano Banana Pro if your priority is Brand kit production, professional UI asset generation, and commercial marketing campaigns. Its performance in demanding tasks justifies its operational token cost. Choose Nano Banana if your workflow prioritizes Storyboarding, consistent character illustration, and interactive image tweaking. 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 Nano Banana Pro compare to Nano Banana in API token pricing and cost per token?+
Nano Banana Pro costs $3.50 per 1M input tokens and $35.00 per 1M output tokens, whereas Nano Banana costs $2.00 input and $20.00 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 Pro or Nano Banana?+
Nano Banana Pro supports a context window of 66K tokens (~49k words), while Nano Banana supports 33K tokens (~25k words). Therefore, Nano Banana Pro can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nano Banana Pro or Nano Banana?+
Nano Banana Pro scores at the frontier of coding benchmarks, while Nano Banana scores competitively for software generation. For complex autonomous debugging and agentic engineering, Nano Banana Pro provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Nano Banana Pro and Nano Banana?+
Nano Banana Pro features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Nano Banana provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nano Banana Pro and Nano Banana?+
Nano Banana Pro generates approximately 50 tokens per second, while Nano Banana delivers 60 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 Pro and Nano Banana 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 Nano Banana Pro. 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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs