Nano Banana Pro Google
💰 Total Cost Calculation (from Plugin)
Output: $0.000000
Output: $0.000000
Unit: $67.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000 input tokens and 500 output tokens:
- Input Cost: $0.000000
- Output Cost: $0.000000
- Unit Cost: $67.000000
- Total Cost: $67.000000
- Cost per 1K tokens: $44.666667 (rounded ~ $44.67)
- Tokens per dollar: 22 tokens
- Context Window: 4096 tokens
- Thinking Source: (0 tokens)
Speed & Performance Analysis
With a processing speed of 1,200 tokens per second and 50ms time to first token:
- Processing Time: 1.43 seconds
- Latency: 50 milliseconds to first token
- Base Throughput: 1,200 tokens/second
Best Use Cases
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← Back to Nano Banana Pro| Rank | AI Model & Provider | Total Cost | vs Nano Banana Pro |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.021388 (rounded ~ $0.02) Best Value | ↓ 100% cheaper |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$0.053845 (rounded ~ $0.05) | ↓ 99.9% cheaper |
| 🥉 |
Gemini 3.5 Flash-Lite
Google
|
$0.064964 (rounded ~ $0.06) | ↓ 99.9% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.064964 (rounded ~ $0.06) | ↓ 99.9% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.107940 (rounded ~ $0.11) | ↓ 99.8% cheaper |
| #6 |
Kimi K2.5
Moonshot AI
|
$0.131104 (rounded ~ $0.13) | ↓ 99.8% cheaper |
| #7 |
Gemini 3.8 Flash
Google
|
$0.161160 (rounded ~ $0.16) | ↓ 99.8% cheaper |
| #8 |
GPT-5.4 mini
OpenAI
|
$0.161535 (rounded ~ $0.16) | ↓ 99.8% cheaper |
| #9 |
Kimi K2.6
Moonshot AI
|
$0.207206 (rounded ~ $0.21) | ↓ 99.7% cheaper |
| #10 |
Kimi K2.7 Code
Moonshot AI
|
$0.207206 (rounded ~ $0.21) | ↓ 99.7% cheaper |
| #11 |
o4-mini Deep Research
OpenAI
|
$0.214380 (rounded ~ $0.21) | ↓ 99.7% cheaper |
| #12 |
Claude Haiku 4.5
Anthropic
|
$0.214880 (rounded ~ $0.21) | ↓ 99.7% cheaper |
| #13 |
Gemini 3.1 Flash
Google
|
$0.215380 (rounded ~ $0.22) | ↓ 99.7% cheaper |
| #14 |
GPT-5.6 Luna
OpenAI
|
$0.215380 (rounded ~ $0.22) | ↓ 99.7% cheaper |
| #15 |
o4-mini
OpenAI
|
$0.235818 (rounded ~ $0.24) | ↓ 99.6% cheaper |
| #16 |
Gemini 3.6 Flash
Google
|
$0.322320 (rounded ~ $0.32) | ↓ 99.5% cheaper |
| #17 |
Gemini 3.5 Flash
Google
|
$0.323070 (rounded ~ $0.32) | ↓ 99.5% cheaper |
| #18 |
GPT-5.3 Codex Spark
OpenAI
|
$0.378665 (rounded ~ $0.38) | ↓ 99.4% cheaper |
| #19 |
GPT-5.3 Instant
OpenAI
|
$0.378665 (rounded ~ $0.38) | ↓ 99.4% cheaper |
| #20 |
Claude Sonnet 5
Anthropic
|
$0.429760 | ↓ 99.4% cheaper |
| #21 |
Grok 4.3
xAI
|
$0.533450 (rounded ~ $0.53) | ↓ 99.2% cheaper |
| #22 |
GPT-5.4
OpenAI
|
$0.538450 (rounded ~ $0.54) | ↓ 99.2% cheaper |
| #23 |
GPT-5.4 Thinking
OpenAI
|
$0.538450 (rounded ~ $0.54) | ↓ 99.2% cheaper |
| #24 |
Gemini 2.5 Pro
Google
|
$0.538450 (rounded ~ $0.54) | ↓ 99.2% cheaper |
| #25 |
GPT-5.6 Terra
OpenAI
|
$0.538450 (rounded ~ $0.54) | ↓ 99.2% cheaper |
| #26 |
Claude Sonnet 4.6
Anthropic
|
$0.644640 (rounded ~ $0.64) | ↓ 99% cheaper |
| #27 |
Grok 4.6
xAI
|
$0.855520 (rounded ~ $0.86) | ↓ 98.7% cheaper |
| #28 |
Grok 4.5
xAI
|
$0.855520 (rounded ~ $0.86) | ↓ 98.7% cheaper |
| #29 |
Gemini 3.1 Pro
Google
|
$0.858520 (rounded ~ $0.86) | ↓ 98.7% cheaper |
| #30 |
Claude Opus 4.7
Anthropic
|
$1.074400 (rounded ~ $1.07) | ↓ 98.4% cheaper |
| #31 |
Claude Opus 5
Anthropic
|
$1.074400 (rounded ~ $1.07) | ↓ 98.4% cheaper |
| #32 |
Claude Opus 4.8
Anthropic
|
$1.074400 (rounded ~ $1.07) | ↓ 98.4% cheaper |
| #33 |
Claude Opus 4.6
Anthropic
|
$1.074400 (rounded ~ $1.07) | ↓ 98.4% cheaper |
| #34 |
GPT-5.5 Instant
OpenAI
|
$1.076900 (rounded ~ $1.08) | ↓ 98.4% cheaper |
| #35 |
GPT-5.6 Sol
OpenAI
|
$1.076900 (rounded ~ $1.08) | ↓ 98.4% cheaper |
| #36 |
Claude Fable 5.1
Anthropic
|
$2.109950 | ↓ 96.9% cheaper |
| #37 |
Claude Mythos 5.1
Anthropic
|
$2.109950 | ↓ 96.9% cheaper |
| #38 |
o3 Deep Research
OpenAI
|
$2.143800 (rounded ~ $2.14) | ↓ 96.8% cheaper |
| #39 |
GPT-5.5
OpenAI
|
$2.146300 (rounded ~ $2.15) | ↓ 96.8% cheaper |
| #40 |
Claude Fable 5
Anthropic
|
$2.148800 (rounded ~ $2.15) | ↓ 96.8% cheaper |
| #41 |
Claude Mythos 5
Anthropic
|
$2.148800 (rounded ~ $2.15) | ↓ 96.8% cheaper |
| #42 |
o3 Pro
OpenAI
|
$4.287600 (rounded ~ $4.29) | ↓ 93.6% cheaper |
| #43 |
GPT-6 Astra
OpenAI
|
$4.297600 (rounded ~ $4.30) | ↓ 93.6% cheaper |
| #44 |
GPT-5.2 Pro
OpenAI
|
$4.543980 (rounded ~ $4.54) | ↓ 93.2% cheaper |
| #45 |
GPT-5.2 Pro
OpenAI
|
$4.543980 (rounded ~ $4.54) | ↓ 93.2% cheaper |
Mistral Small 3 Mistral AI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Kimi K2.5 Moonshot AI
Gemini 3.8 Flash Google
GPT-5.4 mini OpenAI
Kimi K2.6 Moonshot AI
Kimi K2.7 Code Moonshot AI
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
o4-mini OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 5 Anthropic
Grok 4.3 xAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Gemini 2.5 Pro Google
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Grok 4.6 xAI
Grok 4.5 xAI
Gemini 3.1 Pro Google
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.5 Instant OpenAI
GPT-5.6 Sol OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
o3 Deep Research OpenAI
GPT-5.5 OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
o3 Pro OpenAI
GPT-6 Astra OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Optimizing Storyboard Generation Workflows
For independent mobile app developers creating AI-powered storyboarding tools, balancing visual quality with cost is the primary challenge. nano-banana-pro stands out as a specialized solution for generating high-fidelity storyboard frames. Unlike general-purpose models, it is optimized for consistent character generation, spatial composition, and precise visual adherence to prompt instructions—critical factors when building a professional-grade video planning tool.
When scaling to 500 image frames, developers need a model that minimizes the need for iterative prompting and manual post-processing. nano-banana-pro’s architecture excels in maintaining consistency across multiple shots, which helps reduce the total volume of generations required per project. By leveraging its ability to ingest reference images and maintain strict style adherence, you can shorten the feedback loop between the user’s intent and the final visual asset. This approach is particularly effective for indie apps where every API call impacts the budget.
Choosing a dedicated image-generation model over multi-modal alternatives allows for more predictable output latency and tighter integration within an app’s creative workflow. For developers managing a catalog of user-generated storyboards, the model’s capacity for complex composition and text rendering—essential for on-frame scene notations—simplifies the frontend requirements. Focus on optimizing your prompt structure to maximize the model’s native capabilities, ensuring that each of your 500 frames hits the mark on the first attempt, thereby maximizing your return on every generation request.