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Nano Banana (Gemini 2.5 Flash Image)
- Vision-Language
Nano Banana (Gemini 2.5 Flash Image) is Google’s high-speed visual generation and editing model designed for low-latency, high‑volume image workflows with strong character and style consistency.
About the model
What is Nano Banana (Gemini 2.5 Flash Image)?
Nano Banana (officially Gemini 2.5 Flash Image) is a Google DeepMind model for fast, multimodal text-and-image generation and image editing. It is mainly used for text-to-image creation, conversational image edits, and multi-image fusion scenarios that need low latency and high throughput. It also powers image-centric experiences in the Gemini app and via the Gemini API, Google AI Studio, and Vertex AI for developers and enterprises. It belongs to Google’s Gemini 2.5 family of models and sits alongside successors like Nano Banana Pro (Gemini 3 Pro Image) and Nano Banana 2 (Gemini 3.1 Flash Image).
Model capabilities
5 Core Capabilities
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Text-To-Image
Generates detailed, high-quality images directly from natural language prompts, leveraging Gemini’s world knowledge for semantically accurate visuals.
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Image Editing
Performs precise, instruction-based edits on existing images, including targeted region changes, style adjustments, and iterative refinement for production workflows.
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Multi-Image Fusion
Combines content and styles from multiple reference images into a single coherent output while maintaining visual consistency and scene structure.
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Character Consistency
Maintains consistent characters, identities, and visual attributes across multiple generated or edited images for storytelling and branding use cases.
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Multimodal Understanding
Interprets prompts mixing text and images, enabling context-aware generation and edits aligned with both visual references and textual instructions.
Use cases
6 Most Valuable Use Cases
- Marketing Visual Creation
- E-commerce Product Imagery
- Social Media Content
- Photo Retouching Workflows
- Creative Design Prototyping
- A/B Testing Ad Creatives
Transparent pricing
Cost Comparison
LLM API offers the lowest image costs and latency for Nano Banana–class vision models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~180ms | ~150 img/min | 99.99% | $0.0004/img | $0.0004/img | ~20 images |
| Global | ~350ms | ~90 img/min | 99.9% | ~$0.0010/img | ~$0.0010/img | ~16 images | |
| OpenAI | Global | ~400ms | ~80 img/min | 99.9% | ~$0.0012/img | ~$0.0012/img | ~10 images |
| Anthropic | US East | ~420ms | ~75 img/min | 99.9% | ~$0.0011/img | ~$0.0011/img | ~12 images |
Performance benchmarks
Technical Specifications
| Metric | Nano Banana (Gemini 2.5 Flash Image) | OpenAI o3-mini + Vision | Anthropic Claude 3.7 Sonnet Vision |
|---|---|---|---|
| Latency per Image | ~600ms | ~700ms | ~750ms |
| Throughput | ~120 img/s | ~100 img/s | ~90 img/s |
| Max Resolution | 4K | 4K | 4K |
| Price per Image | ~$0.002 | ~$0.003 | ~$0.0035 |
| Supported Formats | JPG, PNG, WEBP | JPG, PNG, WEBP | JPG, PNG, WEBP |
| Max Output Tokens | 4K | 8K | 8K |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 1.8B
- Prompt tokens processed (30 days)
- 12.4M
- Images analyzed (30 days)
- 7.9M
- API requests served (30 days)
- 99.8%
- Avg uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent AI Routing
Automatically route each request to the optimal model across providers based on latency, price, and quality—no client changes required.
One endpoint, any model -
Cost-Aware Optimization
Control spend with policy-driven model selection, rate limits, and real-time cost visibility so teams can experiment freely without budget surprises.
More usage, less spend -
Resilient Fallback Flows
Define provider and model failover chains so requests survive outages, quota limits, and timeouts—with zero logic baked into your app.
Stay online, automatically -
Full-Stack Observability
Trace every call across providers with unified logs, metrics, and latency breakdowns to debug issues and tune performance from one dashboard.
See every token -
Task-Level Orchestration
Express complex workflows as tasks—retrieval, tools, and reasoning—in a single API that abstracts provider quirks and keeps behavior consistent.
Orchestrate, don’t glue-code -
High-Throughput Batch Jobs
Run large-scale generations, evaluations, or data processing as managed batch jobs with automatic chunking, retries, and cost controls.
Scale to millions of calls
Decision guide
When to Use — When NOT to Use
Use it if...
- You need fast, low-cost multimodal inference combining images and short text prompts.
- You need to quickly classify, tag, and filter large volumes of user images.
- Your use case involves generating captions, alt-text, or summaries directly from pictures.
- You need lightweight visual understanding for mobile or web apps with budget constraints.
- Your use case involves prototyping image-based features without requiring top-tier reasoning quality.
- You need to detect simple visual patterns, objects, or layouts rather than nuanced semantics.
Avoid if...
- You need state-of-the-art, deeply accurate visual reasoning on complex or ambiguous images.
- Your workload requires long-context reasoning over many related images and extensive text.
- You need highly reliable domain-expert analysis of medical, legal, or safety-critical imagery.
- Your workload requires consistent, production-critical decisions where small visual errors are unacceptable.
- You need advanced creative image editing or generation beyond simple interpretation and description tasks.
- Your workload requires strict on-prem or fully offline deployment with no external dependencies.
FAQ
Frequently Asked Questions
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What is Nano Banana (Gemini 2.5 Flash Image)?
Nano Banana (Gemini 2.5 Flash Image) is a Google multimodal model optimized for fast, low-cost image-plus-text understanding and generation via LLM.API.
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What modalities does Nano Banana (Gemini 2.5 Flash Image) support?
Nano Banana (Gemini 2.5 Flash Image) supports image inputs combined with text prompts and returns text outputs, making it suitable for many visual understanding workflows.
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How do I access Nano Banana (Gemini 2.5 Flash Image) through LLM.API?
Call the unified LLM.API completions or chat endpoint with the model identifier for Nano Banana (Gemini 2.5 Flash Image) and your LLM.API key.
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What is Nano Banana (Gemini 2.5 Flash Image) best suited for?
It is best for fast, inexpensive visual tasks like image captioning, basic OCR, UI understanding, and lightweight vision-language reasoning where latency matters.
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What is the context window of Nano Banana (Gemini 2.5 Flash Image)?
Nano Banana (Gemini 2.5 Flash Image) supports a context window of up to 32,000 tokens for text, including prompt and response.
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How fast is Nano Banana (Gemini 2.5 Flash Image) on LLM.API?
Nano Banana (Gemini 2.5 Flash Image) is designed for low latency, typically returning responses significantly faster than larger Gemini vision models at similar workloads.
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How is Nano Banana (Gemini 2.5 Flash Image) priced on LLM.API?
Pricing is per-token for text and per-image for vision inputs, with Nano Banana positioned as a budget-friendly Gemini vision option on LLM.API.
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How does Nano Banana (Gemini 2.5 Flash Image) compare to larger Gemini 2.5 vision models?
Compared to larger Gemini 2.5 vision models, Nano Banana trades some reasoning depth and accuracy for much lower cost and faster responses.
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Does Nano Banana (Gemini 2.5 Flash Image) support streaming responses on LLM.API?
Yes, you can enable streaming on LLM.API to receive Nano Banana (Gemini 2.5 Flash Image) tokens incrementally for lower perceived latency.
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What are the main limitations of Nano Banana (Gemini 2.5 Flash Image)?
It may struggle with complex multi-step reasoning, very fine-grained visual details, and tasks requiring long, deeply contextualized analysis.