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For the Google Gemini API, change three controls first: image_size for output detail and image cost, aspect_ratio for the destination layout, and generation_config.thinking_level for prompt-complexity handling and latency. Start at 1K with the correct ratio and medium thinking, then raise resolution or thinking only when the deliverable justifies the extra cost or wait time.
The three settings that matter most
Google’s stable model identifier is gemini-nano-banana-2.1. The model reference, updated October 6, 2026, documents three output-size tiers, configurable thinking, and aspect-ratio controls. See the official Nano Banana 2.1 model reference for the current schema and limits.
| Setting | What it changes | Best starting choice | Raise or lower it when |
|---|---|---|---|
image_size |
Requested output resolution and the image-output component of the charge | 1K for drafts and ordinary web assets | Use 2K or 4K only when display size, print preparation, or crop headroom needs it |
aspect_ratio |
Canvas shape and how well the image fits its eventual placement | Match the target placement before generating | Change it when the image will be used in a portrait, landscape, square, or panoramic slot |
generation_config.thinking_level |
How much reasoning the model applies to the prompt, balancing quality and latency | medium, Google’s default |
Use minimal for rapid simple iterations or high for many constraints and composition-sensitive work |
These controls are documented API options, not guaranteed performance switches. Google does not publish a universal number of seconds saved or a quality score gained at each setting, so treat the recommendations below as a practical workflow rather than a benchmark.
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The API accepts 1K, 2K, and 4K; 1K is the documented default. A larger request gives you more pixels for a large display or a crop, but it also increases the image-output charge. A small image embedded in a webpage usually does not become visibly better merely because it was generated at 4K.
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| Requested output | Standard image-output charge | Batch image-output charge |
|---|---|---|
| 1K | $0.0336 per image | $0.0168 per image |
| 2K | $0.0504 per image | $0.0252 per image |
| 4K | $0.113 per image | $0.0567 per image |
These figures are the image-output component listed on Google’s 2026 pricing page, checked October 13, 2026; they are not necessarily the complete request price. Input tokens, text output, thinking tokens where applicable, and optional grounding can be billed separately. Check the current Gemini Developer API pricing table before making a budget or committing to a production volume.
A practical resolution workflow
- Draft at 1K. Use it to judge subject placement, lighting, style, and whether the prompt is understood.
- Fix the prompt and composition before enlarging. More pixels will not repair an incorrect subject, missing object, or bad layout.
- Regenerate the selected concept at 2K. This is appropriate for many presentation graphics, social crops, and larger on-screen placements.
- Reserve 4K for a documented need. Choose it when the downstream file will be displayed very large, prepared for print, or cropped aggressively. Do not label 4K universally “better”; its value depends on the final dimensions and workflow.
Batch requests have the lower listed image-output charges but are intended for non-interactive jobs. Google says Batch provides higher rate limits with turnaround of up to 24 hours, so it is unsuitable when a user is waiting for an immediate preview.
Set thinking level according to prompt complexity
Nano Banana 2.1 exposes minimal, medium, and high thinking levels, with medium as the default. Google describes the control as a way to balance quality and latency; thinking tokens for thinking models are part of token billing. The image-generation documentation explains the setting alongside image parameters.
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| Thinking level | Use it for | Trade-off |
|---|---|---|
minimal |
Simple prompts, quick concept exploration, and large batches where response time matters | Less reasoning budget for instructions with many relationships or constraints |
medium |
Normal generation and editing when you want a balanced default | Neither the shortest possible wait nor the maximum reasoning budget |
high |
Layered instructions, several required elements, exact relationships, or composition-sensitive scenes | Potentially higher latency and additional thinking-token usage |
Use high thinking because the prompt is difficult, not because every image should receive the maximum setting. A short request such as “make a blue geometric icon on a white background” is a sensible minimal-thinking test. A scene requiring a specific camera angle, five objects in fixed positions, readable labels, and continuity with reference images is a better candidate for high. The official material does not promise that high always improves an image or that minimal is faster by a fixed amount; measure your own workflow if latency is a contractual requirement.
Match aspect ratio to the destination canvas
aspect_ratio is a composition decision as much as a technical one. Choose the shape before generation so the model can arrange the subject inside the real canvas rather than forcing you to crop away important content later. Portrait ratios suit stories and tall social placements; landscape ratios suit slides, headers, and banners; square fits square feeds and icon-style layouts. Use a panoramic ratio only when the destination actually has a panoramic slot.
When there is no input image supplying a size, the documented default is square. If you provide an input image, the output can follow that image’s size. Google specifically notes that tiling artifacts were fixed for the very wide 1:4, 4:1, 1:8, and 8:1 ratios at 2K and 4K. That addresses a model behavior, not a promise that every subject will be positioned perfectly at the edge of a panorama.
Illustrative API configuration
The following shows where the documented controls belong. Include the model, authentication, prompt, and response handling required by your client library; those details vary by SDK.
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{"response_format":{"image_size":"2K","aspect_ratio":"16:9"},"generation_config":{"thinking_level":"medium"}}For a rapid draft, change the size to 1K and thinking to minimal. For a complex final asset, keep the destination ratio and consider high thinking before moving to 4K. Confirm the exact request shape in Google’s current SDK documentation because client libraries can evolve independently of the model reference.
Use reference images deliberately
Nano Banana 2.1 supports up to 14 reference images. Google describes consistency for up to four characters and high-fidelity representation for up to ten objects. Those limits are useful planning boundaries, not a guarantee that every reference will be preserved exactly.
Assign each reference a job
- Use a character reference for identity, clothing, or facial features.
- Use an object reference for product shape, markings, or material.
- Use a style reference for color treatment, lighting, or illustration language.
- Use a pose or layout reference when the scene needs a particular arrangement.
Tell the prompt what each image contributes instead of attaching a large, unlabelled collection. For example, identify one image as the character’s face, another as the jacket, and a third as the lighting reference. Remove redundant references when they conflict; additional images do not automatically improve fidelity.
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Handle text and grounding as separate concerns
Text inside generated images
Specify the exact copy, language, hierarchy, alignment, and location when an image contains lettering. Google promotes improved text rendering, but it does not guarantee typo-free output. For important packaging, signage, or advertising, inspect every character at the intended display size. Google’s guidance says it can work better to generate the text first and then request an image that uses that text. Plan a correction pass rather than assuming the first render is publication-ready. Generated images include SynthID watermarking.
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Google documents Web Search and Image Search grounding for Nano Banana 2.1. Grounding can supply current information as visual context, but it changes both the request design and the bill. The pricing documentation notes that a request can produce one or more Google Search queries and that grounding is charged separately after applicable included requests. Google also says web-search grounding does not currently support using real-world images of people found through web search. See the grounding section of the image-generation guide and verify the pricing page for current terms.
Use grounding when the image genuinely needs current, externally verifiable context. For a fictional scene, a supplied reference image or a precise text description is usually a simpler and more predictable input.
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A repeatable quality, speed, and cost workflow
- Write the deliverable first. Record the placement, final pixel dimensions, crop allowance, and whether the image is a draft or a final.
- Select the canvas ratio. Set
aspect_ratioto the destination shape before writing detailed composition instructions. - Start with economical settings. Use 1K and medium thinking for a normal first pass. Use minimal thinking when the prompt is deliberately simple and speed is the priority.
- Make the prompt testable. State the subject, viewpoint, number of objects, relationships, lighting, palette, and any exact text. Label the role of every reference image.
- Inspect the draft. Check composition, hands and faces, object identity, lettering, and crop safety at the size people will actually see.
- Change one variable at a time. If the composition is wrong, revise the prompt or references. If the composition is right but too small, raise image size. If a complex instruction is being missed, raise thinking level.
- Render the approved version at the needed size. Use 2K or 4K only after the content is correct.
- Review usage and billing data. Include image output, text, thinking-token, and grounding charges in the total rather than multiplying the image-output figure alone.
API controls and Gemini Apps are different interfaces
The named parameters in this guide belong to the developer API. In Gemini Apps, you select an image-generation tool or model in the chat interface; you should not expect an API settings panel containing image_size or generation_config.thinking_level.
Google’s Gemini Apps help page says downloads are 2K with a Google AI plan and 1K without one, and that paid subscribers can redo images using Nano Banana Pro. Plan names, availability, regional eligibility, and interface labels can change, so confirm the current details in Google’s Gemini Apps image-generation help page. Google’s overview is also available at the Gemini image-generation page.
How Nano Banana 2.1 fits Google’s model lineup
Google describes Nano Banana 2.1 as an update to Nano Banana 2 and Gemini 3.1 Flash Image. Its October 2026 model reference says it retains Flash-level speed and cost efficiency while improving visual quality, prompt adherence, multi-turn character consistency, and text rendering. These are Google’s product descriptions, not independent side-by-side test results.
| Model | Google’s positioning | Important qualification |
|---|---|---|
| Nano Banana 2.1 | High-efficiency workhorse for general image generation and editing | Use the documented size, ratio, and thinking controls for the required deliverable |
| Nano Banana 2 Lite | Fastest and cheapest option in Google’s description | Google says it is not optimized for multiple reference inputs or sequential editing |
| Nano Banana Pro | Premium option for complex visual tasks | This is vendor positioning, not a performance measurement in this article |
Choose among them based on the workflow you need—references, editing continuity, complexity, and budget—not on an assumption that a product label guarantees a particular visual result.
Troubleshoot the setting that is actually causing the problem
| Symptom | Likely cause | Action |
|---|---|---|
| The image is sharp enough but the crop cuts off the subject | Canvas ratio does not match the placement | Set the destination aspect_ratio before regenerating; do not rely on a late crop |
| The draft is expensive before the composition is approved | Using 2K or 4K for every iteration | Return to 1K for exploration and upscale only the selected concept |
| A prompt with many constraints misses relationships | Insufficient reasoning budget or ambiguous instructions | Use medium or high thinking and state object relationships explicitly |
| Simple batch work feels slow | Thinking level is higher than the prompt needs | Test minimal thinking and consider Batch for jobs that can wait up to 24 hours |
| Text contains a typo or wrong character | Generated lettering is not deterministic typography | Specify exact copy and placement, inspect the result, and perform a text-focused regeneration or post-edit |
| Reference subjects drift between edits | Too many conflicting references or unclear roles | Keep only relevant images, label each role, and stay within Google’s stated character and object guidance |
| The total invoice exceeds the image-output estimate | Input, text, thinking, or grounding charges were omitted | Read token usage and grounding charges in addition to the per-image output tier |
Bottom line
Set the canvas to the real destination, begin at 1K, and use medium thinking unless the prompt is clearly simple or unusually complex. Move to 2K or 4K only for a final asset that needs the extra pixels, and use high thinking for instruction-heavy compositions rather than as a blanket quality switch. Keep API configuration separate from Gemini Apps, and budget the complete request—not just the listed image-output amount.
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