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gemini-3.1-flash-lite-image API pricing & access

gemini-3.1-flash-lite-image API pricing: $0.25 per 1M tokens. gemini-3.1-flash-lite-image. Supports text, image, video, file input and text, image outpu…

gemini-3.1-flash-lite-image API access

gemini-3.1-flash-lite-image. Supports text, image, video, file input and text, image output. Listed specifications follow public vendor documentation. Available features and limits depend on the selected serving channel.

Compare supported API endpoints and capabilities, then access gemini-3.1-flash-lite-image through the unified API gateway.

Category
image
Context length
65536 tokens
Maximum output
4096 tokens
Supported APIs
Image Generation API, Gemini API, OpenAI Chat Completions API
API endpoints
POST /v1/images/generations, POST /v1beta/models/gemini-3.1-flash-lite-image:generateContent, POST /v1/chat/completions
Input modalities
text, image, video, file
Output modalities
text, image
Capabilities
reasoning, vision

gemini-3.1-flash-lite-image API pricing

Base input price
$0.25 per 1M tokens
Base output price
$30 per 1M tokens

Base prices are shown in USD before group-specific adjustments. Open the live pricing page for current access-group prices.

About gemini-3.1-flash-lite-image

gemini-3.1-flash-lite-image is Google's Nano Banana 2 Lite image-generation and editing model. It can return interleaved text and images and is positioned for lightweight interactive visual workflows such as color swaps, stickers and background changes.

This model supports 1K output rather than 2K or 4K, and search grounding is not supported. Check platform image controls, output structure and measured latency instead of borrowing another Nano Banana model's specifications.

Official reference, checked 2026-10-14.

Use cases and prompting

  1. Start with a 1K target and specify one local change.
  2. Identify subject edges and details that must survive a background adjustment.
  3. Read the returned image data and inspect size; avoid unsupported 2K or 4K requests.
  4. Compare the same test cases with gemini-nano-banana-2.1; check request and response formats in the API documentation.

How to call gemini-3.1-flash-lite-image

gemini-3.1-flash-lite-image is served through tbuy's unified gateway. Existing OpenAI or Anthropic SDK code keeps working: point base_url at https://modelsok.com and use your tbuy API key.

curl (OpenAI-compatible)

curl https://modelsok.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "gemini-3.1-flash-lite-image", "messages": [{"role": "user", "content": "Hello"}]}'

Python (openai SDK)

from openai import OpenAI

client = OpenAI(base_url="https://modelsok.com/v1", api_key="YOUR_API_KEY")
response = client.chat.completions.create(
    model="gemini-3.1-flash-lite-image",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

Frequently asked questions about gemini-3.1-flash-lite-image

What is gemini-3.1-flash-lite-image?

gemini-3.1-flash-lite-image. Supports text, image, video, file input and text, image output. Listed specifications follow public vendor documentation. Available features and limits depend on the selected serving channel.

How do I call gemini-3.1-flash-lite-image?

Create an API key with access to gemini-3.1-flash-lite-image, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.

How is gemini-3.1-flash-lite-image priced?

Pricing depends on the selected provider group and the model billing unit. Refer to the current input, output, request, or media prices in the pricing section.

What is the context window of gemini-3.1-flash-lite-image?

The model catalog lists a context window of 65536 tokens. Check the selected endpoint for request limits.

What is the maximum output of gemini-3.1-flash-lite-image?

The model catalog lists a maximum output of 4096 tokens. Your request settings may set a lower limit.

How should I evaluate gemini-3.1-flash-lite-image for my project?

Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.

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