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gemini-3.8-flash API pricing & access

gemini-3.8-flash API pricing: $0.5 per 1M tokens. Google’s latest Flash model for complex coding, long-running agents, and multimodal unders…

gemini-3.8-flash API access

Google’s latest Flash model for complex coding, long-running agents, and multimodal understanding. Available features and limits depend on the selected channel.

Compare supported API endpoints and capabilities, then access gemini-3.8-flash through the unified API gateway.

Category: text
Context length: 1048576 tokens
Maximum output: 65536 tokens
Supported APIs: Gemini API, OpenAI Chat Completions API, Anthropic Messages API
API endpoints: POST /v1beta/models/gemini-3.8-flash:generateContent, POST /v1/chat/completions, POST /v1/messages
Input modalities: text, image, audio, video, file
Output modalities: text
Capabilities: structured output, function calling, vision, caching, reasoning

gemini-3.8-flash API pricing

Base input price: $0.5 per 1M tokens
Base output price: $3 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-3.8-flash

gemini-3.8-flash is a current stable Flash model for text, image, video, audio and PDF input with text output. Google positions it for longer software-engineering, agent and enterprise workflows.

Thinking supports low, medium and high; minimal returns an error. Audio and video input understanding is distinct from media generation and Live sessions. Validate tools, search and caching on the selected platform route.

Official reference, checked 2026-10-14.

Use cases and prompting

Define the goal, relevant code and final tests for multi-step coding.
Ask for time-position evidence in video or audio analysis and spot-check key segments.
Start with low and compare completed-task accuracy before testing medium or high.
Compare the same test cases with gemini-3.6-flash; check request and response formats in the API documentation.

How to call gemini-3.8-flash

gemini-3.8-flash 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-3.8-flash", "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-3.8-flash",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

Python (anthropic SDK)

from anthropic import Anthropic

client = Anthropic(base_url="https://modelsok.com", api_key="YOUR_API_KEY")
message = client.messages.create(
    model="gemini-3.8-flash",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
)
print(message.content[0].text)

Frequently asked questions about gemini-3.8-flash

What is gemini-3.8-flash?

Google’s latest Flash model for complex coding, long-running agents, and multimodal understanding. Available features and limits depend on the selected channel.

How do I call gemini-3.8-flash?

Create an API key with access to gemini-3.8-flash, 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-3.8-flash 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-3.8-flash?

The model catalog lists a context window of 1048576 tokens. Check the selected endpoint for request limits.

What is the maximum output of gemini-3.8-flash?

The model catalog lists a maximum output of 65536 tokens. Your request settings may set a lower limit.

How should I evaluate gemini-3.8-flash 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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