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Gemini 3.8 Flash

gemini-3.8-flash

Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.

Context
1M tokens
Endpoint

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Pricing

Input$0.75 / 1M
Output$3.75 / 1M
Cache Write (5m)$0.75 / 1M
Cache Write (1h)$0.75 / 1M
Cache Read$0.75 / 1M
Web Search$0 / 1M

Quick Start

Select an endpoint and copy a working example for this model.

Endpoint
python
from openai import OpenAI client = OpenAI(    api_key="YOUR_API_KEY",    base_url="https://api.apertis.ai/v1") response = client.chat.completions.create(    model="gemini-3.8-flash",    messages=[        {"role": "user", "content": "Hello!"}    ],    max_tokens=1024,    temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(#     model="gemini-3.8-flash",#     messages=[{"role": "user", "content": "Hello!"}],#     extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )

Supported Parameters

API docs
Common7 params
modelmessagesmax_tokenstemperaturetop_pstreamtools
Extended4 params
reasoning_effortstream_optionsthinkingextra_body

Cursor IDE Model IDs

Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.

gemini-3.8-flash

Compare with Other Models

See how this model compares to others from the same provider.

Gemini 4 Argon

Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.

Context
1M
Input
$2.00/M
Output
$10.00/M

Gemini 3.1 Flash Lite Image (Google Vertex AI)

Gemini 3.1 Flash Lite Image served from Google Vertex AI. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size.

Context
N/A
Input
— (Not priced per input token)
Output
— (Not priced per output token)

Gemini 3.1 Flash Lite Image (Google AI Studio)

Gemini 3.1 Flash Lite Image served from Google AI Studio. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size.

Context
N/A
Input
— (Not priced per input token)
Output
— (Not priced per output token)

Gemini 3.1 Flash Image (Google Vertex AI)

Gemini 3.1 Flash Image served from Google Vertex AI. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 2K. A request for a larger size, such as 4K or 4096x4096, returns a 2K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size, for example 4096x4096->2048x2048.

Context
N/A
Input
— (Not priced per input token)
Output
— (Not priced per output token)