Gemini 3 Flash Preview

Chat Completions

gemini-3-flash-preview

Google|Created Dec 17, 2025|1.0M context

Chat Completions

Gemini 3 Flash Preview is a high-speed, cost-effective reasoning model built for agent-driven workflows, multi-turn conversation, and coding support. Offering near-Pro level performance in both reasoning and tool use, it stands out by delivering significantly lower latency than larger Gemini versions—making it ideal for interactive development, long-running agent loops, and collaborative programming. Compared to Gemini 2.5 Flash, it features notable improvements in reasoning ability, multimodal comprehension, and overall reliability. The model supports a 1M token context window and handles multimodal inputs—text, images, audio, video, and PDFs—with text-based output. Features like configurable reasoning levels, structured outputs, tool integration, and automatic context caching make it a strong choice for users seeking powerful agentic capabilities without the high cost or lag of more extensive models.

Overview Specifications Activity Performance Uptime Examples

Pricing-50%

Pay-as-you-go rates for this model. More details can be found here.

Input Tokens (1M)

$0.25

Cached Input Tokens (1M)

$0.02

Output Tokens (1M)

$1.50

Capabilities

Input Modalities

TextImageAudioFile

Output Modalities

Text

Supported Parameters

Available parameters for API requests

Max Completion TokensReasoning EffortResponse FormatStopTemperatureTool ChoiceToolsTop PWeb Search Options

Usage Analytics

Token usage of this model on our platform

Throughput

Time-To-First-Token (TTFT)

Code Example

Example code for using this model through our API with Python (OpenAI SDK) or cURL. Replace placeholders with your API key and model ID.

Basic request example. Ensure API key permissions. For more details, see our documentation.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.naga.ac/v1",
    api_key="YOUR_API_KEY",
)

resp = client.chat.completions.create(
    model="gemini-3-flash-preview",
    messages=[
        {"role": "user", "content": "What's 2+2?"}
    ],
    temperature=0.2,
)
print(resp.choices[0].message.content)