New Model Added: Gemini 3 Flash Preview

Date: 2025-12-17 / (1404-09-26)

Summary

AvalAI introduces Google's Gemini 3 Flash Preview (gemini-3-flash-preview), the latest efficient model combining frontier intelligence with superior search and grounding capabilities. This model offers pro-grade reasoning performance at a fraction of the cost, making it ideal for everyday use with a 1M token context window.

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Details

Google - Gemini 3 Flash Preview

Gemini 3 Flash Preview (gemini-3-flash-preview) is Google's most intelligent model built for speed, combining frontier intelligence with superior search and grounding. Released December 2025, this model offers significant improvements over Gemini 2.5 Flash and matches the performance of other frontier models in key benchmarks. Documentation

Key Capabilities:

• 1M Token Context Window: Handle extensive conversations, documents, and code repositories
• Multimodal Input: Process text, images, video, audio, and PDF files
• Thinking/Reasoning: Built-in reasoning capabilities for complex problem-solving
• Function Calling: Full support for tool use and agentic workflows
• Structured Outputs: Generate structured JSON responses
• Search Grounding: Superior search and grounding with real-world knowledge
• Code Execution: Run code directly within the model
• URL Context: Process and understand web page content
• Context Caching: Efficient token caching for repeated contexts

Feature	Details
Model Code	gemini-3-flash-preview
Context Window	Up to 1,048,576 tokens (1M)
Max Output Tokens	65,536 tokens
Inputs	Text, Image, Video, Audio, PDF
Output	Text
Knowledge Cutoff	January 2025
Supported Endpoints	v1/chat/completions, v1/completions
Strengths	Speed, efficiency, pro-grade reasoning, search grounding
Best for	Everyday tasks, video analysis, data extraction, visual Q&A, quick workflows

Pricing:

Token Type	Price per 1M Tokens
Input	$0.50
Cached Input	$0.25
Output	$3.00
Audio Input	$1.50
Audio Cached Input	$0.50
Audio Output	$1.50

Benchmark Performance:

• Humanity's Last Exam: 33.7% (without tool use) - matches GPT-5.2 (34.5%)
• MMMU-Pro: 81.2% - outperforms all competitors including GPT-5.2 (79.5%)
• Significant improvements over Gemini 2.5 Flash in all major benchmarks
• 3x faster than Gemini 2.5 Pro while matching performance
• Uses 30% fewer tokens on average for thinking tasks compared to 2.5 Pro

Supported Capabilities:

Capability	Status
Batch API	✓ Supported
Caching	✓ Supported
Code Execution	✓ Supported
File Search	✓ Supported
Function Calling	✓ Supported
Search Grounding	✓ Supported
Structured Outputs	✓ Supported
Thinking	✓ Supported
URL Context	✓ Supported
Audio Generation	✗ Not supported
Image Generation	✗ Not supported
Live API	✗ Not supported
Grounding with Google Maps	✗ Not supported

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API Request/Response Examples

Basic Chat Completion

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "gemini-3-flash-preview",
    "messages": [
      {
        "role": "user",
        "content": "Explain quantum computing in simple terms"
      }
    ]
  }'

Example Response:

{
  "id": "chatcmpl-gemini3flash-abc123",
  "created": 1765933200,
  "model": "gemini-3-flash-preview",
  "object": "chat.completion",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "Quantum computing is a new type of computing that uses the principles of quantum mechanics to process information...",
        "role": "assistant"
      }
    }
  ],
  "usage": {
    "completion_tokens": 245,
    "prompt_tokens": 12,
    "total_tokens": 257
  },
  "estimated_cost": {
    "unit": "0.0007410000",
    "irt": 97.39,
    "exchange_rate": 131400
  }
}

With Thinking/Reasoning

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "gemini-3-flash-preview",
    "messages": [
      {
        "role": "user",
        "content": "Solve this step by step: If a train travels 120 km in 2 hours, then stops for 30 minutes, then travels another 90 km in 1.5 hours, what is the average speed for the entire journey?"
      }
    ],
    "max_tokens": 2048
  }'

Multimodal Input (Image Analysis)

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "gemini-3-flash-preview",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Analyze this image and describe what you see in detail"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://example.com/image.jpg"
            }
          }
        ]
      }
    ]
  }'

Function Calling

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "gemini-3-flash-preview",
    "messages": [
      {
        "role": "user",
        "content": "What is the weather like in Tokyo?"
      }
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
          "description": "Get current weather information for a location",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "City name"
              },
              "unit": {
                "type": "string",
                "enum": ["celsius", "fahrenheit"],
                "description": "Temperature unit"
              }
            },
            "required": ["location"]
          }
        }
      }
    ],
    "tool_choice": "auto"
  }'

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SDK Usage Examples

Bash

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "gemini-3-flash-preview",
    "messages": [
      {
        "role": "user",
        "content": "Write a Python function to calculate the Fibonacci sequence"
      }
    ]
  }'

Python

from openai import OpenAI

client = OpenAI(api_key="your-avalai-api-key", base_url="https://api.avalai.ir/v1")

response = client.chat.completions.create(
    model="gemini-3-flash-preview",
    messages=[
        {
            "role": "user",
            "content": "Write a Python function to calculate the Fibonacci sequence",
        }
    ],
)

print(response.choices[0].message.content)

JavaScript

import { OpenAI } from "openai";

const client = new OpenAI({
  apiKey: process.env.AVALAI_API_KEY,
  baseURL: "https://api.avalai.ir/v1",
});

const response = await client.chat.completions.create({
  model: "gemini-3-flash-preview",
  messages: [
    {
      role: "user",
      content: "Write a Python function to calculate the Fibonacci sequence",
    },
  ],
});

console.log(response.choices[0].message.content);

With Multimodal Input

Bash

curl https://api.avalai.ir/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AVALAI_API_KEY" \
  -d '{
    "model": "gemini-3-flash-preview",
    "messages": [
      {
        "role": "user",
        "content": [
          {"type": "text", "text": "What is in this image?"},
          {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
        ]
      }
    ]
  }'

Python

from openai import OpenAI

client = OpenAI(api_key="your-avalai-api-key", base_url="https://api.avalai.ir/v1")

response = client.chat.completions.create(
    model="gemini-3-flash-preview",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://example.com/image.jpg"},
                },
            ],
        }
    ],
)

print(response.choices[0].message.content)

JavaScript

import { OpenAI } from "openai";

const client = new OpenAI({
  apiKey: process.env.AVALAI_API_KEY,
  baseURL: "https://api.avalai.ir/v1",
});

const response = await client.chat.completions.create({
  model: "gemini-3-flash-preview",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What is in this image?" },
        { type: "image_url", image_url: { url: "https://example.com/image.jpg" } },
      ],
    },
  ],
});

console.log(response.choices[0].message.content);

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Related Links

• Google Models Documentation
• Text Generation Guide
• Function Calling Guide
• Vision Guide
• Pricing