Google: Gemini 3.5 Flash

Gemini 3.5 Flash is Google’s high-efficiency multimodal model, delivering near-Pro-level reasoning and coding capabilities with Flash-class speed and cost efficiency. It is purpose-built for advanced coding workflows and parallel agentic execution, while supporting a wide range of input modalities including text, images, video, audio, and PDFs.

The model defaults to a medium reasoning mode to balance latency, quality, and cost, while also offering configurable thinking levels — minimal, low, medium, and high — for more precise performance and efficiency tuning across different workloads.

Why It Looks Great

• text+image+file+audio+video->text architecture for Text, Image, Video, file, Audio to Text workloads
• 1048576 context window for long prompts, document analysis, and multi-turn workflows
• Competitive pricing at $1.5/$9 per million tokens
• Vision input support for image understanding and multimodal tasks
• Function calling and tool-use support for agentic application workflows
• Structured output support for JSON responses and schema-constrained generation

Key Features

• Context Window: 1048576 tokens
• Max Input: 983040 tokens
• Max Output: 65536 tokens
• Vision: Supported
• Function Calling: Supported
• Structured Outputs: Supported
• Image Generation: Not listed
• Audio Input: Supported
• Supported Parameters: include_reasoning, max_tokens, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p

Specifications

How to Use

1. Write your prompt - describe the task, provide context, and specify the desired output format.
2. Submit - the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: google/gemini-3.5-flash

API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="google/gemini-3.5-flash",
    messages=[{"role": "user", "content": "Hello!"}]
)

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

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "google/gemini-3.5-flash",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'