Beanbag Big Model 1.6 - Multimodal Deep Thinking Big Model from the Beanbag Team at WordPop

What is the Beanbag Mega Model 1.6

Doubao Big Model 1.6 (Doubao-Seed-1.6) is a multimodal deep thinking big model launched by ByteDance. The model supports a variety of input forms such as text, images, video, etc., and can generate high-quality text output. The model has a 256k long context window, the maximum input length can reach 224k tokens, the output length supports up to 16k tokens, and the reasoning ability is strong. The model provides three thinking modes: auto, thinking and non-thinking, which supports flexible adaptation to different tasks. In authoritative evaluations, Beanbag 1.6 has excellent reasoning and math capabilities, and is widely used in content creation, intelligent dialog, code generation, educational tutoring, and multimodal content generation, providing powerful AI productivity tools for enterprises and developers.

Three versions of the Beanbag Big Model 1.6 model

• doubao-seed-1.6: All-round comprehensive model, the model supports 256K contexts, with the ability of deep thinking, multimodal understanding and graphical interface operation. It supports users to choose whether to turn on the deep thinking mode according to their needs.
• doubao-seed-1.6-thinking: Deep Thinking Intensive Edition, which further enhances basic skills in code writing, mathematical computation, and logical reasoning for scenarios that require deep analysis and complex reasoning.
• doubao-seed-1.6-flash: Extremely responsive version with deep thinking and multimodal understanding, supporting 256K contexts and very low latency (TOPT only 10ms), suitable for scenarios requiring very high response speed, such as real-time interaction and visual task processing.

Key features of Beanbag Big Model 1.6

• Enhanced inference performance: The model shows significant improvement in inference speed, accuracy and stability, and can handle more complex business scenarios.
• Instant search and in-depth research: The model is equipped with instant search capability, supports searching based on incomplete information, and provides recommendations after multiple rounds of thinking and searching. the DeepResearch feature supports the rapid generation of research and analysis reports.
• Comprehensive multimodal understanding: The model natively supports multimodal thinking and is able to understand and process multiple types of data including text, images and video.
• Ability to operate a graphical user interface (GUI operation): Based on visual deep thinking and precise localization, the model is able to interact with browsers and other software to perform a variety of tasks efficiently.

Project address for Beanbag Big Model 1.6

• Project website::https://console.volcengine.com/ark/region:ark+cn-beijing/model/detail?Id=doubao-seed-1-6

How to use Beanbag Big Model 1.6

• Visit the official website: Visit the official project website for Beanbag Grand Model 1.6.Volcano Engine ModelDetails Page. On the official website, learn more details about the model, including features, performance, and application scenarios. Follow the prompts to complete registration and login.
• Getting the API key: In the Volcano Engine console, create an API Key to be used in subsequent API calls.
• Select model version: Choose a different version of the Beanbag Big Model 1.6 according to your needs.
• Write code to call the API: Write code in Python or another programming language that calls the API for Beanbag Big Model 1.6.

import requests
import json

# API密钥和接口地址
api_key = "your_api_key"
api_secret = "your_api_secret"
model_version = "doubao-seed-1.6"  # 或doubao-seed-1.6-thinking、doubao-seed-1.6-flash
api_url = f"https://api.volcengine.com/v1/model/{model_version}"

# 请求数据
data = {
    "input": "你的输入文本",
    "parameters": {
        "max_length": 256,  # 输出的最大长度
        "temperature": 0.7,  # 随机性参数
        "top_p": 0.9,  # 核心采样参数
        "top_k": 50,  # 核心采样参数
        "do_sample": True  # 是否采样
    }
}

# 设置请求头
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

# 发送请求
response = requests.post(api_url, headers=headers, data=json.dumps(data))

# 处理响应
if response.status_code == 200:
    result = response.json()
    print("模型输出：", result["output"])
else:
    print("请求失败，状态码：", response.status_code)
    print("错误信息：", response.text)

Performance of Beanbag Big Model 1.6

• GPQA Diamond test: The Beanbag 1.6-thinking model achieved a score of 81.5 in the GPQA Diamond test. This score reaches the first tier level in the world, making it one of the best reasoning models available.
• math assessment AIME25: The Beanbag 1.6-thinking model scored 86.3 on the math assessment AIME25. This is a 12.3-point improvement over the previous Beanbag 1.5 deep-thinking model, showing significant improvement.

Pricing model for the beanbag mega model 1.6

Pricing for the Beanbag Grand Model 1.6 is based on a unified model, and tokens are all billed consistently.

• Input length 0-32K::
  • Input Price: $0.8/million tokens.
  • output price: $8/million tokens.
• Input length 32K-128K::
  • Input Price: $1.2/million tokens.
  • output price: $16/million tokens.
• Input length 128K-256K::
  • Input Price: $2.4/million tokens.
  • output price: $24/million tokens.
• Input 32K, output within 200 tokens::
  • Input Price: $0.8/million tokens.
  • output price: $2/million tokens.

Core Benefits of Beanbag Big Model 1.6

• Multimodal processing capability: Processes and understands multiple types of data input, including text, images, and video, which gives it a significant advantage in multimedia content generation and understanding.
• Three modes of thinkingThe three modes provided are auto, thinking and non-thinking, adapting to different task requirements and complexity.
• long context window: Supports long context windows up to 256k, maximum input length up to 224k tokens, output length support up to 16k tokens, suitable for processing complex long text tasks.
• Powerful reasoning: Excellent performance on several authoritative assessments, especially in reasoning and math skills, with the ability to reason logically and problem solve quickly.
• Efficient batch processing and cache optimization: Supports batch processing and cache optimization, can efficiently process large-scale data, and is suitable for high-concurrency scenarios.
• Wide range of application scenarios: Applicable to a wide range of fields such as content creation, intelligent dialog, code generation, educational tutoring and multimodal content generation, it provides a powerful AI productivity tool for enterprises and developers.

People for Beanbag Big Model 1.6

• content creator: Writers, editors, journalists, self-publishing operators, etc., generate high-quality ad copy, news stories, stories, novels, and more.
• Developers and programmers: Generate code snippets with the aid of Beanbag Big Model 1.6 to improve development efficiency, or for troubleshooting errors in code.
• Educators and students: Teachers generate instructional resources to assist in lesson planning; students use them to answer subject matter questions and to assist in learning and research.
• Business decision makers and analysts: Support decision making by performing market analysis, risk assessment, etc. with the reasoning and analytical capabilities of the Beanbag Big Model 1.6.
• Intelligent customer service and chatbot developers: Provide a natural and smooth multi-round dialog experience with Beanbag Big Model 1.6 to improve the efficiency of user interaction.