Qwen 3 2507: NEW Opensource LLM KING! NEW CODER! Beats Opus 4, Kimi K2, and GPT-4.1 (Fully Tested)

Coin 3 235B A22B: A Groundbreaking Open-Source Language Model

Key Concepts:

• Open-Source Language Model: A language model whose source code is publicly available, allowing for modification and distribution.
• Parameters: The variables that a language model learns during training, influencing its performance and capabilities.
• Instruct Model: A language model specifically trained to follow instructions and engage in dialogue.
• Thinking Model: A language model designed for deeper logical reasoning and planning.
• Benchmarks: Standardized tests used to evaluate the performance of language models across various tasks.
• Context Understanding: The ability of a language model to process and retain information from previous parts of a conversation or document.
• Human Preference Alignment: Training a language model to generate responses that are more aligned with human values and expectations.
• Agentic Capabilities: The ability of a language model to plan, reason, and execute tasks autonomously, often involving tool use.
• Ollama/LM Studio: Tools for running language models locally on a computer.
• Open Router: A platform that provides access to various language models through a free API.

Introduction of Coin 3 235B A22B

Alibaba has released a new open-source language model called Coin 3 235B A22B, which is a significant advancement in the field. It outperforms Kimmy K2, a previously high-performing open-source model, in nearly every aspect and sets new state-of-the-art benchmarks.

Model Architecture and Training

The Coin 3 upgraded model has 235 billion total parameters and 22 billion active parameters. Unlike previous hybrid reasoning models, this release uses two separate models:

• Instruct Model: Focused on following instructions and dialogue.
• Thinking Model: Designed for deeper logical reasoning and planning.

This dual approach enhances capabilities in instruction following, logic, text comprehension, science, coding, and tool usage. It also improves longtail knowledge across languages and offers enhanced 256k context understanding. The model is better aligned with human preferences, especially for subjective tasks, making it more helpful in conversation and creative writing.

Performance Benchmarks

The model excels in coding, math, agentic testing, and tool use. It performs exceptionally well compared to models like Kimmy K2 (Opus version) and DeepSeek V3.

Accessing the Model

The model can be accessed through Quen's chatbot under the "Quen 3 235B A22B" model card. The instruct and thinking models are available on Hugging Face, allowing for local installation using tools like Ollama or LM Studio. It can also be used for free through Open Router's API.

Practical Demonstrations and Examples

1. SVG Code Generation: The model was prompted to create a butterfly in SVG code. The generated code produced a symmetrical butterfly design with well-defined front and back wings, demonstrating its proficiency in code generation.
2. Front-End Web App Development: The model was tasked with creating a responsive task management web app with a calendar view, task list UI, and task completion options. Without enabling the thinking mode, the model generated approximately 1300 lines of code, resulting in a functional "Task Flow" app with a calendar integration, task management features, and animations.
3. Web Scraping and Data Visualization: Using Client, the model was instructed to write Python code to scrape YouTube trending videos and visualize the data in a bar chart using Matplotlib. The model successfully scraped data, including video titles, channels, and views, and stored it in a data.json file.
4. Reasoning Puzzle: The model was presented with the classic "farmer, fox, chicken, and grain" river crossing puzzle. It correctly identified all the steps required to safely transport all items across the river, demonstrating its reasoning capabilities.

Key Takeaways and Conclusion

The Coin 3 235B A22B model represents a significant advancement in open-source language models, particularly in coding, reasoning, and instruction following. The separation of instruct and thinking models allows for more targeted use cases and improved performance. The model is accessible through various platforms, including Quen's chatbot, Hugging Face, and Open Router, making it widely available for experimentation and development. The practical demonstrations highlight its capabilities in code generation, web development, data analysis, and logical reasoning.