GLM-4.6: New SOTA Opensource KING! Powerful, Fast, & Cheap! Really Good AT Coding! (Fully Tested)

Key Concepts

• GLM 4.6: A new open-source language model by ZAI, focused on advanced agentic workflows, reasoning, and real-world coding.
• Context Window: The amount of text a language model can consider at once (GLM 4.6 supports 200k tokens).
• Agentic Workflows: AI systems designed to perform tasks autonomously, often involving planning, tool use, and reasoning.
• Kilo Code: An IDE extension that allows users to access and test AI models, including GLM 4.6.
• Open Router: An API provider that offers access to various AI models, including GLM 4.6.
• SAS Landing Page: A webpage designed to promote a Software as a Service (SaaS) product.
• SVG: Scalable Vector Graphics, a vector image format.
• Stochastic Reasoning: A method of reasoning that incorporates randomness and probability.
• Markov Modeling: A mathematical system that transitions from one state to another based on probabilistic rules.

GLM 4.6 Overview

• Release and Positioning: GLM 4.6 is a major upgrade from GLM 4.5, designed to compete with models like Claude and OpenAI's offerings.
• Key Features:
  • 200k context window.
  • Strong performance in software development tasks.
  • Advanced reasoning and search capabilities.
  • Focus on real-world coding tasks.
• Pricing:
  • Input: $0.06 per 1 million tokens (cached input at $0.11).
  • Output: $0.20 per 1 million tokens.
• Performance Benchmarks:
  • Outperforms open-source baselines like Deepseek version 3.2.
  • Nears Claude Sonnet 4 in overall performance.
  • Trails Claude Sonnet 4.5 in overall coding.
  • Excels in specific benchmarks like Mac, GPQ, and Live Codebench.
  • Slightly behind Claude Sonnet 4.5 in benchmarks like Swaybench rarified, Terminal Bench, and Tsquare.

Accessing and Using GLM 4.6

• Hugging Face: The model is uploaded on Hugging Face for local hosting.
• ZAI Chatbot: Accessible through the ZAI chatbot for unlimited use.
• Kilo Code: Free API access through Kilo Code with free credits.
• Open Router: Another API provider for accessing the model.

Testing and Examples

• Browser-Based OS Creation:
  • Task: Create a Mac OS-style browser-based OS from scratch.
  • Objective: Evaluate system design, architecture skills, and front-end design capabilities.
  • Result: Kilo Code rapidly generated a functional OS with a file manager, terminal, notes tab, and calculator. The generated OS was considered "probably the best OS I have saw any model generate."
• AI Slide Deck Generation:
  • Task: Create a slide deck about the "World of AI" YouTube channel.
  • Objective: Assess web search, tool usage, and reasoning capabilities.
  • Process: The model scraped the channel's webpage, extracted content (subscriber count, views, videos), and generated a slide deck.
  • Evaluation: The model demonstrated thorough reasoning with individual subtasks and impressive code speed. The slide deck covered channel overview, content focus (AI coding agents), etc. The presenter rated it 8/10 for creativity and content generation.
• SAS Landing Page Creation:
  • Task: Generate a SAS landing page.
  • Objective: Evaluate front-end UX capabilities and component generation.
  • Result: The model generated a unique and abstract landing page with a different color scheme and components like "experience AI in action," a pricing structure, testimonials, seamless integrations, and a Q&A section. The presenter stated, "This is something that I would actually deploy for my website."
• SVG Code Generation:
  • Task: Generate SVG code of a butterfly.
  • Objective: Assess proficiency in SVG code generation and creative feature addition.
  • Result: The model generated a detailed butterfly with eyes, antenna, a body structure, and wings.
• Mathematical and Reasoning Skills:
  • Task: Solve the coin flip problem: "You flipped a fair coin repeatedly. What's the expected number of flips until the pattern heads, tails, heads is appearing for the first time?"
  • Objective: Evaluate reasoning, modeling, and transition logic skills.
  • Process: The model used stochastic reasoning and Markov modeling, stated different conditions and expectations, and arrived at the correct answer of 10.

Key Arguments and Perspectives

• Cost-Effectiveness: GLM 4.6 is presented as a cost-efficient option for coding tasks compared to other models.
• Coding Performance: The model excels in real-world coding tasks, making it a strong choice for software development.
• Reasoning and Tool Usage: GLM 4.6 demonstrates advanced reasoning and tool usage capabilities, enhancing its overall performance.

Notable Quotes

• "This is probably the best OS I have saw any model generate." (Regarding the browser-based OS creation)
• "This is something that I would actually deploy for my website." (Regarding the SAS landing page)

Synthesis/Conclusion

GLM 4.6 is a promising open-source language model that offers a compelling combination of coding performance, reasoning capabilities, and cost-effectiveness. Its strong performance in real-world coding tasks, coupled with its advanced reasoning and tool usage, positions it as a viable alternative to larger, more expensive models. While its context window isn't the largest, its overall performance and pricing make it an attractive option for developers seeking a powerful and efficient coding model. The examples provided showcase its ability to generate complex applications, create engaging content, and solve challenging problems, highlighting its potential across various domains.