GPT-5 Codex + GLM-4.6 : This MULTI-AGENT CODING Setup IS 3X CHEAPER & 2X BETTER than Claude Code!

Key Concepts:

• GLM 4.6: A language model praised for general tasks but identified with specific issues in debugging and planning.
• GPT-5 CodeX: A language model highlighted for its superior performance in planning and debugging, and cost-effectiveness.
• Kilo Code: A tool/platform used for coding, supporting different AI models and modes.
• Architect Mode: A specific mode within Kilo (or similar tools) designed for planning and outlining code structures.
• Code Mode: A mode for generating and applying actual code changes.
• Debug Mode: A mode for identifying and fixing issues in code.
• Orchestrator Mode: An advanced mode that automates the process of running different AI models in various modes to achieve a task.
• Reasoning Variant: A feature in AI models that allows them to show their thought process or "traces" during problem-solving.
• Tool Calls: Instructions or functions an AI model generates to interact with external tools or APIs.
• Claude Sonnet: Another language model, often compared to GLM and GPT-5 CodeX, but deemed less effective for certain tasks by the speaker.
• Klein Rue: Other coding tools mentioned, criticized for their lack of support for non-Claude models.

Challenges with GLM 4.6 and Reasoning Support

The video highlights specific issues with GLM 4.6, particularly its "finicky" nature when asked to debug or perform proper planning, often leading to deviations. A significant underlying reason is the inadequate support for GLM's "reasoning variant" in popular coding tools like Klene Rue, Kilo, and Claude Code. Although GLM 4.6 is an "autoreasoner" and performs reasoning internally, enabling its reasoning traces (as Kilo initially did) resulted in the model outputting "tool calls" within these traces, which "messes up the whole experience and doesn't work properly." Consequently, Kilo had to remove this support and is working to disable tool calls in reasoning. The speaker also notes that even with reasoning, GLM's reasoning is "a bit bugged" and can be excessively verbose.

Strategic Hybrid Approach: Combining GLM 4.6 with GPT-5 CodeX

To overcome GLM 4.6's limitations, the speaker proposes a hybrid strategy, integrating GPT-5 CodeX for specific tasks. GPT-5 CodeX is identified as a "pretty good model" for planning and debugging, being "comparatively cheaper than Claude Sonnet" and offering better caching, which further reduces operational costs.

1. Planning and Architecting Workflow

For planning and architecting new refactors, the following step-by-step process using Kilo Code is recommended:

1. Set Architect Mode: In Kilo, select the "architect mode."
2. Configure Model: Set the model for architect mode to GPT-5 CodeX.
3. Develop Plan: Instruct GPT-5 CodeX to build out the refactor plan, ideally asking it to output the plan in a "markdown file."
4. Refine Plan: Engage in a dialogue with the model to "flesh out the plan a bit first."
5. Switch to Code Mode: Once the plan is finalized, switch to "code mode" (typically using GLM 4.6) to apply the necessary code changes. While GLM 4.6 can also be used in architect mode and "often match the performance of GPT-5 CodeX" in this setup, GPT-5 CodeX is considered "a bit better" for this specific task, especially for dialing in the plan efficiently.

2. Enhanced Debugging Strategy

GLM 4.6 is capable of handling "smaller issues" but struggles with "bigger ones," frequently getting "stuck and introduces more issues while fixing others." This behavior has also been observed with "the newer Sonnet." GPT-5 CodeX, however, "seems to excel at debugging," capable of finding even "smaller issues," particularly when provided with logs.

The recommended debugging process involves:

1. Select Debug Mode: In Kilo, choose "debug mode."
2. Provide System Prompt: Give GPT-5 CodeX a specific system prompt to guide its debugging efforts.
3. Utilize GPT-5 CodeX: Primarily use GPT-5 CodeX for debugging, as it "works the best." The speaker concludes that "combining GPT-5 CodeX and GLM is the best approach" for a robust development workflow.

Kilo Configuration and Orchestrator Mode

To optimize workflow efficiency, users can configure default models for different modes within Kilo. By accessing the "edit option" and then "set the default API profile for different modes," users can save time and minimize errors when rapidly switching between architect, code, and debug modes.

The video also introduces the Orchestrator Mode, which theoretically automates the entire process by having an orchestrator model run different modes with their default configurations, prompt them, and return the consolidated result. An example setup suggested is using GPT-5 CodeX as the orchestrator, GLM 4.6 for "code mode," and GPT-5 CodeX again for "architect mode." While this mode can "orchestrate the GLM model," the speaker personally prefers manual control to understand the code being written. However, for users who prefer to delegate tasks to an agent and return later, this mode can be highly beneficial.

Cost-Benefit Analysis and Critique of Tool Ecosystem

The speaker justifies the hybrid setup by highlighting its significant performance improvements against a modest cost increase. Using GPT-5 CodeX with GLM 4.6 for specific tasks raises API costs by approximately "$20 a month," which is deemed acceptable given the "20 to 30% and sometimes even more for complex repositories" performance improvement. This combined approach offers "way better" performance than relying solely on Claude 4.5 Sonnet. The speaker also notes that using GPT-5 CodeX via the ChatGPT plan offers sufficient limits for most users, though it is "a bit slower."

A strong critique is directed at the current state of AI coding tools:

• Undervaluation of GLM: Dismissing GLM 4.6 is labeled as "ignorant behavior," as GLM is considered "insanely good for the price."
• Claude-Centric Tooling: Tools like Klein, Rue, and others are "super fine-tuned for Claude," creating a misleading perception that Sonnet is superior, when in fact, it's the tools that "lack proper support" for other powerful models.
• Lack of GLM/GPT-5 CodeX Support: The speaker expresses frustration that tools like Klein "doesn't even have support for the GLM coding plan," calling it "mind-boggling." The argument is that "tools are being overly engineered for Claude models while GPT-5 CodeX and GLM aren't getting enough credit for what they can really do." Kilo Code is actively working on improving support for these models, and the speaker hopes other tools will follow suit.

Conclusion

The video strongly advocates for a strategic, hybrid approach to AI-assisted coding, combining the strengths of GLM 4.6 with GPT-5 CodeX. By leveraging GPT-5 CodeX for critical planning and debugging tasks and GLM 4.6 for code generation, developers can effectively overcome the limitations of individual models. This strategy leads to significant performance improvements (20-30% or more) at a justifiable API cost increase (around $20/month). The speaker also critically highlights the current bias in AI tool development towards Claude models, urging for broader and more equitable support for powerful alternatives like GLM 4.6 and GPT-5 CodeX to unlock their full potential. This approach offers a more robust, efficient, and cost-effective workflow for complex software development tasks.