GLM 4.7 is INSANE For Software Dev...

Highlights

• GLM 4.7 achieves 73% on SWE bench at only $29/year, offering exceptional value compared to premium alternatives like Sonnet 4.5

• GLM 4.7 shows significant improvements over 4.6, with explicit optimization for Claude Code and new MCP tool integrations

• Pricing is dramatically cheaper than competitors: $3 for 120 prompts in GLM coding versus $10-40 prompts in Claude's entry-level plan

• GLM 4.7 implements superior backend architecture with centralized mock data management, outperforming Claude in code organization despite slightly weaker UI design

• The model suffers from poor autonomy and still has slow code generation speeds, requiring user steering and corrections

• GLM 4.7 now displays visible thinking process in Claude Code, fixing a critical issue from version 4.6

• Not recommended for fully autonomous coding projects, but excellent as a supplementary tool at its price point

Timeline

1. 00:00 - 00:40

   Introduction: GLM 4.7 Release and Market Context

   The video opens with context about ZAI's latest GLM 4.7 release, priced at $29 annually, claiming 73% performance on SWE bench benchmarks comparable to Sonnet 4.5. The speaker highlights the strategic timing of this release coinciding with ZAI's public offering and their unprecedented Reddit Q&A engagement targeting Western audiences. The model represents primarily a post-training improvement rather than architectural changes, with heavy optimization specifically for Claude Code as the priority framework. This section establishes why the release is significant: aggressive pricing combined with competitive benchmarks marks a notable shift in the AI model market landscape.

2. 00:40 - 01:50

   Technical Features and Pricing Comparison

   This section details GLM 4.7's technical specifications, including new MCP tool integrations that operate as separate servers requiring only API keys for functionality. The speaker provides a detailed pricing breakdown comparing GLM to Claude's plans, revealing substantial cost advantages: entry-level GLM pricing offers 120 prompts for $3 versus Claude's 10-40 prompts, with the gap widening at higher tiers ($30 gets 2,400 prompts versus Claude's 800 for $200). Monthly rates are doubled after the first month, though yearly plans offer significantly better value. These concrete numbers demonstrate why the pricing strategy is generating interest despite the model's architectural similarities to predecessors.

3. 01:50 - 03:10

   Benchmark Testing and UI Evaluation

   The speaker evaluates GLM 4.7's performance on the Humanity's Last Exam benchmark and tests both GLM and Claude's UI generation capabilities using a prompt for an AI-powered code review platform. While Claude produces cleaner design without emojis (aligning better with design language), GLM's output demonstrates acceptable design quality but with minor rendering issues where content length wasn't properly accounted for. The detailed comparison reveals both models' strengths: Claude excels in visual polish and consistency, while GLM shows competent but less refined design execution. This hands-on testing approach validates whether the claimed benchmark improvements translate to practical real-world performance.

4. 03:10 - 04:30

   Autonomous Coding Performance and Limitations

   Testing with a pre-configured Next.js project for a Netflix-like streaming platform exposes critical limitations in GLM 4.7's autonomy and code generation speed. The model exhibits slow processing times (unresolved from version 4.6) and poor contextual awareness, ignoring explicit instructions to use existing project folders and instead creating redundant applications. However, a significant fix from 4.6 is now visible: detailed thinking processes are displayed within Claude Code, indicating internal reasoning was previously hidden. The speaker notes these behavioral inconsistencies—sometimes performing capably, other times acting 'really dumb'—requiring constant user steering and correction, which contradicts claims of improved autonomy.

5. 04:30 - 05:25

   Code Architecture and Backend Implementation Quality

   After steering the model correctly, the analysis reveals surprising architectural strengths in GLM 4.7's code structure compared to Claude's implementation. GLM centralizes mock data into a single file with connected imports, enabling efficient backend migration when needed, whereas Claude scatters imports across multiple components requiring individual file updates. This architectural superiority demonstrates that GLM prioritizes scalability and maintainability despite weaker UI design capabilities. The speaker acknowledges this unexpected finding validates the benchmark claims, representing a dramatic leap from the problematic version 4.6 performance. This discovery is crucial for developers prioritizing backend-ready, scalable code over visual polish.

6. 05:25 - 06:06

   Final Recommendation and Use Case Assessment

   The speaker concludes by recommending the $29 yearly plan as an exceptional value proposition, particularly for developers already using Claude's $20 plan. However, the recommendation comes with clear caveats: GLM 4.7 is unsuitable for completely autonomous coding tasks due to persistent autonomy limitations and quirks requiring frequent user intervention. The appropriate use case is supplementary development assistance rather than primary code generation, where human oversight remains essential. The video ends by reiterating that while benchmarks are justified by real-world testing, the model's practical limitations prevent total reliance, making it an excellent value-add tool for hybrid development workflows.