GLM-4.7 Released: Saving Developer Aesthetics with 'Vibe Coding' and Challenging Top Models at 1/7 the Price

By late 2025, the direction of the AI model race seems to have shifted.

While others have been competing on parameters and computing power, Z.ai’s latest GLM-4.7 has taken a unique path: it doesn’t just make AI coding stronger; it makes AI understand “design.” Defined as a “next-generation coding partner,” this model makes a leap in logical reasoning while solving a long-standing pain point for full-stack developers—perfect backend logic with terrible frontend interfaces.

GLM-4.7 arrives with three major selling points: Vibe Coding (Aesthetic Programming), Preserved Thinking, and unbeatable value that is hard for individual developers and small teams to resist.

What is Vibe Coding? Finally, an AI that understands UI

Honestly, many engineers have been there: you ask an AI to write a web feature, the code runs, the logic is correct, but the button color, font spacing, and overall layout look like something out of the early 2000s.

This is the core problem GLM-4.7 aims to solve.

GLM-4.7 has achieved a major breakthrough in UI/UX perception. According to technical reports, “Vibe Coding” means the model can generate cleaner, more modern web code. It even shows visible improvements in layout and sizing precision when creating slide presentations.

In practical tests, whether requesting a “high-contrast dark mode” or a “pixel-style tower design,” GLM-4.7 produces results with strong visual impact. This is a godsend for independent developers; you no longer need to spend hours manually adjusting CSS margins and paddings. The results generated by the model are often demo-ready for clients.

It’s not just about code accuracy; it’s about an understanding of “aesthetics.”

Goodbye “Goldfish Memory”: Thinking Evolution for Agents

Beyond solving aesthetic issues, GLM-4.7’s stability in handling complex tasks is impressive. For developers used to AI coding tools like Claude Code, Cline, or Roo Code, the biggest fear is the AI “forgetting” its previous reasoning after several rounds of conversation, leading to new changes breaking old features.

GLM-4.7 introduces two targeted technologies to solve this “blackout” problem:

1. Preserved Thinking

This feature is designed specifically for Coding Agent scenarios. When performing complex tasks across multiple rounds of conversation (such as refactoring an entire project module), GLM-4.7 automatically retains thinking blocks across turns. This means it doesn’t need to re-derive the context from scratch every time but can “remember” its previous reasoning path. This significantly reduces information loss, allowing the AI to perform like a senior engineer with consistent logic during long-tail tasks.

2. Turn-level Thinking

This gives developers immense flexibility. You can manually control whether “Thinking Mode” is enabled for each round of conversation.

• Simple Queries: Turn off thinking to save tokens and time.
• Complex Debugging: Turn on thinking to ensure accuracy and stability.

This flexible control mechanism helped GLM-4.7 score 73.8% on SWE-bench Verified, a 5.8% increase over the previous generation. Even more impressively, it reached 42.8% on the HLE (Humanity’s Last Exam) benchmark when used with tools, demonstrating a qualitative leap in its mathematical and reasoning capabilities.

Open and Compatible: Seamlesly Integrating into Your Workflow

While many powerful models are locked into specific platforms, GLM-4.7 chooses to embrace the open-source ecosystem. This means high freedom for developers.

• Full Support for Major Agents: Official support has been announced for Claude Code, Kilo Code, Cline, and Roo Code. If your workflow is already tied to these tools, switching is virtually painless.
• Local Deployment Friendly: For teams prioritizing privacy or with local compute resources, GLM-4.7 weights are available on Hugging Face.
• Efficient Inference Frameworks: Native support for vLLM and SGLang means you can run this model on local servers with high efficiency, maintaining full control over your data.

The Price Disruptor: Top-Tier Experience at 1/7 the Cost

Here’s the kicker. In the tech world, performance matters, but cost often determines large-scale adoption.

Z.ai’s pricing strategy is highly aggressive. For GLM Coding Plan subscribers, they claim “Claude-class model at only 1/7 the price,” along with 3x the usage quota.

For teams with skyrocketing API bills or individual developers just starting out, this value proposition is incredibly attractive. In an environment where AI subscriptions often start at $20, GLM-4.7 provides a choice that saves money without sacrificing performance.

Frequently Asked Questions (FAQ)

To help you get started faster, here are some core questions about GLM-4.7:

Q1: How can I use GLM-4.7 in my IDE?

GLM-4.7 already supports major coding agent plugins. If you use Claude Code, simply update the model name to "glm-4.7" in your configuration file (e.g., ~/.claude/settings.json). For Cline or Roo Code users, you can connect via compatible API providers like OpenRouter or the native Z.ai API.

Q2: Can Vibe Coding really replace designers?

While GLM-4.7 can generate very modern and beautiful UIs, it currently acts more as an “executioner with design sense.” It can save you from the tedious process of writing CSS from scratch and produce high-quality initial drafts. For complex brand visual standards or creative ideation, professional designers remain essential, but GLM-4.7 certainly allows engineers to build respectable products without designer support.

Q3: Do I need to manually enable Preserved Thinking?

In most agent frameworks that support this feature, it is typically enabled automatically for complex multi-turn tasks or has a corresponding configuration option. Its core value lies in “automatically” preserving the thinking context, so developers generally don’t need to manually intervene in the internal memory mechanism; just focus on the task description.

In 2025, the competition in AI has shifted from “who is smarter” to “who is more useful” and “who is cheaper.” GLM-4.7, by precisely targeting developer pain points—solving frontend aesthetics, maintaining long-logic stability, and drastically reducing costs—has undoubtedly become one of the most noteworthy open-source models at the end of this year.

If you’re tired of building ugly web pages or fed up with expensive token costs, head over to the Z.ai official website and give it a try. This might be the coding partner you’ve been looking for.