On December 22, 2025, Zhipu AI (Z.ai) officially released GLM-4.7, the newest iteration in its General Language Model (GLM) family — drawing global attention in the world of open-source AI models. This model not only advances capabilities in coding and reasoning tasks, but also challenges the dominance of proprietary models like GPT-5.2 and Claude Sonnet 4.5 in key benchmarks.
GLM-4.7 enters the competitive landscape where high-performance AI is crucial for real-world development, research, and enterprise workflows. Its release marks a significant milestone for open-source large language models (LLMs) — both technologically and strategically.
What Is GLM 4.7?
GLM stands for General Language Model — a series of large language models developed by Zhipu AI, known for balancing strong performance with open-source accessibility. The GLM line has been progressively refined to support reasoning, multimodal tasks, coding, and tool-enabled workflows, with earlier versions such as GLM-4.5 and GLM-4.6 already recognized for high capability.
GLM-4.7 is the latest version in the GLM-4 line. Unlike a simple minor patch, it introduces meaningful architectural refinements and training improvements that deliver measurable gains across core AI tasks: programming, reasoning, tool usage, and multimodal generation. Importantly, it is released as open-source, enabling broad access for developers, researchers, and enterprise users without proprietary lock-in.
Some of the defining characteristics include:
- A “think before act” mechanism, where the model plans reasoning and tool steps before producing outputs — improving accuracy and reliability.
- Broader multimodal capabilities, extending text reasoning to visual and structured data.
- Stronger support for end-to-end workflows, including tool invocation and agentic behavior.
What Is New in GLM 4.7? How it Compare to GLM 4.6?
Advanced Coding Capabilities
One of the headline improvements in GLM-4.7 is a marked step forward in coding performance — particularly in handling multi-language and multi-step programming scenarios.
| Benchmark | GLM-4.7 | GLM-4.6 |
|---|---|---|
| SWE-bench Verified | 73.8% | 68.8% |
| SWE-bench Multilingual | 66.7% | 53.8% |
| Terminal Bench 2.0 | 41% | 23.5% |
According to benchmark data, GLM-4.7 achieves:
- 73.8% on SWE-bench Verified, a notable jump from GLM-4.6.
- 66.7% on SWE-bench Multilingual (+12.9%), showcasing improved cross-language competence.
- 41% on Terminal Bench 2.0 (+16.5%), indicating better performance in command-line and agent contexts.
These numbers demonstrate substantial strides in both code quality and stability — an important factor for developers using AI tools in real coding environments. Early real-world trials also reveal that GLM-4.7 completes complex front-end to back-end tasks more reliably than its predecessor.
Enhanced Reasoning and Tool Use
GLM-4.7 structures its reasoning pipeline into multiple modes:
- Interleaved reasoning, Model reasons before every response or tool invocation, which plans before each output.
- Retained reasoning, Retains reasoning context across turns, improving long-duration task performance., which preserves context and reduces repeated computation.
- Turn-level control, which adapts reasoning depth dynamically per request.
This yields stronger performance on reasoning benchmarks. For example, on the HLE (“Humanity’s Last Exam”) benchmark, GLM-4.7 achieved 42.8%, a 41% improvement over GLM-4.6 — and by some accounts outperforms GPT-5.1 on similar metrics.
Beyond raw numbers, these improvements translate into more coherent and accurate outputs for analytical queries, mathematical reasoning, and structured instruction following.
Improved Output Aesthetics and Multimodal Capabilities
While GLM-4.7 retains a strong focus on coding and reasoning, it also improves in broader communication tasks:
- Chat quality is more natural and contextually aware.
- Creative writing shows better stylistic variety and engagement.
- Role playing and immersive dialogs feel more human-like.
- Web & UI Code Generation: Produces cleaner and more modern user interfaces, with better layout and aesthetic quality.
- Visual Output: Better generation of slides, posters, and HTML designs with improved formatting and structure.
- Multimodal Support: Enhanced handling of text and other input types for broader application domains.
These qualitative upgrades move GLM-4.7 closer to general-purpose AI utility — not just a specialty model for developers.
Why Does GLM-4.7 Matter?
The launch of GLM-4.7 carries significant implications across technology, business, and wider AI research:
Democratization of Advanced AI
By making a high-performance model fully open source and accessible under permissive licensing, GLM-4.7 lowers barriers for startups, academic groups, and independent developers to innovate without prohibitive costs.
Competition with Closed Proprietary Models
In comparative benchmarks across 17 categories (reasoning, coding, agent tasks):
- GLM-4.7 remains competitive with GPT-5.1-High and Claude Sonnet 4.5.
- It outperforms several other high-tier models in open settings.
This highlights not just incremental gains — but meaningful leaps in performance.
GLM-4.7’s performance — especially in coding and reasoning — challenges the dominance of proprietary frameworks (like OpenAI’s GPT series and Anthropic’s Claude), offering comparable or superior results in several benchmarks.
This intensifies competition in the AI landscape, potentially driving faster innovation, better pricing models, and more diversity in AI offerings.
Strategic Implications for AI Competition
GLM-4.7’s performance challenges traditional hierarchies in AI capability:
- It pushes the benchmark performance frontier among open models.
- Competes with global proprietary leaders in real-world tasks.
- Raises the bar for specialized AI workflows, especially in software development and reasoning-heavy domains.
In this context, GLM-4.7 represents not just a technical step forward — but a strategic milestone in the AI ecosystem’s evolution.
What are real-world use cases for GLM-4.7?
Coding assistants and copilots
Primary adoption scenarios include integrated development environment (IDE) assistants, pull request summarizers, automated refactoring tools, and intelligent code review aides. The model’s improved code synthesis and terminal interaction make it suitable for “assistant as developer” patterns where the model performs or suggests multi-step changes to repository artifacts.
Agentic automation and orchestration
GLM-4.7’s agentic improvements are appropriate for orchestration tasks: automated deployment scripts, CI pipeline assistants, system monitoring agents that propose remediation steps, and pipeline triage bots that can reason across logs, code, and configuration artifacts to propose fixes. The “think before act” capability reduces noisy or unsafe tool calls in these contexts.
Knowledge work with long context
Legal and regulatory review, technical due diligence, research synthesis, and multi-document summarization benefit from long-context capabilities. GLM-4.7 can maintain extended session state and synthesize across larger corpora, enabling workflows such as cross-document Q&A and system-level analysis.
Multilingual engineering and documentation
Teams operating across English and Chinese (and other supported languages) can use GLM-4.7 for documentation translation, localized code comments, and international developer onboarding. The model’s multilingual benchmarks indicate improved accuracy and context handling across languages, which is useful for international product teams.
Prototyping and research
For research teams experimenting with agent architectures, tool chains, or new evaluation methodologies, GLM-4.7’s open distribution lowers barriers to rapid experimentation and reproducible comparison against other open models or proprietary baselines.
Conclusion:
GLM-4.7 is a landmark release in the world of AI:
- It pushes open-source models into performance realms once dominated by closed systems.
- It delivers tangible, real-world practical improvements in coding, reasoning, and agentic workflows.
- Its accessibility and adaptability offer a compelling platform for developers, researchers, and enterprises alike.
In essence, GLM-4.7 is not just another model upgrade — it’s a strategic marker of progress for open AI, challenging the status quo while expanding the frontiers of what developers and organizations can build.
To begin, explore GLM 4.7 and GLM 4.6’s capabilities in the Playground and consult the API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.
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