GLM 4.5: The Open-Source Powerhouse Quietly Outperforming Qwen and Kimi

The real AI race isn’t fought on news headlines—it’s happening in GitHub commits, Hugging Face leaderboards, and Discord threads buzzing with 200+ overnight messages.

While the AI community dissected Kimi-K2, Qwen3, and Qwen3-Coder, Chinese AI firm Zhipu AI silently released GLM 4.5. This open-source model delivers exceptional reasoning, coding, and agent capabilities without fanfare. Here’s why developers and enterprises should pay attention.

1. The Quiet Rise of GLM 4.5

Who’s Behind This Model?

• Zhipu AI: Recognized by OpenAI as a “potential major dominator” in global AI development.
• Proven Track Record: Their earlier GLM 4 (32B parameters) consistently exceeded performance expectations.
• Mission-Driven: Focused on auditable, deployable open-source AI accessible to all.

Two Versions, One Goal

Core Strengths:
✅ Integrated reasoning, coding, and agent task execution
✅ Full-weight openness on Hugging Face and ModelScope
✅ Auditable architecture for enterprise security

2. Performance Breakdown: Where GLM 4.5 Excels

A. Agent Capabilities: Rivaling Claude and GPT-4

Unlike standard chatbots, GLM 4.5 executes multi-step workflows using:

• Native function calling
• 128K context processing
• Real-time web browsing

Benchmark Dominance:

• TAU-Bench (retail/airline automation): Top performer
• BFCL-v3 (function calling): Leader
• BrowseComp (web tasks): Beat Claude-4-Opus, trailed OpenAI’s best by just 2%

Source: Zhipu AI’s agent benchmark results

Why this matters: Automate complex tasks like data analysis, API integrations, or travel planning without manual coding.

B. Reasoning Power: STEM Specialist

In “thinking mode,” GLM 4.5 achieves near-top-tier results:

• MMLU-Pro: 84.6% (general knowledge)
• AIME24: 91% (advanced math)
• MATH500: 98.2% (problem-solving)
• GPQA: 79.1% (scientific reasoning)

Competitive Positioning:
Matches Gemini Pro and GPT-4.1 on technical tasks—ideal for research or engineering workloads.

Performance across 8 reasoning/coding tasks

C. Coding Proficiency: From Scripts to Full Applications

GLM 4.5 builds production-ready projects, not just code snippets:

• SWE-bench Verified: 64.2% (real GitHub issue resolution)
• Terminal Bench: 37.5% (CLI operations)
• Project Types: Full-stack web apps, game logic, slide generation

Head-to-Head Wins:

• Outperformed Qwen3-Coder in 80.8% of tasks
• Beat Kimi-K2 in >50% of evaluations
• Competitive with Claude 4 Sonnet

Real-world coding task results

Tool Compatibility:

• Seamless integration with Claude Code, Gemini CLI
• Supports KiloCode, Clein, and OpenAI-style endpoints

3. Technical Architecture: The Engine Behind the Performance

GLM 4.5 leverages a self-developed Mixture of Experts (MoE) framework:

• Dynamic Compute Routing: Activates specialized sub-networks based on task complexity
• Resource Optimization: Uses only necessary “experts” for efficiency
• Native Agent Support: Built-in tool use/API call capabilities—no plugins required

Translation: It works like an engineering team where simple tasks get one specialist, while complex problems trigger full-team collaboration. This enables true agent behavior out-of-the-box—a rarity in open-source models.

4. Practical Advantages: Cost, Speed & Control

Key Differentiators

• Lower Cost: Cheaper than DeepSeek, Kimi K2, and Qwen
• Blazing Speed: Optimized inference performance
• Local Deployment: GLM 4.5 Air runs on high-spec Mac Studio hardware

GLM 4.5 vs. GLM 4.5 Air specifications

Enterprise Value:

• Avoid vendor lock-in or API dependencies
• Fine-tune models for domain-specific needs
• Maintain data sovereignty

5. Hands-On: How to Test GLM 4.5 Free Today

Method 1: VS Code Integration (Zero Cost)

1. Install development tools:

   • KiloCode
   • Clein
2. Configure settings:

   • Open extension settings
   • Select GLM 4.5 or GLM 4.5 Air as primary model

Model selection in Clein

Method 2: Direct API Access

1. Get API key from Zhipu AI
2. Integrate via:

   • Claude-compatible endpoints
   • OpenAI-style API structure
   • Private cloud deployment (docs: Zhipu AI Blog)

6. Real-World Implementation Examples

Case 1: Game Development

Prompt: “Generate a Flappy Bird clone in Python with collision detection and scoring.”
Output: Playable game with complete logic/assets in <2 minutes.

Case 2: Presentation Automation

Workflow:

1. Upload research paper
2. Request: “Create 12-slide summary with diagrams and citations.”
3. Model:

   • Extracts key points
   • Designs layout
   • Adds CC-licensed visuals
   • Formats references

Case 3: Full-Stack Application

Prompt: “Build a task manager with React frontend, Flask backend, and user auth.”
Iteration Cycle:

• Initial code output in 45 seconds
• “Add dark mode support” → instant UI update
• “Integrate calendar sync” → functional API connection

7. Essential Questions Answered (FAQ)

Q: Is GLM 4.5 truly open-source?
A: Yes. Weights are publicly available on Hugging Face/ModelScope for auditing, modification, and offline deployment—unlike API-only models.

Q: GLM 4.5 vs. GLM 4.5 Air—which should I use?
A: Choose GLM 4.5 for maximum capability (cloud/high-performance servers). Use GLM 4.5 Air for local dev work (faster response, lower resource needs).

Q: How does its coding performance compare?
A: Based on verified benchmarks:

• Outperforms Qwen3-Coder in 80.8% of tasks
• Beats Kimi-K2 in >50% of evaluations
• Nears Claude 4 Sonnet’s capability

Q: What does ‘agentic capability’ mean practically?
A: It can:

• Execute multi-step workflows (e.g., “Analyze this dataset → email insights to team”)
• Call APIs/tools without manual coding
• Adapt actions based on real-time inputs

Q: Will free access continue?
A: Currently available via:

• Free tiers on KiloCode/Clein
• Trial API credits
• Permanent local use after model download

8. Why GLM 4.5 Changes the Game

1. Triple-Threat Ability: First open-source model matching top proprietary models in reasoning, coding, AND agent tasks.
2. Transparency Advantage: Full auditability resolves enterprise security/ethics concerns.
3. Cost Efficiency: 30-50% cheaper operation than comparable models.
4. Deployment Flexibility: Local operation unlocks data-sensitive industries (healthcare/finance).
5. Architecture Innovation: MoE design sets new standards for efficient intelligence scaling.

The bottom line: GLM 4.5 proves open-source models can compete with closed ecosystems—while giving developers full control. Its quiet release speaks louder than marketing hype: raw capability trumps buzz.