Z.ai: GLM 4.6
by z-ai
Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex...
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Benchmarking Z.ai: GLM 4.6
Coding Performance
Coding benchmarks and performance metrics for development tasks
Coding Benchmarks
| Benchmark | Score |
|---|---|
| AA Coding Index | 29.5% |
| LiveCodeBench | 69.5% |
| SciCode | 38.4% |
| TerminalBench Hard | 25.0% |
| LCR | 54.3% |
| IFBench | 43.4% |
Speed & Efficiency
| Metric | Value |
|---|---|
| Output Speed | 39 tok/s |
Performance metrics from Artificial Analysis
Real-World Usage
Real-world usage statistics from the Kilo Code community
Weekly Token Usage
Mode Rankings (Last Week)
Where this model ranks for each built-in mode
Code
Write, modify, and refactor code
Ask
Get answers and explanations
Debug
Diagnose and fix software issues
Orchestrator
Coordinate tasks across multiple modes
Real-world metrics from the Kilo Code Leaderboard
Pricing
Cost per 1 million tokens
Example Cost
Analyzing a 10,000 line codebase (≈40k input tokens, 10k output tokens) costs approximately $0.0346
Coding Capabilities
Features and parameters relevant to coding tasks
Coding Features
Pricing details from OpenRouter
Technical Details
Architecture and implementation specifications
- Model ID
- z-ai/glm-4.6
- Artificial Analysis Slug
- glm-4-6-reasoning
- Created
- September 30, 2025
- Tokenizer
- Other
- Input Modalities
- text
- Context Window
- 202,752 tokens
- Max Completion Tokens
- 131,072 tokens
- Input Price
- $0.43 per 1M tokens
- Output Price
- $1.74 per 1M tokens
- Cache Read Price
- $0.08 per 1M tokens
- Content Moderation
- Disabled
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