GLM-4-32B-0414

GLM-4-32B-0414

About GLM-4-32B-0414

GLM-4-32B-0414 is a new generation model in the GLM family with 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including a large amount of reasoning-type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post-training stage, in addition to human preference alignment for dialogue scenarios, the team enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in areas such as engineering code, Artifact generation, function calling, search-based Q&A, and report generation. On several benchmarks, its performance approaches or even exceeds that of larger models like GPT-4o and DeepSeek-V3-0324 (671B)

Explore how GLM-4-32B-0414's powerful reasoning, code, and artifact generation capabilities can solve complex, real-world problems.

Intelligent Code Engineering

Beyond simple completion, GLM-4 analyzes codebases, identifies subtle logical errors, and suggests performance optimizations based on deep algorithmic understanding.

Use Case Example:

"Pinpointed a critical memory leak in a large-scale Rust application by reasoning through its execution flow, providing a precise fix overlooked by developers."

Advanced Research & Analysis

Accelerate scientific discovery by using GLM-4 to analyze complex datasets, generate and verify mathematical proofs, and draft technical papers with coherent reasoning.

Use Case Example:

"Assisted a bioinformatics researcher by formulating and solving a series of statistical models to analyze genomic data, reducing theoretical validation time by weeks."

Strategic Business Intelligence

Leverage GLM-4 for multi-step quantitative analysis on financial reports and market data, inferring causal relationships and generating detailed strategic recommendations.

Use Case Example:

"Analyzed a company's quarterly earnings and market trends to produce a multi-page investment thesis, outlining risks and growth opportunities with step-by-step financial reasoning."

Dynamic Content & Artifact Generation

Utilize GLM-4's artifact generation capabilities to create complex animations, interactive web designs, and detailed SVG graphics from natural language prompts.

Use Case Example:

"Generated a fully functional, responsive web UI for a mobile machine learning platform, including data visualization charts, directly from a detailed text description."

Automated Agent Workflow

Orchestrate complex multi-step agent tasks by leveraging GLM-4's instruction following and robust function calling for autonomous decision-making and execution.

Use Case Example:

"Developed an AI agent that autonomously manages cloud resource provisioning, dynamically adjusting configurations based on real-time load metrics via API calls."

Metadata

Create on

Apr 18, 2025

License

MIT

Provider

Z.ai

HuggingFace

Specification

State

Deprecated

Architecture

Calibrated

Yes

Mixture of Experts

No

Total Parameters

32B

Activated Parameters

32B

Reasoning

No

Precision

FP8

Context length

33K

Max Tokens

33K

Ready to accelerate your AI development?

Ready to accelerate your AI development?

Ready to accelerate your AI development?