GLM-5.1: Properties, Measurements, Capabilities & How to Use It

Z.ai has come out with its next generation AI model and named it GLM-5.1. With its combination of wide model size, efficiency, and high-level reasoning functions, the model represents a major step forward in large-scale linguistic models. The system improves on previous GLM models by introducing an advanced Mixture-of-Experts framework, which enables it to perform complex multi-step tasks faster, with more accurate results.

GLM-5.1 is also powerful because of its support for the development of agent-based systems that require advanced reasoning capabilities. The model even introduces new features that improve both coding skills and understanding of long content. All of this has implications for real AI applications and developers’ workflows.

This leaves no room for doubt that the introduction of GLM-5.1 is an important update. Here, we focus on just that, and learn all about the new GLM-5.1 and its capabilities.

Components of GLM-5.1 Modeling

GLM-5.1 builds on modern LLM design principles by integrating efficiency, scalability, and remote content management into a unified architecture. It helps maintain efficiency with its ability to manage up to 100 billion parameters. This allows practicality in daily activities.

The system uses a hybrid attention mechanism and an optimized recording pipeline. This enables it to excel in tasks that require handling long documents, reasoning, and code generation.

Here are all the components that make up its architecture:

• Mix-of-Expertise (MoE): The MoE model has 744 billion parameters, dividing it among 256 experts. The system uses top-8-routing, which allows eight experts to work on each token, and one expert working on all tokens. The system requires about 40 billion parameters per token.
• Note: The program uses two types of attention methods. These include Multi-head Latent Attention and DeepSeek Sparse Attention. The system can handle 200000 tokens, as its maximum capacity reaches 202752 tokens. The KV-cache system uses compressed data, operating at LoRA rank 512 and head dimension 64 to improve system performance.
• Structure: The system consists of 78 layers, which works out to 6144 hidden size. The first three layers follow a standard dense structure, while the following layers use smaller MoE blocks.
• Predictive decoding (MTP): The decoding process is faster with Speculative Decoding because it uses a multi-token prediction header, allowing simultaneous prediction of multiple tokens.

GLM-5.1 achieves its large scale and extended understanding of the situation by using these features, which require less processing power than a fully dense system.

How to access GLM-5.1

Developers can use GLM-5.1 in many ways. Complete model weights are available as open source software under the MIT license. The following list contains some of the available options:

• Hug Face (MIT license): Weights are available for download. The system requires enterprise GPU hardware as its minimum requirement.
• Z.ai API / Coding Programs: The service offers direct API access at a cost of $1.00 per million tokens and $3.20 per million tokens. The program works with Claude and the OpenAI system toolchains.
• Third party platforms: The system works with inference engines, including OpenRouter and SGLang that support GLM-5.1 set models.
• Local Use: Users with sufficient hardware resources can run GLM-5.1 locally with vLLM or SGLang tools when they have multiple B200 GPUs or similar hardware.

GLM-5.1 provides open weights and commercial API access, making it available to both business entities and individuals. Mainly in this blog, we will use the Hugging Face token to access this model.

GLM-5.1 Benchmarks

Here are the various scores obtained by GLM-5.1 in all benchmarks.

Coding

GLM-5.1 demonstrates exceptional ability to complete planning tasks. Its code performance achieved a score of 58.4 in SWE-Bench Pro, surpassing both the GPT-5.4 (57.7) and the Claude Opus 4.6 (57.3). GLM-5.1 scored over 55 in all three coding tests, including SWE-Bench Pro, Terminal-Bench 2.0, and CyberGym, to secure third place worldwide behind GPT-5.4 (58.0) and Claude 4.6 (57.5) overall. The system outperforms GLM-5 by a significant margin, showing its better performance in coding tasks with 68.7 points compared to 48.3. The new system allows GLM-5.1 to generate complex code with greater accuracy than before.

Agentic

GLM-5.1 supports agent workflows, which include multiple steps that require both programming and coding as well as tooling. This program shows significant progress during long working hours. Through its performance in the optimization function of VectorDBBench, GLM-5.1 performed 655 iterations, including more than 6000 tool functions to find several algorithmic optimizations. It also maintains its development track after reaching 1000 tool usage, which proves its ability to keep improving with continuous optimization.

• VectorDBBench: Achieved 21,500 QPS with 655 iterations (6× gain) in index optimization function.
• KernelBench: 3.6× ML performance gain for GPU kernels vs 2.6× for GLM-5, going back 1000 turns.
• Debugging: Build a complete Linux desktop stack from scratch within 8 hours (programming, testing, debugging) as described by Z.ai.

Consultation

GLM-5.1 gives excellent results in all general logic tests and QA validation tests. The program shows performance results similar to the best programs used to assess general intelligence.

The GLM-5.1 scored 95.3% on the AIME, which is an advanced math test, and 86.2% on the GPQA, which tests advanced question-answering skills. Scores approach the highest performance of the top programs, including GPT-5.4 which achieved 98.7% and 94.3% in two separate tests. GLM-5.1 demonstrates broad academic abilities, resulting in high achievement across multiple disciplines and Olympiad competitions.

GLM-5.1 Skills

GLM-5.1 excels in three specific tasks – long-term planning, code generation, and multivariable logical reasoning. The software can generate and modify computer code, while it can solve difficult questions and perform complex tasks with excellent results. The system allows developers to create “agents” that use their active calling and structured output capabilities to interact with various tools. The two functions shown here demonstrate the system’s programming capabilities. The program allows users to solve problems using information, leading to intuitive solutions that often involve coding.

Exercise 1: Finding the Factor of a Large Number

Notify:

“Hello, you are an excellent teacher, so help me with this question.

Q. Write the Python code to find the Factorial of a Large Number.”

The answer comes from GLM-5.1:

def factorial_large(n: int) -> str:
    if n < 0: return ""
    if n == 0: return "1"

    # Array to store individual digits (stored in reverse order for easier carry handling)
    res = [1]

    for x in range(2, n + 1):
        carry = 0

        # Multiply x with every digit in the current result
        for i in range(len(res)):
            product = res[i] * x + carry
            res[i] = product % 10
            carry = product // 10

        # Append the remaining carry to the result array
        while carry:
            res.append(carry % 10)
            carry //= 10

    # The digits are stored in reverse order, so reverse and join them
    return "".join(map(str, res[::-1]))

Output:

Decision: The program shows good performance because it follows a two-point process, which provides the best performance results. The solution uses very little resources while using active helper functions. The implementation is in a short format, without full papers and comments. This makes it more suitable for solving problems quickly instead of using it in production environments, which require readable and maintainable code.

Exercise 2: Checking if a String is a Palindrome

Notify:

“Hello, you are an excellent teacher, so help me with this question.

Q. Given the string s, return true if is can be a palindrome after removing approximately one letter from it.”

The answer comes from GLM-5.1:

def validPalindrome(s: str) -> bool:
    def is_palindrome(l: int, r: int) -> bool:
        while l < r:
            if s[l] != s[r]:
                return False
            l += 1
            r -= 1
        return True

    left, right = 0, len(s) - 1

    while left < right:
        if s[left] != s[right]:
            return is_palindrome(left + 1, right) or is_palindrome(left, right - 1)
        left += 1
        right -= 1

    return True

Output:

Decision: The response from GLM-5.1 shows the efficiency combined with the technical validity. Demonstrates ability to perform complex numerical operations through manual digit processing. The program achieves its design goals through its iterative approach, which combines performance and correct output. The implementation exists in a concise format and provides limited documentation on basic error handling. This makes the code suitable for algorithm development but not suitable for production use because that environment requires clear, extensible, and robust functionality.

General Review of GLM-5.1 Skills

GLM-5.1 provides many applications with its open source infrastructure and its complex system architecture. This allows developers to create deep thinking capabilities, code generation functions, and tooling programs. The system retains all the capabilities of the existing GLM family with small MoE capabilities and long-range capabilities. It also introduces new functions that allow dynamic logic and debugging loop execution. With its open frameworks and cost-effective API options, the system provides access to research while supporting practical applications in software engineering and other fields.

The conclusion

GLM-5.1 is a live example of how current AI systems are improving their efficiency and robustness, while improving their reasoning capabilities. It ensures high performance with its Mixture-of-Experts construction, while keeping operating costs reasonable. Overall, this system allows the management of real AI applications that require extensive functionality.

As AI moves towards agent-based systems and extended context understanding, GLM-5.1 establishes a foundation for future developments. Its routing and attention system, as well as its multi-token prediction system, create new possibilities for future large-scale language models.