GLM-5.1: Structure, Benchmarks, Capabilities & Find out how to Use It

Z.ai is out with its next-generation flagship AI mannequin and has named it GLM-5.1. With its mixture of intensive mannequin dimension, operational effectivity, and superior reasoning features, the mannequin represents a serious step ahead in massive language fashions. The system improves upon earlier GLM fashions by introducing a complicated Combination-of-Consultants framework, which allows it to carry out intricate multi-step operations quicker, with extra exact outcomes.

GLM-5.1 can be highly effective due to its assist for the event of agent-based techniques that require superior reasoning capabilities. The mannequin even presents new options that improve each coding capabilities and long-context understanding. All of this influences precise AI purposes and builders’ working processes.

This leaves no room for doubt that the launch of the GLM-5.1 is a crucial replace. Right here, we give attention to simply that, and be taught all in regards to the new GLM-5.1 and its capabilities.

GLM-5.1 Mannequin Structure Parts

GLM-5.1 builds on fashionable LLM design ideas by combining effectivity, scalability, and long-context dealing with right into a unified structure. It helps in sustaining operational effectivity via its capability to deal with as much as 100 billion parameters. This permits sensible efficiency in day-to-day operations.

The system makes use of a hybrid consideration mechanism along with an optimized decoding pipeline. This permits it to carry out successfully in duties that require dealing with prolonged paperwork, reasoning, and code era.

Listed below are all of the elements that make up its structure:

• Combination-of-Consultants (MoE): The MoE mannequin has 744 billion parameters, which it divides between 256 specialists. The system implements top-8-routing, which allows eight specialists to work on every token, plus one knowledgeable that operates throughout all tokens. The system requires roughly 40 billion parameters for every token.
• Consideration: The system makes use of two kinds of consideration strategies. These embody Multi-head Latent Consideration and DeepSeek Sparse Consideration. The system can deal with as much as 200000 tokens, as its most capability reaches 202752 tokens. The KV-cache system makes use of compressed knowledge, which operates at LoRA rank 512 and head dimension 64 to reinforce system efficiency.
• Construction: The system comprises 78 layers, which function at a hidden dimension of 6144. The primary three layers comply with a typical dense construction, whereas the next layers implement sparse MoE blocks.
• Speculative Decoding (MTP): The decoding course of turns into quicker via Speculative Decoding as a result of it makes use of a multi-token prediction head, which allows simultaneous prediction of a number of tokens.

GLM-5.1 achieves its massive scale and prolonged contextual understanding via these options, which want much less processing energy than a whole dense system.

Find out how to Entry GLM-5.1

Builders can use GLM-5.1 in a number of methods. The whole mannequin weights can be found as open-source software program underneath the MIT license. The next record comprises a few of the out there choices:

• Hugging Face (MIT license): Weights out there for obtain. The system wants enterprise GPU {hardware} as its minimal requirement.
• Z.ai API / Coding Plans: The service supplies direct API entry at a price of roughly $1.00 per million tokens and $3.20 per million tokens. The system works with the present Claude and OpenAI system toolchains.
• Third-Occasion Platforms: The system features with inference engines, which embody OpenRouter and SGLang that assist preset GLM-5.1 fashions.
• Native Deployment: Customers with ample {hardware} assets can implement GLM-5.1 regionally via vLLM or SGLang instruments after they possess a number of B200 GPUs or equal {hardware}.

GLM-5.1 supplies open weights and business API entry, which makes it out there to each enterprise companies and people. Significantly for this weblog, we are going to use the Hugging Face token to entry this mannequin.

GLM-5.1 Benchmarks

Listed below are the assorted scores that GLM-5.1 has obtained throughout benchmarks.

Coding

GLM-5.1 exhibits distinctive capability to finish programming assignments. Its coding efficiency achieved a rating of 58.4 on SWE-Bench Professional, surpassing each GPT-5.4 (57.7) and Claude Opus 4.6 (57.3). GLM-5.1 reached a rating above 55 throughout three coding exams, together with SWE-Bench Professional, Terminal-Bench 2.0, and CyberGym, to safe the third place worldwide behind GPT-5.4 (58.0) and Claude 4.6 (57.5) total. The system outperforms GLM-5 by a big margin, which exhibits its higher efficiency in coding duties with scores of 68.7 in comparison with 48.3. The brand new system permits GLM-5.1 to supply intricate code with larger accuracy than earlier than.

Agentic

The GLM-5.1 helps agentic workflows, which embody a number of steps that require each planning and code execution and power utilization. This method shows vital progress throughout extended operational intervals. By means of its operation on the VectorDBBench optimization job, GLM-5.1 executed 655 iterations, which included greater than 6000 instrument features to find a number of algorithmic enhancements. Additionally maintains its improvement monitor after reaching 1000 instrument utilization, which proves its capability to maintain bettering via sustained optimization.

• VectorDBBench: Achieved 21,500 QPS over 655 iterations (6× acquire) on an index optimization job.
• KernelBench: 3.6× ML efficiency acquire on GPU kernels vs 2.6× for GLM-5, persevering with previous 1000 turns.
• Self-debugging: Constructed a whole Linux desktop stack from scratch inside 8 hours (planning, testing, error-correction) as claimed by Z.ai.

Reasoning

GLM-5.1 supplies glorious outcomes throughout customary reasoning exams and QA analysis exams. The system demonstrates efficiency outcomes that match main techniques used for normal intelligence evaluation.

GLM-5.1 achieved 95.3% on AIME, which is a complicated math competitors, and 86.2% on GPQA, which exams superior question-answering skills. The scores method the very best efficiency of prime techniques, which embody GPT-5.4 that achieved 98.7% and 94.3% in two separate assessments. GLM-5.1 demonstrates intensive educational capabilities, which lead to excessive achievement throughout a number of disciplines and Olympiad competitions.

GLM-5.1 Capabilities

GLM-5.1 performs exceptionally effectively in three particular duties – long-term planning, code era, and multi-turn logical reasoning. The software program can produce and debug pc code, whereas it might probably clear up tough inquiries and execute advanced duties with glorious outcomes. The system allows builders to assemble “brokers” that use its operate calling and structured output capabilities to work together with varied instruments. The 2 duties proven right here display the system’s programming capabilities. The system permits customers to resolve issues via prompts, resulting in correct options that often embody code.

Activity 1: Discovering the Factorial of a Giant Quantity

Immediate:

“Hello, you might be a wonderful code teacher, so assist me with this query.

Q. Write Python code for locating the Factorial of a Giant Quantity.”

Response by GLM-5.1:

def factorial_large(n: int) -> str:
    if n 

Output:

Verdict: The system exhibits environment friendly functioning as a result of it follows a two-pointer method, offering the very best efficiency outcomes. The answer makes use of minimal overhead assets whereas implementing efficient helper features. The implementation exists in a brief format, missing full documentation and commentary. This makes it extra appropriate for fixing issues quickly as an alternative of utilizing it in manufacturing environments, which want readable and maintainable code.

Activity 2: Checking if a String is a Palindrome

Immediate:

“Hello, you might be a wonderful code teacher, so assist me with this query.

Q. Given a string s, return true if s generally is a palindrome after deleting at most one character from it.”

Response by GLM-5.1:

def validPalindrome(s: str) -> bool:
    def is_palindrome(l: int, r: int) -> bool:
        whereas l 

Output:

Verdict: The response from GLM-5.1 exhibits environment friendly efficiency mixed with technical validity. It exhibits competence in executing intensive numerical operations via handbook digit processing. The system achieves its design objectives via its iterative methodology, which mixes efficiency with appropriate output. The implementation exists in a brief format and supplies restricted documentation via primary error dealing with. This makes the code applicable for algorithm improvement however unsuitable for manufacturing utilization as a result of that surroundings requires clear, extendable, and powerful efficiency.

General Evaluation of GLM-5.1 Capabilities

GLM-5.1 supplies a number of purposes via its open-source infrastructure and its subtle system design. This permits builders to create deep reasoning capabilities, code era features, and power utilization techniques. The system maintains all current GLM household strengths via sparse MoE and lengthy context capabilities. It additionally introduces new features that enable for adaptive considering and debugging loop execution. By means of its open weights and low-cost API choices, the system gives entry to analysis whereas supporting sensible purposes in software program engineering and different fields.

Conclusion

The GLM-5.1 is a reside instance of how present AI techniques develop their effectivity and scalability, whereas additionally bettering their reasoning capabilities. It ensures a excessive efficiency with its Combination-of-Consultants structure, whereas sustaining an affordable operational price. General, this method allows the dealing with of precise AI purposes that require intensive operations.

As AI heads in the direction of agent-based techniques and prolonged contextual understanding, GLM-5.1 establishes a base for future improvement. Its routing system and a focus mechanism, along with its multi-token prediction system, create new prospects for upcoming massive language fashions.