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zhipu

GLM-5.1

GLM-5.1 is Zhipu AI's flagship reasoning model, featuring a 202K context window and an autonomous 8-hour execution loop for complex agentic engineering.

ReasoningAgentic AIOpen WeightsCodingMultimodal
zhipu logozhipuGLM2026-04-08
Context
203Ktokens
Max Output
164Ktokens
Input Price
$1.40/ 1M
Output Price
$4.40/ 1M
Modality:TextImage
Capabilities:VisionToolsStreamingReasoning
Benchmarks
GPQA
86.2%
GPQA: Graduate-Level Science Q&A. A rigorous benchmark with 448 multiple-choice questions in biology, physics, and chemistry created by domain experts. PhD experts only achieve 65-74% accuracy, while non-experts score just 34% even with unlimited web access (hence 'Google-proof'). GLM-5.1 scored 86.2% on this benchmark.
HLE
31%
HLE: High-Level Expertise Reasoning. Tests a model's ability to demonstrate expert-level reasoning across specialized domains. Evaluates deep understanding of complex topics that require professional-level knowledge. GLM-5.1 scored 31% on this benchmark.
MMLU
89%
MMLU: Massive Multitask Language Understanding. A comprehensive benchmark with 16,000 multiple-choice questions across 57 academic subjects including math, philosophy, law, and medicine. Tests broad knowledge and reasoning capabilities. GLM-5.1 scored 89% on this benchmark.
MMLU Pro
89%
MMLU Pro: MMLU Professional Edition. An enhanced version of MMLU with 12,032 questions using a harder 10-option multiple choice format. Covers Math, Physics, Chemistry, Law, Engineering, Economics, Health, Psychology, Business, Biology, Philosophy, and Computer Science. GLM-5.1 scored 89% on this benchmark.
IFEval
73%
IFEval: Instruction Following Evaluation. Measures how well a model follows specific instructions and constraints. Tests the ability to adhere to formatting rules, length limits, and other explicit requirements. GLM-5.1 scored 73% on this benchmark.
AIME 2025
95.3%
AIME 2025: American Invitational Math Exam. Competition-level mathematics problems from the prestigious AIME exam designed for talented high school students. Tests advanced mathematical problem-solving requiring abstract reasoning, not just pattern matching. GLM-5.1 scored 95.3% on this benchmark.
MATH
80%
MATH: Mathematical Problem Solving. A comprehensive math benchmark testing problem-solving across algebra, geometry, calculus, and other mathematical domains. Requires multi-step reasoning and formal mathematical knowledge. GLM-5.1 scored 80% on this benchmark.
GSM8k
96%
GSM8k: Grade School Math 8K. 8,500 grade school-level math word problems requiring multi-step reasoning. Tests basic arithmetic and logical thinking through real-world scenarios like shopping or time calculations. GLM-5.1 scored 96% on this benchmark.
MGSM
90%
MGSM: Multilingual Grade School Math. The GSM8k benchmark translated into 10 languages including Spanish, French, German, Russian, Chinese, and Japanese. Tests mathematical reasoning across different languages. GLM-5.1 scored 90% on this benchmark.
MathVista
70%
MathVista: Mathematical Visual Reasoning. Tests the ability to solve math problems that involve visual elements like charts, graphs, geometry diagrams, and scientific figures. Combines visual understanding with mathematical reasoning. GLM-5.1 scored 70% on this benchmark.
SWE-Bench
58.4%
SWE-Bench: Software Engineering Benchmark. AI models attempt to resolve real GitHub issues in open-source Python projects with human verification. Tests practical software engineering skills on production codebases. Top models went from 4.4% in 2023 to over 70% in 2024. GLM-5.1 scored 58.4% on this benchmark.
HumanEval
94.6%
HumanEval: Python Programming Problems. 164 hand-written programming problems where models must generate correct Python function implementations. Each solution is verified against unit tests. Top models now achieve 90%+ accuracy. GLM-5.1 scored 94.6% on this benchmark.
LiveCodeBench
68%
LiveCodeBench: Live Coding Benchmark. Tests coding abilities on continuously updated, real-world programming challenges. Unlike static benchmarks, uses fresh problems to prevent data contamination and measure true coding skills. GLM-5.1 scored 68% on this benchmark.
MMMU
73%
MMMU: Multimodal Understanding. Massive Multi-discipline Multimodal Understanding benchmark testing vision-language models on college-level problems across 30 subjects requiring both image understanding and expert knowledge. GLM-5.1 scored 73% on this benchmark.
MMMU Pro
58%
MMMU Pro: MMMU Professional Edition. Enhanced version of MMMU with more challenging questions and stricter evaluation. Tests advanced multimodal reasoning at professional and expert levels. GLM-5.1 scored 58% on this benchmark.
ChartQA
89%
ChartQA: Chart Question Answering. Tests the ability to understand and reason about information presented in charts and graphs. Requires extracting data, comparing values, and performing calculations from visual data representations. GLM-5.1 scored 89% on this benchmark.
DocVQA
93%
DocVQA: Document Visual Q&A. Document Visual Question Answering benchmark testing the ability to extract and reason about information from document images including forms, reports, and scanned text. GLM-5.1 scored 93% on this benchmark.
Terminal-Bench
63.5%
Terminal-Bench: Terminal/CLI Tasks. Tests the ability to perform command-line operations, write shell scripts, and navigate terminal environments. Measures practical system administration and development workflow skills. GLM-5.1 scored 63.5% on this benchmark.
ARC-AGI
12%
ARC-AGI: Abstraction & Reasoning. Abstraction and Reasoning Corpus for AGI - tests fluid intelligence through novel pattern recognition puzzles. Each task requires discovering the underlying rule from examples, measuring general reasoning ability rather than memorization. GLM-5.1 scored 12% on this benchmark.

About GLM-5.1

Learn about GLM-5.1's capabilities, features, and how it can help you achieve better results.

GLM-5.1 is Zhipu AI's flagship foundation model designed for complex system engineering and long-horizon agentic tasks. Built on a Mixture-of-Experts (MoE) architecture with 744 billion parameters and 40 billion active per pass, it represents a significant leap in endurance and autonomous problem-solving. The model is specifically engineered to overcome the reasoning plateaus seen in earlier large language models, maintaining productivity and code quality over thousands of tool calls and hundreds of iterations. It identifies blockers, runs experiments, and adjusts its own strategy without human intervention.

Technically, GLM-5.1 excels as a primary reasoning engine in multi-agent systems. It handles high-level architectural decisions while delegating implementation to smaller models. It features a 202K context window supported by a dynamic sparse attention mechanism, ensuring coherence across massive codebases. The model is released as open weights under the MIT License, providing a viable local alternative to proprietary frontier models for tasks like database optimization, GPU kernel engineering, and full-stack web application development.

KernelBench Level 3 results show that GLM-5.1 maintains a significant speedup in agentic ML workloads over long turns compared to Claude Opus 4.6. This endurance allows developers to trigger an engineering task in the morning and receive a fully tested, deployed service by the end of the day. It handles the entire lifecycle of a bug fix, from reproducing the issue in a sandbox to submitting the final pull request.

GLM-5.1

Use Cases

Discover the different ways you can use GLM-5.1 to achieve great results.

Autonomous Software Engineering

It runs autonomously for 8+ hours to design, implement, and debug microservices without human guidance.

High-Performance Database Tuning

The model iteratively optimizes Rust-based vector search implementations over hundreds of rounds.

GPU Kernel Optimization

It analyzes reference implementations to produce faster GPU kernels that outperform default autotune compilers.

Multi-Agent Orchestration

It acts as a reasoning core that coordinates sub-tasks and tool-calls across a swarm of specialized smaller models.

Complex Terminal Tasks

It executes real-world terminal operations and multi-step system administration via agentic CLI tools.

Full-Stack Web Design

The model generates visually consistent UI layouts and backend logic for browser-based desktop environments.

Strengths

Limitations

8-Hour Iteration Horizon: Maintains productivity over thousands of tool calls without hitting the reasoning plateaus common in other models.
High Latency: The reasoning-heavy architecture results in significantly slower token generation compared to standard non-reasoning models.
SOTA Coding Performance: Achieves a 58.4 score on SWE-Bench Pro, outperforming proprietary models like GPT-5.4 and Claude Opus 4.6.
Extreme Resource Demands: The raw model requires 1.65TB of disk space; even quantized versions require 256GB of VRAM/system memory to run.
Open Weights Access: Released under the MIT License, enabling local deployment of frontier-level reasoning capabilities for enterprise use.
Prompt Sensitivity: Unlocking full agentic performance often requires extremely detailed 300+ line system prompts to guide the reasoning loop.
Large Context Coherence: Maintains stability and accuracy up to 202k tokens, which is critical for long-horizon agentic engineering tasks.
API Instability: Users report frequent 500 errors and rate-limiting during peak Beijing usage hours on the official Z.ai endpoint.

API Quick Start

zhipu/glm-5.1

View Documentation
zhipu SDK
import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.ZHIPU_API_KEY,
  baseURL: 'https://api.z.ai/api/paas/v4'
});

const chat = await client.chat.completions.create({
  model: 'glm-5.1',
  messages: [{ role: 'user', content: 'Optimize this database schema.' }],
  stream: true
});

for await (const chunk of chat) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

Install the SDK and start making API calls in minutes.

Community Feedback

See what the community thinks about GLM-5.1

GLM-5.1 looped on one prompt for 8 straight hours. It didn't quit like most models do; it kept adding features and self-reviewing.
ziwenxu_
twitter
I've soak-tested it to 140k context no less than 5 times and it's remained coherent. SOTA might have a challenger.
Sensitive_Song4219
reddit
GLM-5.1 is basically neck-and-neck with Opus on this benchmark. It's now the #1 open model in the Arena.
tmuxvim
hackernews
Every time I see an NPC get genuinely convinced through unscripted dialogue with GLM-5.1, it's pure magic.
orblabs
reddit
The coding performance is legitimate. It fixed a race condition in our Go backend that GPT-4o kept hallucinating about.
DevScale_AI
twitter
Running this locally with Unsloth is a game changer for data privacy in our legal tech stack.
LawyerWhoCodes
reddit

Related Videos

Watch tutorials, reviews, and discussions about GLM-5.1

GLM-5.1 got 45.3% on this benchmark, which is a substantial jump for the family.

It's an incredibly slow model... they probably have more of their GPUs still serving GLM-5.

The way it handles tool calls is much more robust than the standard GLM 5.

It's currently the strongest reasoning model you can download and run on your own hardware.

You can see it actually identifying its own mistakes in the thinking log.

It can run autonomously for 8 hours, refining strategies through thousands of iterations.

It outperforms Gemini 3.1 Pro and Qwen 3.6 Plus on popular repo-generation benchmarks.

The agentic mode is where this model truly shines, it doesn't give up on hard bugs.

Z.ai has basically dropped the paywall on a frontier-level 744B parameter model.

It effectively manages the 'plateau' problem where other LLMs lose focus over time.

80% size reduction from the original 1.65 TB down to 236GB while maintaining quality.

The power of open source: even in a quantized version, it wrote working code for fireworks.

You'll need at least 256GB of system RAM to even think about loading this MoE giant.

It utilizes a dynamic sparse attention mechanism to keep that 202k context coherent.

Using Unsloth makes the training and inference process significantly more efficient.

More than just prompts
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Pro Tips

Expert tips to help you get the most out of GLM-5.1 and achieve better results.

Toggle Thinking Mode

Ensure the 'Thinking' toggle is enabled in your configuration to unlock the 8-hour autonomous iteration capabilities.

Use Off-Peak Quotas

Run large engineering batches during off-peak hours outside 14:00-18:00 Beijing Time for better pricing.

Local Memory Requirements

Use Unsloth Dynamic GGUF quantization to fit the 1.6TB model into 256GB of system memory for local runs.

Strategic Task Selection

Reserve GLM-5.1 for architectural reasoning and use GLM-4.7 for routine implementations to manage costs.

Testimonials

What Our Users Say

Join thousands of satisfied users who have transformed their workflow

Jonathan Kogan

Jonathan Kogan

Co-Founder/CEO, rpatools.io

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Sarah Chen

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Emily Rodriguez

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Jonathan Kogan

Jonathan Kogan

Co-Founder/CEO, rpatools.io

Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.

Mohammed Ibrahim

Mohammed Ibrahim

CEO, qannas.pro

I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!

Ben Bressington

Ben Bressington

CTO, AiChatSolutions

Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!

Sarah Chen

Sarah Chen

Head of Growth, ScaleUp Labs

We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.

David Park

David Park

Founder, DataDriven.io

The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!

Emily Rodriguez

Emily Rodriguez

Marketing Director, GrowthMetrics

Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.

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Frequently Asked Questions

Find answers to common questions about GLM-5.1