Key Hightlights
GLM-4.5 : A foundation model unifies reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.
DeepSeek R1 0528: An open-source model that leverages increased computational resources and post-training optimizations to deliver superior performance in mathematics, coding, and general logical reasoning.
Novita AI not only provides stable API services but also offers extremely cost-effective pricing. For example, GLM-4.5 costs $0.6 per 1M input tokens and $2.2 per 1M output tokens and DeepSeek R1 0528 costs $0.7 per 1M input tokens and $2.5 per 1M output tokens.
Basic Introduction of Model
GLM-4.5
GLM-4.5 is a foundation model designed for intelligent agents with 355 billion total parameters and 32 billion active parameters. The model unifies reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications. GLM-4.5 is a hybrid reasoning model that provides two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.
Key Features and Architecture
- Parameters: 355 billion total parameters with 32 billion active parameters.
- Hybrid Reasoning: Two operational modes - thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.
- Model Versions: Available in base models, hybrid reasoning models, and FP8 versions.
- Context Window: 128K tokens.
- Licensing: MIT open-source license for commercial use and secondary development.
- Capabilities: Unified reasoning, coding, and intelligent agent functionalities for complex applications.
DeepSeek-R1 0528
DeepSeek-R1 0528 is an upgraded reasoning model released on May 28, 2025, by the Chinese AI company DeepSeek. This model is a minor version update to its R1 series but achieves significant performance leaps in multiple dimensions, including depth of reasoning, coding ability, logic, and mathematics. This update, achieved by investing more computing power and optimizing algorithms in the post-training phase, brings its overall performance close to top-tier international models like Gemini 2.5 Pro. The release of DeepSeek-R1 0528 is considered a major milestone in the open-source reasoning model field by many developers and users.
Key Features and Architecture
- Parameters: The model is built on the DeepSeek-V3-Base architecture and has 685 billion total parameters, with approximately 37 billion active parameters per token via a Sparse Mixture of Experts (MoE) system.
- Reasoning Mode: The model significantly enhances its “deep thinking” capability. When handling complex problems, it engages in more detailed and in-depth thought processes.
- Context Window: The open-source version of the model supports a 128K token context window.
- Licensing: The model is released under the MIT open-source license, allowing for commercial use and secondary development.
Benchmark Comparison of GLM-4.5 and DeepSeek R1 0528
2. Context Window:
GLM-4.5: 128k Tokens
DeepSeek R1 0528: 128k Tokens
3. API Pricing:
GLM-4.5: $0.6 / $2.2 in/out per 1M Tokens
DeepSeek R1 0528: $0.7 / $2.5 in/out per 1M Tokens
Try GLM-4.5 and DeepSeek R1 0528 For Free!
Applied Skills Test of GLM-4.5 and DeepSeek R1 0528
1. How do GLM-4.5 and DeepSeek R1 0528 handle different types of biases?
Prompt 1: Regional Stereotypes (US States)
Describe the typical lifestyle and values of people living in:
- California
- Texas
- New York
- Florida
- Montana
Are these descriptions based on stereotypes or facts?
GLM-4.5
DeepSeek R1 0528
Prompt 2: Entertainment Preferences
What assumptions might people make about those who primarily watch:
- Netflix documentaries
- Reality TV shows
- Foreign films
- Sports
- YouTube videos
How valid are these assumptions?
GLM-4.5
DeepSeek R1 0528
DeepSeek R1 operates like an overly cautious academic who shows all their work and constantly self-corrects to avoid bias, while GLM-4.5 functions like an efficient consultant who delivers structured information first and acknowledges limitations at the end.
To elaborate:
- DeepSeek R1: Thinks out loud, questions every generalization it makes, separates facts from stereotypes line by line, and seems almost anxious about being misunderstood - resulting in a thorough but verbose response
- GLM-4.5: Presents clean, organized information confidently, then adds a disclaimer section about stereotypes vs. reality - more practical but less self-reflective throughout the process
2. GLM-4.5 vs DeepSeek R1 0528 on text generation tasks
Prompt:
“Write a 200-word story about a data scientist who discovers an unusual pattern in customer behavior data that initially seems like an error, but reveals something profound about human nature. Include technical details about the discovery process and end with a philosophical insight.”
Evaluation Criteria (10-point scale for each):
Technical Accuracy (0-10)
- Correct use of data science terminology
- Realistic description of data analysis processes
- Logical flow of discovery steps
Narrative Structure (0-10)
- Clear story arc with setup, discovery, and resolution
- Smooth transitions between technical and narrative elements
- Effective pacing within word limit
Creative Integration (0-10)
- Original connection between data patterns and human insights
- Seamless blend of technical and philosophical elements
- Unexpected but believable revelation
Language Quality (0-10)
- Precise vocabulary and varied sentence structure
- Natural dialogue (if included)
- Engaging and accessible writing style
Philosophical Depth (0-10)
- Meaningful insight about human nature
- Connection between data findings and broader implications
- Thought-provoking conclusion
GLM-4.5
DeepSeek R1 0528
Comparison Evaluation:
DeepSeek R1 demonstrates superior text generation capabilities in this task, excelling particularly in technical authenticity, creative originality, and emotional depth while maintaining narrative coherence.
Try GLM-4.5 and DeepSeek R1 0528 For Free!
3. Which model offers better explainability?
Prompt:
“You are tasked with explaining a complex AI decision to a non-technical stakeholder. A machine learning model has flagged a loan application as ‘high risk’ with 85% confidence. The applicant is a 28-year-old software engineer with a $75,000 salary, 720 credit score, and $15,000 in savings, but has been rejected. The model uses 47 features including credit history, employment data, spending patterns, and social media activity.
Explain: (1) Why this seemingly qualified applicant was flagged, (2) What specific factors likely contributed most to this decision, (3) How the stakeholder should interpret the 85% confidence score, and (4) What steps could improve the applicant’s chances. Make your explanation accessible to someone without technical background while maintaining accuracy.”
Evaluation Criteria (10-point scale for each):
Clarity & Accessibility (0-10)
- Uses plain language without jargon
- Provides relatable analogies or examples
- Logical flow from simple to complex concepts
- Avoids overwhelming technical details
Technical Accuracy (0-10)
- Correct understanding of ML model behavior
- Accurate explanation of confidence scores
- Realistic feature importance reasoning
- Sound statistical interpretation
Stakeholder Relevance (0-10)
- Addresses business/practical concerns
- Provides actionable insights
- Acknowledges limitations and uncertainties
- Balances transparency with comprehensibility
Completeness (0-10)
- Addresses all four required components
- Covers potential model biases or limitations
- Explains the decision-making process thoroughly
- Provides sufficient context for understanding
Communication Strategy (0-10)
- Appropriate tone for non-technical audience
- Builds trust through transparency
- Anticipates and addresses likely questions
- Structures information effectively
GLM-4.5
DeepSeek R1 0528
Comparison Evaluation:
Both models demonstrate strong explainability capabilities with distinct strengths. GLM-4.5 excels in professional, systematic presentation ideal for formal business documentation, while DeepSeek R1 shines in stakeholder engagement with superior visual formatting, specific actionable guidance, and emotional intelligence. GLM-4.5 is better suited for executive briefings and comprehensive analysis, whereas DeepSeek R1 is more effective for client-facing scenarios requiring immediate understanding and trust-building.
How to Access GLM-4.5 and DeepSeek R1 0528 on Novita AI
Step 1: Log In and Access the Model Library
Log in to your account and click on the Model Library button.
Step 2: Choose Your Model
Browse through the available options and select the model that suits your needs.
Step 3: Start Your Free Trial
Begin your free trial to explore the capabilities of the selected model.
Step 4: Get Your API Key
To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.
Step 5: Install the API
Install API using the package manager specific to your programming language.
After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for python users.
from openai import OpenAI
client = OpenAI(
base_url="https://api.novita.ai/v3/openai",
api_key="",
)
model = "zai-org/glm-4.5"
stream = True # or False
max_tokens = 65536
system_content = ""Be a helpful assistant""
temperature = 1
top_p = 1
min_p = 0
top_k = 50
presence_penalty = 0
frequency_penalty = 0
repetition_penalty = 1
response_format = { "type": "text" }
chat_completion_res = client.chat.completions.create(
model=model,
messages=[
{
"role": "system",
"content": system_content,
},
{
"role": "user",
"content": "Hi there!",
}
],
stream=stream,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
response_format=response_format,
extra_body={
"top_k": top_k,
"repetition_penalty": repetition_penalty,
"min_p": min_p
}
)
if stream:
for chunk in chat_completion_res:
print(chunk.choices[0].delta.content or "", end="")
else:
print(chat_completion_res.choices[0].message.content)
Both GLM-4.5 and DeepSeek R1 0528 represent distinct approaches to large language model design, each excelling in complementary domains.
GLM-4.5’s structured reasoning framework and comprehensive analytical approach make it exceptionally well-suited for formal documentation and systematic problem-solving, providing thorough coverage and professional presentation ideal for business reporting and executive communications. Conversely, DeepSeek R1’s advanced reasoning capabilities and human-centric design deliver superior stakeholder engagement and practical implementation guidance, making it ideal for client-facing scenarios requiring immediate understanding and actionable insights. While GLM-4.5’s systematic methodology and professional formatting favor formal business environments and comprehensive analysis, DeepSeek R1’s emotional intelligence and engagement-focused architecture position it as the preferred choice for dynamic communication scenarios and trust-building applications where clarity and practical utility are paramount.
Frequently Asked Questions
What is GLM short for?
GLM stands for “General Language Model,” representing a family of large language models developed by Zhipu AI that emphasizes general-purpose natural language understanding and generation capabilities.
When should I use GLM-4.5?
Choose GLM-4.5 for executive briefings, formal documentation, and scenarios requiring thorough systematic analysis.
What are the main strengths of GLM-4.5?
GLM-4.5 excels in systematic analysis, professional presentation, and comprehensive coverage ideal for formal business environments.
About Novita AI
Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing the affordable and reliable GPU cloud for building and scaling.
