This article provides a comprehensive and up-to-date comparison of GLM 4.5 and Qwen3 235B 2507, two of the most advanced open-source large language models available today. By breaking down their architectures, reasoning capabilities, efficiency, benchmark results, pricing, and usability, the article helps you:
- Understand the key differences between the models in technical design, performance, and deployment scenarios.
- Identify which model best fits your needs—whether you value long-context handling, cost efficiency, reasoning depth, or code generation abilities.
- GLM 4.5 VS Qwen3 235B 2507: Architecture Comparison
- GLM 4.5 VS Qwen3 235B 2507: Benchmark Comparison
- GLM 4.5 VS Qwen3 235B thinking 2507: Ability Comparison
- GLM 4.5 vs Qwen 3 235B 2507: Efficiency Comparison
- Best LLM for Complex Reasoning Tasks: GLM 4.5 or Qwen 3 235B 2507
- How to Access GLM 4.5 or Qwen 3 235B 2507?
- Third Platform Guide
GLM 4.5 VS Qwen3 235B 2507: Architecture Comparison
| Feature | Qwen3 235B A22B Instruct 2507 | GLM 4.5 |
|---|---|---|
| Model Size | 235B total parameters 22B active parameters | 355B total parameters, 32B active parameters |
| Open Source | Yes | Yes |
| Architecture | MoE (Mixture of Experts) | MoE (Mixture of Experts) |
| Context Length | 262,144 tokens | 128,000 tokens |
| Language Support | Multilingual | Chinese and English |
| Multimodal | Text to text | Text to text |
| Reasoning Modes | No “thinking mode” (no internal chain-of-thought or <think> blocks) | Supports both “thinking mode” and “non-thinking mode” |
| Improvement | Instruction-tuned for better instruction following Optimized for general text generation, reasoning, math, science, coding, and tool use Improved alignment with human preferences in open-ended and subjective tasks | MuonClip optimizer at unprecedented scale Novel optimization techniques for scaling stability Hybrid reasoning: thinking mode for complex reasoning and tool use non-thinking mode for instant answers |
How Does the Parameter Count (235B) Impact Qwen-3’s Performance?
The massive 235 billion parameter count endows Qwen 3 with an enormous knowledge base and a high capacity for nuanced understanding. The MoE architecture is the key to making this scale practical. By only activating about 22 billion parameters at a time, the model achieves the knowledge and reasoning capabilities associated with its large total size while having an inference cost closer to a much smaller dense model.This provides an excellent balance between performance quality and computational efficiency, allowing it to tackle complex problems without the prohibitive cost of a 235B dense model.
GLM 4.5 VS Qwen3 235B 2507: Benchmark Comparison
Qwen3 235B A22B Instruct 2507 demonstrates a more balanced and comprehensive performance. It excels not only in traditional areas such as knowledge, reasoning, coding, and mathematics, but also shows strong capabilities in long-context understanding and handling complex tasks. Although GLM 4.5 performs well overall, it falls noticeably behind Qwen3 in more challenging tasks like mathematics, instruction following, and long-context reasoning.
GLM 4.5 VS Qwen3 235B thinking 2507: Ability Comparison
Reasoning Capabilities
Qwen3 235B Thinking 2507 demonstrates slightly stronger reasoning capabilities than GLM 4.5, as seen in the reasoning benchmarks (71.0 vs 68.8). This means Qwen3 is particularly well-suited for tasks involving complex logical inference and problem-solving. However, GLM 4.5 offers a more balanced performance across agentic and coding tasks, making it a more versatile choice for broader use cases.
Generalization
- GLM 4.5 was designed to unify diverse capabilities without sacrificing performance in any single area, reflecting a strong emphasis on generalization. It was trained on 15 trillion tokens of general text plus 8 trillion tokens of specialized data, giving it a broad and deep knowledge base.
- Qwen3 235B Thinking 2507 also demonstrates strong generalization, with training data covering 36 trillion tokens in 119 languages. However, the development of specialized variants like the “Thinking” and “Coder” models suggests a strategy of optimizing for specific tasks, which may sometimes trade off some generality.
GLM 4.5 vs Qwen 3 235B 2507: Efficiency Comparison
Speed Comparsion
GLM 4.5 is slightly faster in output speed and has lower latency, especially with long input contexts. Qwen 3 235B 2507 is close in short contexts but slows down more as input size increases.
Price Comparsion on Novita AI
| Model | Context Length | Input Price (/M Tokens) | OUtput Price (/M Tokens) |
|---|---|---|---|
| Qwen3 235B A22B Thinking 2507 | 131,072 | $0.3 | $3.0 |
| GLM 4.5 | 131,072 | $0.6 | $2.2 |
GLM 4.5 offers better efficiency and is more suitable for tasks with large outputs or long context windows, especially when response time is critical.
Qwen3 235B A22B Thinking 2507 provides lower input costs, which can be attractive if your workload is prompt-heavy rather than output-heavy.
Best LLM for Complex Reasoning Tasks: GLM 4.5 or Qwen 3 235B 2507
Prompt: Make a Flappy Bird Game
| Dimension | Qwen 3 235B | GLM-4.5 |
|---|---|---|
| Usability | Paste-and-play, minimal dependencies, ideal for quick prototyping and testing | Well-structured, suitable for further extension or team development |
| Gameplay Fidelity | Highly faithful to the original, core mechanics are simple and clear | Highly faithful, with special attention to visuals and interactive details |
| Code Style | Modern frontend style, concise and clear, great for solo development | Educational/engineering style, modular and clear, ideal for teams/teaching |
| Visuals | Simple and practical, good for technical demos | Delicate and polished, suitable for presentations and portfolios |
| Extensibility | Strong, easy to integrate into more complex web projects | Strong, easy to package for business logic or feature expansion |
| User Experience | User-friendly interaction, highly usable | Refined interaction, more polished UI/UX |
Qwen 3 235B is better for scenarios that require minimalism, quick integration, and concise code—perfect for prototyping and learning. GLM 4.5 is better for scenarios that demand teaching, maintainability, and visual aesthetics—ideal for engineering or classroom use.
How to Access GLM 4.5 or Qwen 3 235B 2507?
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="session_UsudmdAIggvSInjIdO2HWaTCyXxTFOXDV8TH8UCPbA576Rs4AGqSA5ThNbelSDgdEGAWQcWXnAU2bHi5BueceA==",
)
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)
Third Platform Guide
Using CLI like Trae,Claude Code, Qwen Code
If you want to use Novita AI’s top models (like Qwen3-Coder, Kimi K2, DeepSeek R1， GLM 4.5) for AI coding assistance in your local environment or IDE, the process is simple: get your API Key, install the tool, configure environment variables, and start coding.
For detailed setup commands and examples, check the official tutorials:
- Trae : Step-by-Step Guide to Access AI Models in Your IDE
- Claude Code:How to Use Kimi-K2 in Claude Code on Windows, Mac, and Linux
- Qwen Code:How to Use OpenAI Compatible API in Qwen Code (60s Setup!)
Multi-Agent Workflows with OpenAI Agents SDK
Build advanced multi-agent systems by integrating Novita AI with the OpenAI Agents SDK:
- Plug-and-play: Use Novita AI’s LLMs in any OpenAI Agents workflow.
- Supports handoffs, routing, and tool use: Design agents that can delegate, triage, or run functions, all powered by Novita AI’s models.
- Python integration: Simply set the SDK endpoint to
https://api.novita.ai/v3/openaiand use your API key.
Connect API on Third-Party Platforms
- OpenAI-Compatible API: Enjoy hassle-free migration and integration with tools such as Cline and Cursor, designed for the OpenAI API standard.
- Hugging Face: Use Modeis in Spaces, pipelines, or with the Transformers library via Novita AI endpoints.
- Agent & Orchestration Frameworks: Easily connect Novita AI with partner platforms like Continue, AnythingLLM,LangChain, Dify and Langflow through official connectors and step-by-step integration guides.
GLM-4.5 and Qwen3 235B 2507 both represent state-of-the-art advancements in LLM technology, but each model excels in different areas:
In summary:
- Choose Qwen3 235B 2507 for tasks requiring vast context windows, multilingual interaction, and specialized “thinking” or “coder” variants.
- Choose GLM-4.5 for applications where efficiency, output cost, versatility, and advanced agentic or engineering use cases are paramount.
Frequently Asked Questions
Both use Mixture of Experts (MoE) architectures. Qwen3 235B has 235B parameters (22B active per inference), while GLM-4.5 has 355B (32B active). Qwen3 235B offers a longer context window (262,144 vs 128,000 tokens).
GLM-4.5 achieves superior results on SWE-bench Verified for complex reasoning relative to model size, but Qwen3 235B 2507 slightly leads on some reasoning benchmarks (e.g., 71.0 vs 68.8). GLM-4.5 supports both hybrid “thinking” and instant modes, giving it more flexibility in agentic workflows.
Both models are among the best for code generation and instruction following. Qwen3 235B 2507 is instruction-tuned for comprehensive performance, while GLM-4.5 offers robust support for tool use, agentic coding tasks, and balanced generalization.
Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing an affordable and reliable GPU cloud for building and scaling.
Recommend Reading
- Novita Kimi K2 API Support Function Calling Now!
- Why Kimi K2 VRAM Requirements Are a Challenge for Everyone？
- Access Kimi K2: Unlock Cheaper Claude Code and MCP Integration, and more！
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