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GLM-4vsGPT-o1

Zhipu AI vs OpenAI — Side-by-side model comparison

GPT-o1 leads 4/5 categories

Head-to-Head Comparison

MetricGLM-4GPT-o1
Provider
Arena Rank
#3
Context Window
128K
200K
Input Pricing
Undisclosed/1M tokens
$15.00/1M tokens
Output Pricing
Undisclosed/1M tokens
$60.00/1M tokens
Parameters
Undisclosed
Undisclosed
Open Source
No
No
Best For
Chinese language tasks, reasoning, coding
Complex reasoning, math, science, coding
Release Date
Jan 16, 2024
Dec 17, 2024

GLM-4

GLM-4 is Zhipu AI's flagship multimodal model, one of the leading AI models developed in China. It supports text, image, and video understanding with strong performance on Chinese-language tasks while maintaining competitive English capabilities. GLM-4 powers Zhipu's ChatGLM assistant and is widely used across Chinese enterprises for customer service, content generation, and data analysis applications.

View Zhipu AI profile →

GPT-o1

GPT-o1 is OpenAI's first dedicated reasoning model, introducing the concept of 'thinking tokens' where the model reasons through problems step-by-step before generating a response. This approach significantly improves performance on complex mathematics, coding challenges, and scientific reasoning compared to standard language models. With a 200K token context window, o1 can process lengthy technical documents while applying deep reasoning. It excels on competition-level math problems, PhD-level science questions, and complex coding tasks that require careful logical thinking. While slower and more expensive than GPT-4o due to the reasoning overhead, o1 delivers substantially better results on tasks that benefit from deliberate, structured problem-solving rather than quick pattern matching.

View OpenAI profile →

Key Differences: GLM-4 vs GPT-o1

1

GPT-o1 supports a larger context window (200K), allowing it to process longer documents in a single request.

G

When to use GLM-4

  • +Your use case involves chinese language tasks, reasoning, coding
View full GLM-4 specs →
G

When to use GPT-o1

  • +You need to process long documents (200K context)
  • +Your use case involves complex reasoning, math, science, coding
View full GPT-o1 specs →

The Verdict

GPT-o1 wins our head-to-head comparison with 4 out of 5 category wins. It's the stronger choice for complex reasoning, math, science, coding, though GLM-4 holds an edge in chinese language tasks, reasoning, coding.

Last compared: March 2026 · Data sourced from public benchmarks and official pricing pages

Frequently Asked Questions

Which is better, GLM-4 or GPT-o1?
In our head-to-head comparison, GPT-o1 leads in 4 out of 5 categories (arena rank, context window, input pricing, output pricing, and parameters). GPT-o1 excels at complex reasoning, math, science, coding, while GLM-4 is better suited for chinese language tasks, reasoning, coding. The best choice depends on your specific requirements, budget, and use case.
How does GLM-4 pricing compare to GPT-o1?
GLM-4 charges Undisclosed per 1M input tokens and Undisclosed per 1M output tokens. GPT-o1 charges $15.00 per 1M input tokens and $60.00 per 1M output tokens. For high-volume production workloads, the pricing difference can significantly impact total cost of ownership.
What is the context window difference between GLM-4 and GPT-o1?
GLM-4 supports a 128K token context window, while GPT-o1 supports 200K tokens. GPT-o1 can process longer documents, codebases, and conversations in a single request. Context window size matters most for tasks involving long documents, large codebases, or extended conversations.
Can I use GLM-4 or GPT-o1 for free?
GLM-4 is a paid API model starting at Undisclosed per 1M input tokens. GPT-o1 is a paid API model starting at $15.00 per 1M input tokens.
Which model has better benchmarks, GLM-4 or GPT-o1?
GLM-4's arena rank is not yet available, while GPT-o1 holds rank #3. Note that benchmarks don't capture every use case — we recommend testing both models on your specific tasks.
Is GLM-4 or GPT-o1 better for coding?
GLM-4 is specifically optimized for coding tasks. GPT-o1 is specifically optimized for coding tasks. For coding specifically, arena rank and code-specific benchmarks are the best indicators of performance.