Kimi K1.5 AI Model by Moonshot AI Outperforms OpenAI o1

This week, a relatively under-the-radar AI model has made waves in the artificial intelligence community. Developed by a startup called Moonshot AI, the Kimi K1.5 model has outperformed OpenAI's o1 model in various benchmarks.

What makes this model particularly interesting is its multimodal capabilities, which allow it to process not just text but also images and videos. In this article, I’ll break down the key features, performance, and implications of this new AI model.

---

What is Kimi K1.5?

A Multimodal AI Model

Kimi K1.5 is a top-tier multimodal AI model developed by Moonshot AI. Unlike some other models, such as DeepSeek R1, which is limited to processing text, Kimi K1.5 has vision capabilities. This means it can analyze and interpret images and videos, making it a versatile tool for a wide range of applications.

Here’s a quick breakdown of its benchmark scores:

• MMLU Benchmark: Kimi K1.5 scored 77.5, compared to OpenAI’s 74.1.
• Math Benchmark: It achieved 96.2, while OpenAI scored 94.8.
• Vision Benchmark (MathVista): Kimi K1.5 scored 74.9, outperforming OpenAI’s 71.

Training Methodology

One of the standout features of Kimi K1.5 is its training process. The model was trained using reinforcement learning, a method that doesn’t rely on more complex techniques like Monte Carlo research, value functions, or process reward models.

This approach seems to be gaining traction, as both Moonshot AI and DeepSeek released models trained with reinforcement learning in the same week. This could signal a shift in how AI models are developed in the future.

---

Kimi K1.5 Model Overview:

Feature	Details
Model Name	Kimi K1.5
Paper	arxiv.org/abs/2501.12599
GitHub Repository	github.com/MoonshotAI/Kimi-k1.5
Organization	github.com/MoonshotAI

---

Performance Benchmarks

Kimi K1.5 vs. OpenAI o1

When comparing Kimi K1.5 to OpenAI’s o1 model, the results are impressive. Across multiple benchmarks, Kimi K1.5 either outperforms OpenAI o1 or matches its performance. This is a significant achievement, considering OpenAI’s reputation in the AI space.

Here’s a quick breakdown of the benchmarks:

• Text Processing: Kimi K1.5 performs on par with OpenAI o1.
• Vision Capabilities: Kimi K1.5 excels, thanks to its multimodal design.
• Overall Accuracy: In most tests, Kimi K1.5 beats OpenAI o1.

Kimi K1.5 vs. Other Models

Kimi K1.5 was also compared to other leading models, including GPT-4o and CLAUDE 3.5 Sonnet.

Model	Text Processing	Vision Capabilities	Overall Accuracy
Kimi K1.5	Excellent	Excellent	Top-tier
GPT-4o	Good	Limited	Good
CLAUDE 3.5 Sonnet	Good	Limited	Good

As you can see, Kimi K1.5 consistently outperforms these models, particularly in tasks that require vision capabilities.

---

How to Use Kimi K1.5 for Free?

Kimi K1.5 isn’t open-source, but it’s available for free on the Kimi AI platform. Here’s how you can access and use it:

Step 1: Access the Kimi AI Platform

1. Visit the Kimi AI website.
2. You’ll see a chat interface where you can interact with Kimi K1.5.

Step 2: Start Chatting

• Simply type your question or query into the chatbox.
• For example, I asked, “Where are Coldplay concerts in India?”
• Within seconds, the model started web searching, read 28 web pages, and provided detailed answers.

This level of speed and accuracy makes me think it’s time to say goodbye to traditional search engines like Google!

Step 3: Explore Advanced Features

Kimi K1.5 offers several advanced features that make it incredibly versatile:

• Web Search: The model automatically performs web searches and provides accurate, up-to-date information.
• File Uploads: You can upload up to 50 files, each up to 100 MB in size (totaling 5 GB). Supported file types include PDFs, docs, XLS, PPT, text files, and images.
• Chat History: Your chat history is saved, allowing you to revisit previous conversations.
• Browser Extension: A browser extension is available for easier access.
• Mobile App: You can scan a QR code to download the mobile application.

Step 4: Log In (Optional)

While you can use Kimi K1.5 without logging in, creating an account (using your mobile number) unlocks additional features like chat history and file uploads.

---

How to Test Kimi k1.5 via API?

1. Apply for Access:
   Fill out the test application form to request a test account. You'll receive an email with access details.

2. Install Required Library:
   Ensure you have the openai library installed:

   pip install openai

3. Call the API:
   Use the provided Python code to interact with Kimi k1.5:

   from openai import Client
   
   client = Client(
       api_key="YOUR_KIMI_KEY",
       base_url="https://api.moonshot.ai/v1",
   )
   
   messages = [
       {"role": "user", "content": "The lengths of the two legs of a right triangle are 3 cm and 4 cm. Find the hypotenuse."},
   ]
   
   stream = client.chat.completions.create(
       model="kimi-k1.5-preview",
       messages=messages,
       temperature=0.3,
       stream=True,
       max_tokens=8192,
   )
   
   for chunk in stream:
       if chunk.choices[0].delta.content:
           print(chunk.choices[0].delta.content, end="")

4. Run the Code:
   Replace YOUR_KIMI_KEY with your API key and execute the script to test the model.

5. Check Output:
   The model will stream the response, printing the length of the hypotenuse (e.g., 5 cm).

---

Key Features of Kimi K1.5

Multimodal Capabilities

The ability to process both text and visual data sets Kimi K1.5 apart from many other AI models. This makes it suitable for applications like:

• Image and Video Analysis: Identifying objects, scenes, and patterns in visual data.
• Content Moderation: Detecting inappropriate or harmful content in images and videos.
• Data Interpretation: Combining text and visual data for more comprehensive insights.

Reinforcement Learning

The use of reinforcement learning in training Kimi K1.5 is a notable departure from traditional methods. This approach allows the model to learn and improve through trial and error, leading to better performance over time.

---

Accessing Kimi K1.5

Availability:

Unlike DeepSeek R1, which is open-source, Kimi K1.5 is not open-source. This means you can only access it through Moonshot AI’s platform. To get access, you’ll need to fill out a test application form on their website.

GitHub Repository:

For those interested in diving deeper into the technical details, Moonshot AI has made a GitHub repository available at github.com/MoonshotAI/Kimi-k1.5

---

Implications for the AI Industry

A Shift Toward Reinforcement Learning

The fact that both Moonshot AI and DeepSeek released models trained with reinforcement learning in the same week suggests that this method could become the next big thing in AI development. It offers a more efficient way to train models, potentially leading to even better performance in the future.

Multimodal Models on the Rise

Kimi K1.5’s success highlights the growing importance of multimodal AI models. As the demand for more versatile AI tools increases, we can expect to see more models with capabilities similar to Kimi K1.5.

---

My Experience with Kimi K1.5

During my testing, I was blown away by how quickly and accurately Kimi K1.5 responded to my queries. For instance, when I asked about the differences between DeepSeek V3 and DeepSeek R1, it provided a detailed comparison within seconds.

The ability to upload files and ask questions about their content is another standout feature. Whether it’s a PDF, spreadsheet, or image, Kimi K1.5 can analyze and provide insights effortlessly.

---

Conclusion

The release of Kimi K1.5 by Moonshot AI is a significant development in the AI industry. Its multimodal capabilities and reinforcement learning training set it apart from many other models, and its performance in benchmarks is nothing short of impressive. While it’s not open-source, those interested in testing it can apply through Moonshot AI’s platform.