OpenAI was the first to introduce reasoning models like o1 and o1-mini, but is it the only player in the game? Not by a long shot! Chinese LLMs like DeepSeek, Qwen, and now Kimi are stepping up to challenge OpenAI by delivering similar capabilities at much more affordable prices. After DeepSeek’s impressive debut, it’s Kimi AI’s turn to shine with its new Kimi k1.5 model. In this article, we will be testing Kimi k1.5 against OpenAI o1 on the same tasks and see which one is better!

Table of contents

• What is Kimi k1.5?
• Kimi k1.5 vs OpenAI o1 and o1-mini
• Task 1: Zebra Puzzle
• Task 2: Logical Reasoning
• Task 3: Create an HTML Site
• Task 4: Web Search
• Task 5: Image Analysis
• Final Result: Kimi k1.5 vs OpenAI o1
• Conclusion

Please Note: Please note: Kimi k1.5 is free, while accessing ChatGPT Plus for o1 and o1-mini costs $20. Before diving into the tasks, let’s compare the two models.

What is Kimi k1.5?

Kimi k1.5 is a multi-modal LLM by Moonshot AI that is trained with reinforcement learning (RL) and designed to excel in various reasoning tasks across text, vision, and coding. Launched recently, Kimi K1.5 has quickly gained attention for its impressive performance, matching the capabilities of OpenAI’s o1 model without the preview or mini suffixes.

Key Features

• Completely FREE with unlimited usage
• Real-time web search across 100 websites
• Analyze up to 50 files (PDFs, Docs, PPTs, Images) with ease
• Advanced CoT Reasoning, available at no cost
• Enhanced image understanding, going beyond basic text extraction

Also Read:

• OpenAI o1: OpenAI’s New Model Series
• o1-mini: A Game-Changing Model for STEM and Reasoning

Kimi k1.5 vs OpenAI o1 and o1-mini

Kimi k1.5 matches OpenAI’s o1 and o1-mini in long-CoT tasks and outperforms GPT-4o and Claude Sonnet 3.5 in short-CoT tasks. Its multi-modal capabilities, particularly in visual reasoning, position it as a strong competitor to OpenAI’s models. The use of RL training techniques, multi-modal data recipes, and infrastructure optimization has been pivotal in achieving these results.

K1.5 Long-CoT Model: Advancing Multi-Modal Reasoning

The Kimi k1.5 long-CoT model achieves state-of-the-art (SOTA) performance through a combination of long-context scaling, improved policy optimization methods, and vision-text reinforcement learning (RL). Unlike traditional language model pretraining, which relies on next-token prediction and is limited by available training data, Kimi k1.5 leverages RL to scale its training data by learning to explore with rewards. This approach establishes a simplistic yet effective RL framework, avoiding complex techniques like Monte Carlo tree search or value functions.

Key benchmarks highlight the model’s exceptional performance:

• Mathematical Reasoning: Scores of 96.2 on MATH-500 and 77.5 on AIME 2024, matching OpenAI’s o1 and o1-mini models.
• Programming: 94th percentile on Codeforces, excelling in competitive programming.
• Visual Reasoning: 74.9 on MathVista, showcasing strong multi-modal integration.

The model’s ability to handle long-context tasks like planning, reflection, and correction is enhanced by partial rollouts during training, improving efficiency and performance.

K1.5 Short-CoT Model: Efficiency and Performance

The short-CoT model builds on the success of the long-CoT model, using effective long2short methods to distill long-CoT techniques into a more efficient framework. This approach combines fine-tuning, reinforcement learning, and long-to-short distillation, delivering rapid and accurate reasoning for short-context tasks.

Notable achievements include:

• Mathematical Reasoning: Scores of 94.6 on MATH-500 and 60.8 on AIME 2024, outperforming existing short-CoT models like GPT-4o and Claude Sonnet 3.5 by up to 550%.
• Programming: 47.3 on LiveCodeBench, excelling in code generation and debugging tasks.
• Natural Language Understanding: Effective for real-time applications like chatbots and virtual assistants.

The long-to-short distillation techniques, including model merging and shortest rejection sampling, ensure high performance with reduced computational costs, making the short-CoT model highly efficient.

To know more you can checkout their research paper here!

Now, let’s evaluate Kimi k1.5 and OpenAI o1 on the same prompts!

Task 1: Zebra Puzzle

Prompt: Solve this zebra puzzle correctly.

OpenAI o1 Response:

Collector Position	Nationality	Lot	Amount Spent	Shirt Color
Collector #1	Portuguese	Toy	Unknown	Blue
Collector #2	Japanese	Lot 40	0,000	Purple
Collector #3	Italian	Lot 30	0,000	Pink
Collector #4	Canadian	Unknown	0,000	Yellow
Collector #5	Unknown	Unknown	0,000	Unknown

Kimi k1.5 Response:

Collector #	Nationality	Lot	Item	Amount	Shirt Color
1	Italian	30	Clock	0,000	Blue
2	Japanese	40	Painting	0,000	Purple
3	Canadian	10	Vase	0,000	Pink
4	Portuguese	34	Toy	0,000	Yellow
5	(Unknown)

Observation:

The response provided by OpenAI o1 is significantly inaccurate. The resulting table does not adhere to the required format specified in the question, making the response unreliable. On the other hand, Kimi k1.5 provided a response in the correct format as per the question, but the content of the answer itself is incorrect.

Verdict:

OpenAI o1: ? | Kimi k1.5: ?

Task 2: Logical Reasoning

Prompt: You walk into a room and see a bed. On the bed there are two dogs, four cats, a giraffe, five cows, and a duck. There are also three chairs and a table. How many legs are on the floor?

OpenAI o1 Response:

Kimi k1.5 Response:

Observation:

Correct Answer:22 (2 (human) 4 (table) 4 (bed) 12 (3 chairs))

OpenAI o1 took too long to generate the response. While the calculations were correct and included a detailed explanation, it failed to account for the human legs on the floor, making its answer incorrect. In contrast,Kimi k1.5provided the correct answer in less time.

Verdict:

OpenAI o1: ? | Kimi k1.5: ?

Task 3: Create an HTML Site

Prompt: Create a simple HTML page with a button that changes the background color to Pink when clicked with some confetti.

OpenAI o1 Response:

Kimi k1.5 Response:

Observation:

Both the models adhered to the prompt and provided correct responses. In terms of look and functionality of the webpage, Kimi k1.5 is doing a much better job!

Verdict:

OpenAI o1: ? | Kimi k1.5: ?

Task 4: Web Search

Prompt: Read the article – https://www.analyticsvidhya.com/blog/2024/07/building-agentic-rag-systems-with-langgraph/ to understand the process of building a vector database for Wikipedia data. Summarize the key steps in a concise manner.

OpenAI o1 Response:

Key Points to Summarize the Process of Building a Vector Database for Wikipedia Data:

1. Collect Data: Obtain Wikipedia articles from dumps or APIs.
2. Preprocess Text: Clean the text and split it into smaller, coherent “chunks.”
3. Select an Embedding Model: Use a language model (e.g., OpenAI embeddings) to convert text chunks into numerical vectors.
4. Generate Embeddings: For each chunk, produce its semantic vector representation.
5. Set Up a Vector Database: Choose a vector store (e.g., Pinecone, Weaviate, FAISS, Chroma) to store and index the embeddings.
6. Insert Vectors: Load all the chunk vectors and associated metadata (e.g., original article references) into the database.
7. Test Retrieval: Query the database with a vectorized question to ensure relevant Wikipedia chunks are returned.
8. Optimize and Integrate: Use indexing and integration with a retrieval-augmented generation (RAG) pipeline for efficient and accurate information retrieval.

Kimi k1.5 Response:

The article “A Comprehensive Guide to Building Agentic RAG Systems with LangGraph” outlines the process of building a vector database for Wikipedia data as part of creating an Agentic RAG system. Here are the key steps summarized:

1. Install Dependencies: Install necessary libraries such as langchain, langchain-openai, langchain-community, langgraph, and langchain-chroma.
2. Enter API Keys: Securely input API keys for OpenAI and Tavily Search using the getpass function to avoid exposing them in the code.
3. Setup Environment Variables: Set environment variables for the OpenAI and Tavily Search API keys.
4. Load Wikipedia Data:
   • Download Wikipedia data from a provided Google Drive link or use a local archive file.
   • Extract and load the data into a list of documents.
5. Chunk Documents:
   • Use a text splitter to divide the documents into smaller, manageable chunks.
   • The article uses RecursiveCharacterTextSplitter to split the documents into chunks of approximately 2000 characters with an overlap of 300 characters.
6. Create Vector Database:
   • Initialize a Chroma vector database client.
   • Use OpenAI’s embedding model (text-embedding-3-small) to convert document chunks into embeddings.
   • Store the document chunks and their embeddings in the vector database index, specifying cosine similarity as the distance function.
7. Setup Vector Database Retriever:
   • Configure a retriever to fetch the top 3 most similar documents based on cosine similarity.
   • Set a similarity threshold (e.g., 0.3) to filter out documents that are not sufficiently similar to the query.
8. Test the Retriever:
   • Test the retriever with sample queries to ensure it returns relevant documents.
   • For queries without relevant documents in the vector database, the retriever should return an empty list.

Observation:

Kimi k1.5’s output excels by providing detailed, actionable implementation steps with specific technical parameters and library requirements. It balances comprehensive depth with clear organization, explains the reasoning behind each step, and effectively connects the technical process to the broader goal of building an Agentic RAG system.

Verdict:

OpenAI o1: ? | Kimi k1.5: ?

Task 5: Image Analysis

Prompt: The image shows a highly simplified schematic of a datacenter in space, with some simplifying assumptions that need justification.

Tasks:
Estimate the lower bound of the required radiator surface area for the datacenter.
Address how to handle the Sun and deep space in the context of thermal management.
Explain how the First Law of Thermodynamics applies to this scenario.

Input Image:

OpenAI o1 Response:

Below is a step-by-step reasoning process with clearly stated simplifying assumptions, justifications, and the application of the First Law of Thermodynamics. The final result will be a lower bound on the radiator area required to dissipate 1 GW of heat generated by a space-based data center.

Key Assumptions and Simplifications

1. Power Input and Heat Generation:

Assume the data center is powered by a 1 GW solar power source. Nearly all of the electrical power consumed by GPUs will be converted into heat (electrical-to-thermal energy conversion is essentially 100% at steady state since all electrical energy not stored must eventually dissipate as heat). Thus, the waste heat to be removed is approximately 1 GW = 1×10^9 W.

2. Radiative Heat Transfer Only:

In space, there is no convective or conductive cooling to the environment—only radiative heat transfer. Therefore, the primary mode of heat rejection is thermal radiation from a radiator surface into deep space.

3. Emissivity and Absorptivity:

Assume the radiator is constructed with a high-emissivity surface, sayε=0.9ε=0.9. This is plausible for surfaces coated with certain high-emissivity paints or materials. For a lower bound on required area, we assume ideal conditions:

• The radiator primarily faces away from the Sun to minimize solar absorptance.

Kimi k1.5 Response:

To estimate the lower bound of the required radiator surface area for the data center in space, I start by applying the Stefan-Boltzmann Law, which describes the power radiated by a surface:

To handle the Sun and deep space in thermal management, the data center should be shielded from direct sunlight using a sunshade or positioned in a location where it is not exposed to the Sun, such as the dark side of a planet or the Moon. The radiator must be designed to efficiently radiate heat into the vacuum of space, which is extremely cold.

The First Law of Thermodynamics applies to this scenario by ensuring that the heat generated by the data center’s equipment is equal to the heat radiated away by the radiator. This law guarantees that the total energy in the system remains constant, with the heat generated being fully accounted for in the radiator’s design.

The lower bound of the required radiator surface area is approximately 766 square meters. The data center should be shielded from direct sunlight and the radiator should be optimized for heat dissipation in the vacuum of space to maintain thermal equilibrium.

Observation:

Kimi k1.5’s response is superior for several reasons. It demonstrates clear mathematical reasoning through a step-by-step approach, starting with the fundamental Stefan-Boltzmann Law equation. Kimi clearly defines all variables and their values, shows the mathematical process of solving for the radiator area, and provides a concrete numerical result of 766 square meters. The explanation includes clear justifications for thermal management strategies, practical considerations such as positioning the radiator on the dark side of a planet, and a direct connection to the First Law of Thermodynamics with real-world application. The response concludes with specific numbers and actionable recommendations.

In contrast, OpenAI o1’s response remains more theoretical, focusing on general assumptions and setup rather than completing the actual calculation. It lacks a concrete numerical solution and does not fully address the thermal management aspect, making it less practical and actionable compared to Kimi k1.5’s detailed and solution-oriented approach.

Verdict:

OpenAI o1: ? | Kimi k1.5: ?

Final Result: Kimi k1.5 vs OpenAI o1

Task	Winner
Zebra Puzzle	Neither
Logical Reasoning	Kimi k1.5
Create an HTML Site	Kimi k1.5
Web Search	Kimi k1.5
Image Analysis	Kimi k1.5

Also Read: Kimi k1.5 vs DeepSeek R1: Battle of the Best Chinese LLMs

Conclusion

Free models like Kimi k1.5 and DeepSeek R1 are challenging OpenAI o1’s dominance, offering superior performance in reasoning, coding, and multi-modal tasks at no cost. With Kimi k1.5 outperforming OpenAI in key benchmarks and DeepSeek R1 excelling in coding challenges, is paying $20/month for OpenAI o1 still justified? Let us know in the comment section below!

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