{"id":84720,"date":"2025-02-12T22:24:20","date_gmt":"2025-02-12T22:24:20","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/can-o3-mini-replace-deepseek-r1-for-logical-reasoning\/"},"modified":"2025-02-12T22:24:20","modified_gmt":"2025-02-12T22:24:20","slug":"can-o3-mini-replace-deepseek-r1-for-logical-reasoning","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=84720","title":{"rendered":"Can o3-mini Replace DeepSeek-R1 for Logical Reasoning?"},"content":{"rendered":"


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AI-powered reasoning models are taking the world by storm in 2025! With the launch of DeepSeek-R1 and o3-mini<\/a>, we have seen unprecedented levels of logical reasoning capabilities in AI chatbots. In this article, we will access these models via their APIs and evaluate their logical reasoning skills to find out if o3-mini can replace DeepSeek-R1<\/a>. We will be comparing their performance on standard benchmarks as well as real-world applications like solving logical puzzles and even building a Tetris game! So buckle up and join the ride.<\/p>\n

DeepSeek-R1 vs o3-mini: Logical Reasoning Benchmarks<\/h2>\n

DeepSeek-R1 and o3-mini offer unique approaches to structured thinking and deduction, making them apt for various kinds of complex problem-solving tasks. Before we speak of their benchmark performance, let\u2019s first have a sneak peek at the architecture of these models.<\/p>\n

o3-mini is OpenAI\u2019s most advanced reasoning model. It uses a dense transformer architecture, processing each token with all model parameters for strong performance but high resource consumption. In contrast, DeepSeek\u2019s most logical model, R1, employs a Mixture-of-Experts (MoE) framework, activating only a subset of parameters per input for greater efficiency. This makes DeepSeek-R1 more scalable and computationally optimized while maintaining solid performance.<\/p>\n

Learn More: Is OpenAI\u2019s o3-mini Better Than DeepSeek-R1?<\/a><\/em><\/p>\n

Now what we need to see is how well these models perform in logical reasoning tasks. First, let\u2019s have a look at their performance in the livebench benchmark tests.<\/p>\n

\"o3-mini<\/figure>\n

Sources: livebench.ai<\/a><\/p>\n

The benchmark results show that OpenAI\u2019s o3-mini outperforms DeepSeek-R1 in almost all aspects, except for math. With a global average score of 73.94 compared to DeepSeek\u2019s 71.38, the o3-mini demonstrates slightly stronger overall performance. It particularly excels in reasoning, achieving 89.58 versus DeepSeek\u2019s 83.17, reflecting superior analytical and problem-solving capabilities.<\/p>\n

Also Read: Google Gemini 2.0 Pro vs DeepSeek-R1: Who Does Coding Better?<\/a><\/em><\/p>\n

DeepSeek-R1 vs o3-mini: API Pricing Comparison<\/h2>\n

Since we are testing these models through their APIs, let\u2019s see how much these models cost.<\/p>\n

\n\n\n\n\n\n\n\n
Model<\/td>\nContext length<\/td>\nInput Price<\/td>\nCached Input Price<\/td>\nOutput Price<\/td>\n<\/tr>\n
o3-mini<\/td>\n200k<\/td>\n$1.10\/M tokens<\/td>\n$0.55\/M tokens<\/td>\n$4.40\/M tokens<\/td>\n<\/tr>\n
deepseek-chat<\/td>\n64k<\/td>\n$0.27\/M tokens<\/td>\n$0.07\/M tokens<\/td>\n$1.10\/M tokens<\/td>\n<\/tr>\n
deepseek-reasoner<\/td>\n64k<\/td>\n$0.55\/M tokens<\/td>\n$0.14\/M tokens<\/td>\n$2.19\/M tokens<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

As seen in the table, OpenAI\u2019s o3-mini is nearly twice as expensive as DeepSeek R1 in terms of API costs. It charges $1.10 per million tokens for input and $4.40 for output, whereas DeepSeek R1 offers a more cost-effective rate of $0.55 per million tokens for input and $2.19 for output, making it a more budget-friendly option for large-scale applications.<\/p>\n

Sources: DeepSeek-R1<\/a> | o3-mini<\/a><\/p>\n

How to Access DeepSeek-R1 and o3-mini via API<\/h2>\n

Before we step into the hands-on performance comparison, let\u2019s learn how to access DeepSeek-R1 and o3-mini using APIs. <\/p>\n

All you have to do for this, is import the necessary libraries and api keys:<\/p>\n

from openai import OpenAI\nfrom IPython.display import display, Markdown\nimport time<\/code><\/pre>\n
with open(\"path_of_api_key\") as file:\n   openai_api_key = file.read().strip()<\/code><\/pre>\n
with open(\"path_of_api_key\") as file:\n   deepseek_api = file.read().strip()<\/code><\/pre>\n
DeepSeek-R1 vs o3-mini: Logical Reasoning Comparison<\/h2>\n
Now that we\u2019ve gotten the API access, let\u2019s compare DeepSeek-R1 and o3-mini based on their logical reasoning capabilities. For this, we will give the same prompt to both the models and evaluate their responses based on these metrics:<\/p>\n
    \n
  1. Time taken by the model to generate the response,<\/li>\n
  2. Quality of the generated response, and<\/li>\n
  3. Cost incurred to generate the response.<\/li>\n<\/ol>\n
    We will then score the models 0 or 1 for each task, depending on their performance. So let\u2019s try out the tasks and see who emerges as the winner in the DeepSeek-R1 vs o3-mini reasoning battle!<\/p>\n
    Task 1: Building a Tetris Game<\/h3>\n
    This task requires the model to implement a fully functional Tetris game using Python, efficiently managing game logic, piece movement, collision detection, and rendering without relying on external game engines.<\/p>\n
    Prompt:<\/strong> \u201cWrite a python code for this problem: generate a Python code for the Tetris game\u201c<\/em><\/p>\n
    Input to DeepSeek-R1 API<\/strong><\/p>\n
    INPUT_COST_CACHE_HIT = 0.14 \/ 1_000_000  # $0.14 per 1M tokens\nINPUT_COST_CACHE_MISS = 0.55 \/ 1_000_000  # $0.55 per 1M tokens\nOUTPUT_COST = 2.19 \/ 1_000_000  # $2.19 per 1M tokens\n\n# Start timing\ntask1_start_time = time.time()\n\n# Initialize OpenAI client for DeepSeek API\nclient = OpenAI(api_key=api_key, base_url=\"https:\/\/api.deepseek.com\")\n\nmessages = [\n    {\n        \"role\": \"system\",\n        \"content\": \"\"\"You are a professional Programmer with a large experience.\"\"\"\n    },\n    {\n        \"role\": \"user\",\n        \"content\": \"\"\"write a python code for this problem: generate a python code for Tetris game.\"\"\"\n    }\n]\n\n# Get token count using tiktoken (adjust model name if necessary)\nencoding = tiktoken.get_encoding(\"cl100k_base\")  # Use a compatible tokenizer\ninput_tokens = sum(len(encoding.encode(msg[\"content\"])) for msg in messages)\n\n# Call DeepSeek API\nresponse = client.chat.completions.create(\n    model=\"deepseek-reasoner\",\n    messages=messages,\n    stream=False\n)\n\n# Get output token count\noutput_tokens = len(encoding.encode(response.choices[0].message.content))\n\ntask1_end_time = time.time()\n\ntotal_time_taken = task1_end_time - task1_start_time\n\n# Assume cache miss for worst-case pricing (adjust if cache info is available)\ninput_cost = (input_tokens \/ 1_000_000) * INPUT_COST_CACHE_MISS\noutput_cost = (output_tokens \/ 1_000_000) * OUTPUT_COST\n\ntotal_cost = input_cost + output_cost\n\n# Print results\nprint(\"Response:\", response.choices[0].message.content)\nprint(\"------------------ Total Time Taken for Task 1: ------------------\", total_time_taken)\nprint(f\"Input Tokens: {input_tokens}, Output Tokens: {output_tokens}\")\nprint(f\"Estimated Cost: ${total_cost:.6f}\")\n\n# Display result\nfrom IPython.display import Markdown\ndisplay(Markdown(response.choices[0].message.content))<\/code><\/pre>\n
    Response by DeepSeek-R1<\/strong><\/p>\n
    \"DeepSeek-R1<\/figure>\n
    You can find DeepSeek-R1\u2019s complete response here<\/a>.<\/p>\n
    Output token cost:<\/strong><\/p>\n
    Input Tokens: 28 | Output Tokens: 3323 | Estimated Cost: $0.0073<\/p>\n
    Code Output<\/strong><\/p>\n