Phi-4-mini vs o1-mini: Which is the Better SLM?

The evolution of AI models has reached new heights, particularly in small language models (SLMs), where efficiency and performance are key. Among the latest contenders, Phi-4-mini and o1-mini stand out as advanced and efficient models. In this article, we’ll be doing a Phi-4 mini vs o1-mini comparison to check out their user experience, speed, and performance on STEM applications and coding tasks. We will be assessing their strengths in programming, debugging, and overall efficiency to see which model performs better. By the end, you’ll have a clear perspective on which model aligns with your needs.

What is Phi-4-mini?

Phi-4-mini is a state-of-the-art SLM designed for high-performance reasoning and coding tasks. It strikes a balance between efficiency and accuracy, making it a strong contender in AI-driven applications. The model is designed for high-accuracy text generation and complex reasoning tasks while being computationally efficient, making it well-suited for edge computing environments

Architecture Overview

Phi-4-mini is a dense, decoder-only transformer model with 3.8 billion parameters and a 128K token context window. It supports a vocabulary size of 200,064 tokens and incorporates Grouped Query Attention (GQA) to optimize resource efficiency while maintaining high performance.

Grouped Query Attention (GQA) is an efficient attention mechanism that balances the speed of multi-query attention (MQA) with the quality of multi-head attention (MHA) by grouping query heads and sharing key/value heads, improving inference speed for language models

Key Features

• Shared Input-Output Embeddings: Reduces memory overhead by reusing embeddings for both input and output.
• Training Data: Trained on 5 trillion tokens, including high-quality educational material, coding examples, and synthetic data tailored for reasoning.
• Performance: Excels in reasoning, mathematics, coding, and instruction-following, with the ability to integrate external APIs through function calling.

Also Read: Phi-4 vs GPT-4o-mini Face-Off

What is o1-mini?

o1-mini is a lightweight and cost-efficient SLM aimed at balancing affordability and performance. It prioritizes efficient processing while maintaining a reasonable level of accuracy for general AI applications.

Architecture Overview

o1-mini follows a standard transformer architecture, with fewer parameters than Phi-4-mini (exact size undisclosed). It also supports a 128K token context window but focuses on cost-effective processing rather than architectural optimizations like GQA.

Also Read: OpenAI’s o1-preview vs o1-mini: A Step Forward to AGI

Model Comparison: Phi-4-mini vs o1-mini

Phi-4-mini is a powerful model designed for tasks like reasoning, math, and coding, while o1-mini follows a simpler design focused on cost-effective coding. The table below highlights their key differences:

Phi-4-mini stands out with advanced features like GQA and shared embeddings, making it superior in reasoning, coding, and API integration. In contrast, o1-mini is a lighter, cost-effective alternative optimized for coding, though it lacks the architectural refinements seen in Phi-4-mini. Choosing between the two depends on whether the priority is high accuracy and reasoning power or affordability and efficiency in specific tasks.

Evaluation of Reasoning Performance

This section looks at how the Phi-4-mini and o3-mini models perform in reasoning compared to their larger models. It focuses on how well they solve complex problems and make logical conclusions, highlighting the differences in accuracy, efficiency, and clarity between the smaller and larger models.

Phi-4-mini and o1-mini vs Larger Models

The reasoning capabilities of the reasoning-enhanced Phi-4-mini and o1-mini were evaluated across multiple benchmarks, including AIME 2024, MATH-500, and GPQA Diamond. These benchmarks assess advanced mathematical reasoning and general problem-solving skills, providing a basis for comparison against several larger models from DeepSeek, Bespoke, and OpenThinker.

Despite having only 3.8 billion parameters, the reasoning-trained Phi-4-mini demonstrates strong performance, surpassing larger models such as:

• DeepSeek-R1-Distill-Llama-8B (8B parameters)
• Bespoke-Stratos-7B (7B parameters)
• OpenThinker-7B (7B parameters)

Additionally, it achieves performance comparable to DeepSeek-R1-Distill-Qwen-7B, a significantly larger 7B model, further highlighting its efficiency. However, o1-mini, despite its undisclosed parameter size, leads across several benchmarks, making it a strong contender in AI reasoning tasks.

Benchmark Comparisons

The performance of both models, as shown in the provided image, highlights their competitiveness against larger models:

• AIME Benchmark:
  • o1-mini scores 63.6, the highest among all models.
  • Phi-4-mini (reasoning trained) scores 50.0, a fivefold improvement over its base version (10.0).
• MATH-500 Benchmark:
  • Phi-4-mini (90.4) slightly outperforms o1-mini (90.0), making it highly effective for complex mathematical reasoning tasks.
• GPQA Diamond:
  • o1-mini leads with 60.0, showcasing superior general problem-solving capabilities.
  • Phi-4-mini (49.0) outperforms several 7B and 8B models, proving its efficiency in reasoning tasks.

These results indicate that o1-mini dominates in general problem-solving and reasoning, while Phi-4-mini (reasoning-trained) excels in mathematical benchmarks despite its smaller size (3.8B parameters). Both models demonstrate exceptional efficiency, challenging and even outperforming significantly larger models across key AI benchmarks.

Phi-4-mini vs o1-mini: Reasoning and Coding Capabilities

Now we will compare the reasoning and programming capabilities of Phi-4-mini and o1-mini. For that, we are going to give the same prompt to both models and evaluate their responses and we will be using API to load the model. Here are the tasks we’ll be trying out in this comparison:

1. Analyzing building order relationships
2. Logical reasoning in Math
3. Finding the Longest Substring

Task 1: Analyzing Building Order Relationships

This task requires the model to deduce the relative positions of buildings based on the given constraints and identify the middle building.

Prompt: “There are five buildings called V, W, X, Y and Z in a row (not necessarily in that order). V is to the West of W. Z is to the East of X and the West of V, W is to the West of Y. Which is the building in the middle?
Options:
A) V
B) W
C) X
D) Y”

Input to o1-mini

from openai import OpenAI
import time
import tiktoken
from IPython.display import display, Markdown
with open("path_to_api_key") as file:
   api_key = file.read().strip()


task1_start_time = time.time()
client = OpenAI(api_key=api_key)
messages = [
   {
       "role": "user",
       "content": """
   There are five buildings called V, W, X, Y and Z in a row (not necessarily in that order).
   V is to the West of W. Z is to the East of X and the West of V, W is to the West of Y.
   Which is the building in the middle?
   Options:
   A) V
   B) W
   C) X
   D) Y
"""
   }
]
completion = client.chat.completions.create(
   model="o1-mini-2024-09-12",
   messages=messages
)
task1_end_time = time.time()
# Print results
print(completion.choices[0].message)
print("----------------=Total Time Taken for task 1:----------------- ", task1_end_time - task1_start_time)
# Display result
from IPython.display import Markdown
display(Markdown(completion.choices[0].message.content))

Response by o1-mini

Input to Phi 4-mini

from transformers import pipeline
import time
from IPython.display import display, Markdown


from transformers import AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)


# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-4-mini-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-4-mini-instruct", trust_remote_code=True, quantization_config=quantization_config) 


task1_start_time = time.time()
messages = [
   {"role": "system", "content": "You are an expert in solving numerical and general reasoning questions."},
   {"role": "user", "content": """There are five buildings called V, W, X, Y and Z in a row (not necessarily in that order).
   V is to the West of W. Z is to the East of X and the West of V, W is to the West of Y.Which is the building in the middle? Options:
A) V
B) W
C) X
D) Y"""},
]


pipe = pipeline(
   "text-generation",
   model=model,
   tokenizer=tokenizer,
)
generation_args = {
   "max_new_tokens": 1024,
   "return_full_text": False,
   "temperature": 0.0,
   "do_sample": False,
}
output = pipe(messages, **generation_args) #,
task1_end_time = time.time()
print("----------------=Total Time Taken for task 1:----------------- ", task1_end_time - task1_start_time)
display(Markdown((output[0]['generated_text'])))
pipe = pipeline("text-generation", model="microsoft/Phi-4-mini-instruct", trust_remote_code=True)
pipe(messages)

Response by Phi 4-mini

Comparative Analysis

o1-mini is better than Phi-4-mini in both speed and accuracy for this task. o1-mini quickly figures out the correct answer (“V”) with just a few steps, while Phi-4-mini takes much longer because it goes through each detail step by step. Even with all that effort, Phi-4-mini still gets the wrong answer (“Z”), which isn’t even one of the choices. This shows that Phi-4-mini struggles with simple logic problems, while o1-mini handles them quickly and correctly. Phi-4-mini’s detailed thinking might be useful for harder problems, but in this case, it only caused delays and mistakes.

Task 2:  Logical Reasoning in Math

This task requires the model to recognize the pattern in a given number sequence and identify the missing number.

Prompt: “Select the number from among the given options that can replace the question mark (?) in the following series:16, 33, 100, 401, ?
Options:A) 1235
B) 804
C) 1588
D) 2006″

Input to o1-mini

task2_start_time = time.time()

client = OpenAI(api_key=api_key)

messages = [
   {
       "role": "user",
       "content": """Select the number from among the given options that can replace the question mark (?) in the following series.16, 33, 100, 401, ?
       A) 1235
       B) 804
       C) 1588
       D) 2006"""
   }
]


# Use a compatible encoding (cl100k_base is the best option for new OpenAI models)
encoding = tiktoken.get_encoding("cl100k_base")


# Calculate token counts
input_tokens = sum(len(encoding.encode(msg["content"])) for msg in messages)
completion = client.chat.completions.create(
   model="o1-mini-2024-09-12",
   messages=messages
)
output_tokens = len(encoding.encode(completion.choices[0].message.content))


task2_end_time = time.time()
# Print results
print(completion.choices[0].message)
print("----------------=Total Time Taken for task 2:----------------- ", task2_end_time - task2_start_time)
# Display result
from IPython.display import Markdown
display(Markdown(completion.choices[0].message.content))

Response by o1-mini

Input to Phi 4-mini

task2_start_time = time.time()
messages = [
   {"role": "system", "content": "You are an expert in solving numerical and general reasoning questions."},
   {"role": "user", "content": """Select the number from among the given options
   that can replace the question mark (?) in the following series.16, 33, 100, 401, ?
A) 1235
B) 804
C) 1588
D) 2006"""},
]
pipe = pipeline(
   "text-generation",
   model=model,
   tokenizer=tokenizer,
)
generation_args = {
   "max_new_tokens": 1024,
   "return_full_text": False,
   "temperature": 0.0,
   "do_sample": False,
}
output = pipe(messages, **generation_args) #,
task2_end_time = time.time()
print("----------------=Total Time Taken for task 2:----------------- ", task2_end_time - task2_start_time)
display(Markdown((output[0]['generated_text'])))

Response by Phi 4-mini

Comparative Analysis

o1-mini performed better than Phi-4-mini in both speed and accuracy for this number pattern task. o1-mini quickly recognized the pattern and correctly chose 2006 in just 10.77 seconds. On the other hand, Phi-4-mini took much longer (50.25 seconds) and still got the wrong answer (120). Meanwhile, o1-mini followed a clear and direct approach, solving the problem correctly and efficiently. This shows that o1-mini is better at spotting number patterns quickly, while Phi-4-mini tends to overcomplicate simple problems, leading to mistakes and delays.

Task 3: Finding the Longest Substring

This problem asks you to find the length of the longest substring within a given string that doesn’t contain any repeating characters. For example, in the string “abcabcbb”, the longest substring without repeating characters would be “abc”, and its length is 3.

Prompt: “Given a string s, find the length of the longest substring without repeating characters.
Write a function lengthOfLongestSubstring(s: str) -> int that returns the length of the longest substring without repeating characters.”

Input to o1-mini

task3_start_time = time.time()
client = OpenAI(api_key=api_key)
messages = [
   {
       "role": "user",
       "content": """
Given a string s, find the length of the longest substring without repeating characters.


Write a function lengthOfLongestSubstring(s: str) -> int that returns the length of the longest substring without repeating characters.
"""
   }
]
# Use a compatible encoding (cl100k_base is the best option for new OpenAI models)
encoding = tiktoken.get_encoding("cl100k_base")
# Calculate token counts
input_tokens = sum(len(encoding.encode(msg["content"])) for msg in messages)
completion = client.chat.completions.create(
   model="o1-mini-2024-09-12",
   messages=messages
)
output_tokens = len(encoding.encode(completion.choices[0].message.content))
task3_end_time = time.time()
# Print results
print(completion.choices[0].message)
print("----------------=Total Time Taken for task 3:----------------- ", task3_end_time - task3_start_time)
# Display result
from IPython.display import Markdown
display(Markdown(completion.choices[0].message.content))

Response by o1-mini

def lengthOfLongestSubstring(s: str) -> int:
   """
   Returns the length of the longest substring without repeating characters in the string s.


   Parameters:
   s (str): The input string.


   Returns:
   int: The length of the longest substring without repeating characters.
   """
   # Dictionary to store the last positions of each character.
   char_map = {}
   # Initialize the left pointer of the window.
   left = 0
   # Initialize the maximum length.
   max_length = 0


   # Iterate over the string with the right pointer.
   for right, char in enumerate(s):
       # If the character is found in char_map and its last position is >= left pointer,
       # move the left pointer to one position right of its last occurrence to avoid duplicates.
       if char in char_map and char_map[char] >= left:
           left = char_map[char] + 1


       # Update/insert the character's latest index.
       char_map[char] = right


       # Calculate the length of the current window and update max_length if necessary.
       current_window_length = right - left + 1
       if current_window_length > max_length:
           max_length = current_window_length


       # Debug statements to trace the internal state (can be removed in production)
       # print(f"Current character: {char}, Left: {left}, Right: {right}, Current Window: '{s[left:right+1]}', Max Length: {max_length}")


   return max_length


# Example usage:
if __name__ == "__main__":
   test_strings = [
       "abcabcbb",
       "bbbbb",
       "pwwkew",
       "",
       "a",
       "dvdf"
   ]
   for s in test_strings:
       print(f"Longest substring without repeating characters in '{s}' is of length {lengthOfLongestSubstring(s)}.")

Output

Input to Phi 4-mini

from transformers import pipeline
messages = [
   {"role": "user",
    "content":"""Given a string s, find the length of the longest substring without repeating characters.
Write a function lengthOfLongestSubstring(s: str) -> int that returns the length of the longest substring without repeating characters."""},
]
pipe = pipeline("text-generation", model="microsoft/Phi-4-mini-instruct", trust_remote_code=True)
pipe(messages)

Response by Phi 4-mini

def lengthOfLongestSubstring(s: str) -> int:
   char_index_map = {}
   left = 0
   max_length = 0
   for right in range(len(s)):
       if s[right] in char_index_map and char_index_map[s[right]] >= left:
           left = char_index_map[s[right]] + 1
       char_index_map[s[right]] = right
       max_length = max(max_length, right - left + 1)


   return max_length
print("Longest substring without repeating characters in 'abcabcbb' is of length ",lengthOfLongestSubstring("abcabcbb"))
print("Longest substring without repeating characters in 'bbbbb' is of length  ",lengthOfLongestSubstring("bbbbb"))

Output

Comparative Analysis

Both o1-mini and Phi-4-mini used the sliding window method correctly, but o1-mini’s code was more organized and easier to understand. It included clear explanations, comments, test cases, and easy-to-read variable names. Phi-4-mini’s solution was shorter but lacked explanations and structure, making it harder to follow in bigger projects. o1-mini was also faster and produced a cleaner, more readable solution, while Phi-4-mini focused more on keeping the code brief.

Overall Comparative Analysis

Here’s the overall comparative analysis for all 3 tasks:

Conclusion

Overall, o1-mini excelled in structured reasoning, accuracy, and coding best practices, making it more suitable for complex problem-solving and maintainable code. While Phi-4-mini was faster, its exploratory approach occasionally led to inefficiencies or incorrect conclusions, especially in reasoning tasks. In coding, o1-mini provided well-documented and readable solutions, whereas Phi-4-mini prioritized brevity at the cost of clarity. If speed is the main concern, Phi-4-mini is a solid choice, but for precision, clarity, and structured problem-solving, o1-mini stands out as the better option.

Frequently Asked Questions

Vipin Vashisth

Hello! I'm Vipin, a passionate data science and machine learning enthusiast with a strong foundation in data analysis, machine learning algorithms, and programming. I have hands-on experience in building models, managing messy data, and solving real-world problems. My goal is to apply data-driven insights to create practical solutions that drive results. I'm eager to contribute my skills in a collaborative environment while continuing to learn and grow in the fields of Data Science, Machine Learning, and NLP.