o3 vs o4-mini vs Gemini 2.5 pro: The Ultimate Reasoning Battle

AI models keep getting smarter, but which one truly reasons under pressure? In this blog, we put o3, o4-mini, and Gemini 2.5 Pro through a series of intense challenges: physics puzzles, math problems, coding tasks, and real-world IQ tests. No hand-holding, no easy wins—just a raw test of thinking power. We’ll break down how each model performs in advanced reasoning across different domains. Whether you’re tracking the latest in AI or just want to know who comes out on top, this article has you covered.

What are o3 and o4-mini?

o3 and o4‑mini are OpenAI’s newest reasoning models, successors to o1 and o3‑mini that go beyond pattern matching by running a deeper, longer internal “chain of thought.” They can agentically invoke the full suite of ChatGPT tools and excel at STEM, coding, and logical deduction.

• o3: Flagship model with ~10× the compute of o1, capable of “thinking with images” for direct visual reasoning; ideal for in‑depth analytical tasks.
• o4‑mini: Compact, efficient counterpart optimized for speed and throughput; delivers strong math, coding, and vision performance at lower cost.

You can access both in ChatGPT and via the Responses API.

Key Features of o3 and o4-mini

Here are some of the key features of these advanced and powerful reasoning models:

• Agentic Behavior: They exhibit proactive problem-solving abilities, autonomously determining the best approach to complex tasks and executing multi-step solutions efficiently.​
• Advanced Tool Integration: The models seamlessly utilize tools like web browsing, code execution, and image generation to enhance their responses and effectively tackle complex queries.​
• Multimodal Reasoning: They can process and integrate visual information directly into their reasoning chain, which enables them to interpret and analyze images alongside textual data.​
• Advanced Visual Reasoning (“Thinking with Images”): The models can interpret complex visual inputs, such as diagrams, whiteboard sketches, or even blurry or low-quality photos. They can even manipulate these images (zoom, crop, rotate, enhance) as part of their reasoning process to extract relevant information.

What is Gemini 2.5 Pro?

Gemini 2.5 Pro is Google DeepMind’s latest AI model, designed to offer improved performance, efficiency, and capabilities over its predecessors. It is part of the Gemini 2.5 series and represents the Pro-tier version, which strikes a balance between power and cost efficiency for developers and businesses.

Key Features of Gemini 2.5 Pro

Gemini 2.5 Pro introduces several notable enhancements:​

• Multimodal Capabilities: The model supports various data types, including text, images, video, audio, and code repositories. It can thus handle a diverse range of inputs and outputs, making it a versatile tool across different domains.
• Advanced Reasoning System: At the core of Gemini 2.5 Pro is its sophisticated reasoning system, which enables the AI to analyze information before generating responses methodically. This deliberate approach allows for more accurate and contextually relevant outputs.
• Extended Context Window: It features an expanded context window of 1 million tokens. This allows it to process and understand larger volumes of information simultaneously.
• Enhanced Coding Performance: The model demonstrates significant improvements in coding tasks, offering developers more efficient and accurate code generation and assistance.
• Extended Knowledge Base: Compared to most other models, it is trained on more recent data, marking a cutoff in knowledge as of January 2025.

You can access Gemini 2.5 Pro via Google AI Studio or on the Gemini website (for Gemini Advanced subscribers).

o3 vs o4‑mini vs Gemini 2.5: Task Comparison Showdown

To see which model really shines across a spectrum of real‑world challenges, we put o3, o4‑mini, and Gemini 2.5 head‑to‑head on five very different tasks:

• Resonant Attenuation Reasoning: Computing the absorption coefficient, phase‑velocity ordering, and on‑resonance refractive index for a dispersive gaseous medium.
• Numerical Series Puzzle: Cracking a subtly growing sequence to pinpoint the missing term.
• LRU Cache Implementation: Designing a high‑performance, constant‑time Least Recently Used cache in code.
• Responsive Portfolio Webpage: Crafting a clean, mobile‑friendly personal site with semantic HTML and custom CSS.
• Multimodal Task Breakdown: Analyzing how each model would tackle an image‑based challenge.

Each test probes a different strength, in deep physics reasoning, pattern recognition, coding prowess, design fluency, and image‑context understanding; so you can see exactly where each model excels or falls short.

Task 1: Reasoning

Input prompt: Dispersive Gaseous Medium. A dilute gaseous medium is found to exhibit a single optical resonance at frequency \\( \omega_0 = 2\pi \cdot 10^{15} \\) Hz. The electric field of a plane wave at frequency \\( \omega_0 \\) propagating through this medium is attenuated by a factor of two over a distance of 10 meters. The frequency width of the absorption resonance is \\( \Delta \omega \\). (a) What is the absorption coefficient \\( \alpha \\) at resonance? (b) Arrange in ascending order the propagation velocities at frequencies \\( \omega_0, \omega_0 + \Delta \omega / 10 \\), and \\( \omega_0 – \Delta \omega / 10 \\). Show your reasoning. (c) If there were no other resonances in the medium, what are the approximate numerical values of the index of refraction and the propagation velocity on resonance?

o3 Output:  

o4-mini Output:

Gemini 2.5 Output:

Output Comparison

Final verdict:

All three models provide correct and coherent answers, but Gemini 2.5 stands out as the best overall performer. While O3 offers the most technical rigor, O4-mini excels in speed and clarity. Gemini 2.5 strikes the optimal balance between depth, conceptual clarity, and structured presentation. It not only delivers the correct results but also explains the underlying physics with intuitive reasoning, making it ideal for both understanding and verification.

Task 2: Numerical Reasoning

Input prompt: Select the number from among the given options that can replace the question mark (?) in the following series: 16, 33, 100, 401,?

• 1235
• 804
• 1588
• 2006

o3 Output:  

o4-mini Output:

Gemini 2.5 Output:

Output Comparison

Final Verdict:

All three models correctly identify the pattern and arrive at the correct answer, 2006. While Gemini 2.5 provides a detailed, step-by-step explanation and O3 explains the logic clearly, O4-mini stands out for its elegance and mathematical precision. By expressing the sequence as a general formula, an=an−1×n+1a_n = a_{n-1} \times n + 1an​=an−1​×n+1, O4-mini captures the underlying pattern in the most concise and structured way. This makes it ideal for learners who appreciate clarity and compact reasoning. O4-mini delivers the best overall explanation.

Task 3: Coding task

Input prompt: Design and implement a data structure for Least Recently Used (LRU) cache. Implement the class `LRUCache` with the following methods:

– `get(key: int) -> int`: Return the value of the key if it exists, otherwise return -1.

– `put(key: int, value: int) -> None`: Update the value of the key if it exists, or insert the key-value pair if it doesn’t. When the cache reaches its capacity, it should invalidate the item that has been used least recently.

The cache should operate in O(1) time complexity for both operations.

o3 Code: 

class Node:
    """Doubly‑linked‑list node holding a cache entry (key, value)."""
    __slots__ = ("key", "value", "prev", "next")

    def __init__(self, key: int, value: int):
        self.key   = key
        self.value = value
        self.prev  = None
        self.next  = None


class LRUCache:
    def __init__(self, capacity: int):
        if capacity <= 0:
            raise ValueError("Capacity must be positive")
        self.cap = capacity
        self.map: dict[int, Node] = {}

        # Dummy head/tail simplify edge‑case handling
        self.head = Node(0, 0)          # Most‑recently‑used sentinel
        self.tail = Node(0, 0)          # Least‑recently‑used sentinel
        self.head.next = self.tail
        self.tail.prev = self.head

    # ───────────────────────── Internal helpers ──────────────────────────
    def _add_to_front(self, node: Node) -> None:
        """Insert node right after head (MRU position)."""
        node.prev        = self.head
        node.next        = self.head.next
        self.head.next.prev = node
        self.head.next      = node

    def _remove(self, node: Node) -> None:
        """Unlink node from list."""
        node.prev.next = node.next
        node.next.prev = node.prev

    # ───────────────────────── Public API ────────────────────────────────
    def get(self, key: int) -> int:
        """Return value if present; else ‑1. Marks entry as recently used."""
        if key not in self.map:
            return -1
        node = self.map[key]
        self._remove(node)
        self._add_to_front(node)
        return node.value

    def put(self, key: int, value: int) -> None:
        """
        Insert/update (key,value).  
        If capacity exceeded, evict the least‑recently‑used entry.
        """
        if key in self.map:                        # Update existing
            node = self.map[key]
            node.value = value
            self._remove(node)
            self._add_to_front(node)
            return

        # Insert new key
        node = Node(key, value)
        self.map[key] = node
        self._add_to_front(node)

        # Evict if over capacity
        if len(self.map) > self.cap:
            lru = self.tail.prev                  # Real LRU node
            self._remove(lru)
            del self.map[lru.key]

o4-mini Code:

class Node:
    __slots__ = ('key', 'value', 'prev', 'next')
    def __init__(self, key: int = None, value: int = None):
        self.key = key
        self.value = value
        self.prev = None
        self.next = None

class LRUCache:
    def __init__(self, capacity: int):
        self.capacity = capacity
        self.cache = {}               # key → Node
        # Dummy head/tail
        self.head = Node()
        self.tail = Node()
        self.head.next = self.tail
        self.tail.prev = self.head

    def _remove(self, node: Node):
        """Disconnect node from list."""
        prev, nxt = node.prev, node.next
        prev.next = nxt
        nxt.prev = prev

    def _add_to_front(self, node: Node):
        """Insert node right after head."""
        node.next = self.head.next
        node.prev = self.head
        self.head.next.prev = node
        self.head.next = node

    def get(self, key: int) -> int:
        if key not in self.cache:
            return -1
        node = self.cache[key]
        # Move to front
        self._remove(node)
        self._add_to_front(node)
        return node.value

    def put(self, key: int, value: int) -> None:
        if key in self.cache:
            # Update existing node and move it to front
            node = self.cache[key]
            node.value = value
            self._remove(node)
            self._add_to_front(node)
        else:
            # Insert new node
            new_node = Node(key, value)
            self.cache[key] = new_node
            self._add_to_front(new_node)
            # Evict LRU if over capacity
            if len(self.cache) > self.capacity:
                # Tail.prev is LRU
                lru = self.tail.prev
                self._remove(lru)
                del self.cache[lru.key]

Gemini 2.5 Code:

import collections

class Node:
    """Node for the doubly linked list."""
    def __init__(self, key, value):
        self.key = key
        self.value = value
        self.prev = None
        self.next = None

class LRUCache:

    def __init__(self, capacity: int):
        if capacity <= 0:
            raise ValueError("Capacity must be a positive integer")
        self.capacity = capacity
        self.cache = {}  # Stores key -> Node mapping
        # Initialize sentinel nodes for the doubly linked list
        self.head = Node(0, 0) # Dummy head
        self.tail = Node(0, 0) # Dummy tail
        self.head.next = self.tail
        self.tail.prev = self.head

    def _remove_node(self, node: Node):
        """Removes an existing node from the linked list."""
        prev_node = node.prev
        next_node = node.next
        prev_node.next = next_node
        next_node.prev = prev_node
        # Optional: clear node pointers for garbage collection if needed
        # node.prev = None
        # node.next = None

    def _add_to_front(self, node: Node):
        """Adds a node right after the head (most recently used)."""
        node.prev = self.head
        node.next = self.head.next
        self.head.next.prev = node
        self.head.next = node

    def get(self, key: int) -> int:
        """
        Return the value of the key if it exists, otherwise return -1.
        Moves the accessed node to the front (most recently used).
        """
        if key in self.cache:
            node = self.cache[key]
            # Move accessed node to the front
            self._remove_node(node)
            self._add_to_front(node)
            return node.value
        else:
            return -1

    def put(self, key: int, value: int) -> None:
        """
        Update the value of the key if it exists, or insert the key-value pair.
        Moves the updated/inserted node to the front.
        Evicts the least recently used item if capacity is exceeded.
        """
        if key in self.cache:
            # Key exists, update value and move to front
            node = self.cache[key]
            node.value = value
            self._remove_node(node)
            self._add_to_front(node)
        else:
            # Key doesn't exist, insert new node
            new_node = Node(key, value)
            self.cache[key] = new_node
            self._add_to_front(new_node)

            # Check if capacity is exceeded
            if len(self.cache) > self.capacity:
                # Evict the least recently used item (node before tail)
                lru_node = self.tail.prev
                self._remove_node(lru_node)
                # Remove from cache as well
                del self.cache[lru_node.key]

# Example Usage:
try:
    cache = LRUCache(2)
    cache.put(1, 1)       # cache is {1=1}
    print(f"Cache after put(1, 1): {[(k, v.value) for k, v in cache.cache.items()]}")
    cache.put(2, 2)       # cache is {1=1, 2=2}
    print(f"Cache after put(2, 2): {[(k, v.value) for k, v in cache.cache.items()]}")
    print(f"get(1): {cache.get(1)}")       # returns 1, cache is {2=2, 1=1} (1 is now MRU)
    print(f"Cache after get(1): {[(k, v.value) for k, v in cache.cache.items()]}") # Order in dict might not reflect LRU order
    cache.put(3, 3)       # LRU key 2 was evicted, cache is {1=1, 3=3}
    print(f"Cache after put(3, 3): {[(k, v.value) for k, v in cache.cache.items()]}")
    print(f"get(2): {cache.get(2)}")       # returns -1 (not found)
    cache.put(4, 4)       # LRU key 1 was evicted, cache is {3=3, 4=4}
    print(f"Cache after put(4, 4): {[(k, v.value) for k, v in cache.cache.items()]}")
    print(f"get(1): {cache.get(1)}")       # returns -1 (not found)
    print(f"get(3): {cache.get(3)}")       # returns 3
    print(f"Cache after get(3): {[(k, v.value) for k, v in cache.cache.items()]}")
    print(f"get(4): {cache.get(4)}")       # returns 4
    print(f"Cache after get(4): {[(k, v.value) for k, v in cache.cache.items()]}")

except ValueError as e:
    print(f"Error: {e}")

Output Comparison

Final Verdict

o3 offers the best balance of readability, safety, design, and performance, making it the most suitable for production and long-term use.

Task 4: Webpage Creation

Input prompt: Design a responsive personal portfolio webpage using HTML and CSS. The page should include the following sections:

1. Header: Display the user’s name and a brief tagline.

2. About Me: A short paragraph describing the user’s background and skills.

3. Projects: Showcase at least three projects with titles, descriptions, and links.

4. Contact: Provide contact information or a contact form.

Ensure the design is clean and mobile-friendly. Use semantic HTML elements and include basic CSS styling to enhance the visual appeal. Avoid using external CSS frameworks; write custom CSS for styling.

Implement the webpage in a single HTML file with embedded CSS.

o3 Output:  

o4-mini Output:

Gemini 2.5 Output:

Output Comparison

Final Verdict:

It features a more polished UI, improved responsiveness, modern CSS techniques (such as variables and clamp), and a professional overall presentation. It’s production-ready and showcases a deeper mastery of CSS.

Task 5: Image analysis

Input Image:

Input prompt: Explain the image to me in simple words, around 60 words

o3 Output:  

o4-mini Output:

Gemini 2.5 Output:

Output Comparison

Final verdict:

The o3 output provides the best balance of clarity, completeness, and simplicity, making it ideal for a general audience. It explains the process of a rainbow clearly, without overwhelming the reader with excessive details, while still covering essential aspects like refraction, internal reflection, and how multiple drops create the rainbow effect. Its concise style makes it easy to digest and understand, making it the most effective choice for explaining the phenomenon of a rainbow.

Overall Review

O3 is the best overall performer across all dimensions. It strikes the perfect balance between being scientifically accurate and easy to understand. While Gemini 2.5 is ideal for very basic understanding and O4-mini for more technical readers, O3 fits best for a general audience and educational purposes, offering a complete and engaging explanation without being overly technical or oversimplified.

Benchmark Comparison 

To better understand the performance capabilities of cutting-edge AI models, let’s compare Gemini 2.5 Pro, o4-mini, and o3 across a range of standardized benchmarks. These benchmarks evaluate models across various competencies, ranging from advanced mathematics and physics to software engineering and complex reasoning.

Key takeaways

• Mathematical reasoning: o4‑mini leads on AIME 2024 (93.4%) and AIME 2025 (92.7%), slightly outperforming o3 and Gemini 2.5 Pro.
• Physics knowledge: Gemini 2.5 Pro scores highest on GPQA (84%), suggesting strong domain expertise in graduate‑level physics.
• Complex reasoning challenge: All models struggle on Humanity’s Last Exam (<21%), with o3 at 20.3% as the top performer.
• Software engineering: o3 achieves 69.1% on SWE-Bench, edging out o4‑mini (68.1%) and Gemini 2.5 Pro (63.8%).
• Multimodal tasks: o3 also tops MMMU (82.9%), though differences are marginal.

Interpretation & implications

These results highlight each model’s strengths: o4‑mini excels in structured math benchmarks, Gemini 2.5 Pro shines in specialized physics, and o3 demonstrates balanced capability in coding and multimodal understanding. The low scores on “Humanity’s Last Exam” reveal room for improvement in abstract reasoning tasks. ​

Conclusion

Ultimately, all three models, o3, o4‑mini, and Gemini 2.5 Pro, represent the cutting edge of AI reasoning, and each has different strengths. o3 stands out for its balanced prowess in software engineering, deep analytical tasks, and multimodal understanding, thanks to its image‑driven chain of thought and robust performance across benchmarks. o4‑mini, with its optimized design and lower latency, excels in structured mathematics and logic challenges, making it ideal for high‑throughput coding and quantitative analysis.

The Gemini 2.5 Pro’s massive context window and native support for text, images, audio, and video give it a clear advantage in graduate-level physics and large-scale, multimodal workflows. Choosing between them comes down to your specific needs (for example, analytical depth with o3, rapid mathematical precision with o4‑mini, or extensive multimodal reasoning at scale with Gemini 2.5 Pro), but in every case, these models are redefining what AI can accomplish.

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