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

AI fashions preserve getting smarter, however which one really causes beneath stress? On this weblog, we put o3, o4-mini, and Gemini 2.5 Professional by way of a sequence of intense challenges: physics puzzles, math issues, coding duties, and real-world IQ assessments. No hand-holding, no straightforward wins—only a uncooked take a look at of considering energy. We’ll break down how every mannequin performs in superior reasoning throughout totally different domains. Whether or not you’re monitoring the most recent in AI or simply need to know who comes out on high, this text has you coated.

What are o3 and o4-mini?

o3 and o4‑mini are OpenAI’s latest reasoning fashions, successors to o1 and o3‑mini that transcend sample matching by operating a deeper, longer inside “chain of thought.” They will agentically invoke the complete suite of ChatGPT instruments and excel at STEM, coding, and logical deduction.

• o3: Flagship mannequin with ~10× the compute of o1, able to “considering with photographs” for direct visible reasoning; perfect for in‑depth analytical duties.
• o4‑mini: Compact, environment friendly counterpart optimized for pace and throughput; delivers sturdy math, coding, and imaginative and prescient efficiency at decrease value.

You’ll be able to entry each in ChatGPT and by way of the Responses API.

Key Options of o3 and o4-mini

Listed here are a few of the key options of those superior and highly effective reasoning fashions:

• Agentic Habits: They exhibit proactive problem-solving talents, autonomously figuring out the most effective strategy to advanced duties and executing multi-step options effectively.​
• Superior Device Integration: The fashions seamlessly make the most of instruments like net searching, code execution, and picture era to boost their responses and successfully deal with advanced queries.​
• Multimodal Reasoning: They will course of and combine visible info instantly into their reasoning chain, which allows them to interpret and analyze photographs alongside textual information.​
• Superior Visible Reasoning (“Pondering with Photos”): The fashions can interpret advanced visible inputs, corresponding to diagrams, whiteboard sketches, and even blurry or low-quality images. They will even manipulate these photographs (zoom, crop, rotate, improve) as a part of their reasoning course of to extract related info.

What’s Gemini 2.5 Professional?

Gemini 2.5 Professional is Google DeepMind’s newest AI mannequin, designed to supply improved efficiency, effectivity, and capabilities over its predecessors. It’s a part of the Gemini 2.5 sequence and represents the Professional-tier model, which strikes a stability between energy and price effectivity for builders and companies.

Key Options of Gemini 2.5 Professional

Gemini 2.5 Professional introduces a number of notable enhancements:​

• Multimodal Capabilities: The mannequin helps numerous information varieties, together with textual content, photographs, video, audio, and code repositories. It may possibly thus deal with a various vary of inputs and outputs, making it a flexible device throughout totally different domains.
• Superior Reasoning System: On the core of Gemini 2.5 Professional is its refined reasoning system, which allows the AI to investigate info earlier than producing responses methodically. This deliberate strategy permits for extra correct and contextually related outputs.
• Prolonged Context Window: It options an expanded context window of 1 million tokens. This permits it to course of and perceive bigger volumes of knowledge concurrently.
• Enhanced Coding Efficiency: The mannequin demonstrates vital enhancements in coding duties, providing builders extra environment friendly and correct code era and help.
• Prolonged Data Base: In comparison with most different fashions, it’s skilled on more moderen information, marking a cutoff in information as of January 2025.

You’ll be able to entry Gemini 2.5 Professional by way of Google AI Studio or on the Gemini web site (for Gemini Superior subscribers).

o3 vs o4‑mini vs Gemini 2.5: Job Comparability Showdown

To see which mannequin actually shines throughout a spectrum of actual‑world challenges, we put o3, o4‑mini, and Gemini 2.5 head‑to‑head on 5 very totally different duties:

• Resonant Attenuation Reasoning: Computing the absorption coefficient, section‑velocity ordering, and on‑resonance refractive index for a dispersive gaseous medium.
• Numerical Sequence Puzzle: Cracking a subtly rising sequence to pinpoint the lacking time period.
• LRU Cache Implementation: Designing a excessive‑efficiency, fixed‑time Least Lately Used cache in code.
• Responsive Portfolio Webpage: Crafting a clear, cellular‑pleasant private web site with semantic HTML and customized CSS.
• Multimodal Job Breakdown: Analyzing how every mannequin would deal with a picture‑based mostly problem.

Every take a look at probes a special energy, in deep physics reasoning, sample recognition, coding prowess, design fluency, and picture‑context understanding; so you’ll be able to see precisely the place every mannequin excels or falls brief.

Job 1: Reasoning

Enter immediate: Dispersive Gaseous Medium. A dilute gaseous medium is discovered to exhibit a single optical resonance at frequency ( omega_0 = 2pi cdot 10^{15} ) Hz. The electrical discipline of a aircraft wave at frequency ( omega_0 ) propagating by way of this medium is attenuated by an element of two over a distance of 10 meters. The frequency width of the absorption resonance is ( Delta omega ). (a) What’s the absorption coefficient ( alpha ) at resonance? (b) Organize in ascending order the propagation velocities at frequencies ( omega_0, omega_0 + Delta omega / 10 ), and ( omega_0 – Delta omega / 10 ). Present your reasoning. (c) If there have been no different resonances within 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 Comparability

Last verdict:

All three fashions present right and coherent solutions, however Gemini 2.5 stands out as the most effective total performer. Whereas O3 affords probably the most technical rigor, O4-mini excels in pace and readability. Gemini 2.5 strikes the optimum stability between depth, conceptual readability, and structured presentation. It not solely delivers the right outcomes but additionally explains the underlying physics with intuitive reasoning, making it perfect for each understanding and verification.

Job 2: Numerical Reasoning

Enter immediate: Choose the quantity from among the many given choices that may exchange the query mark (?) within the following sequence: 16, 33, 100, 401,?

o3 Output:  

o4-mini Output:

Gemini 2.5 Output:

Output Comparability

Last Verdict:

All three fashions accurately determine the sample and arrive on the right reply, 2006. Whereas Gemini 2.5 gives an in depth, step-by-step clarification and O3 explains the logic clearly, O4-mini stands out for its class and mathematical precision. By expressing the sequence as a basic formulation, an=an−1×n+1a_n = a_{n-1} instances n + 1an​=an−1​×n+1, O4-mini captures the underlying sample in probably the most concise and structured means. This makes it perfect for learners who respect readability and compact reasoning. O4-mini delivers the most effective total clarification.

Job 3: Coding job

Enter immediate: Design and implement an information construction for Least Lately Used (LRU) cache. Implement the category `LRUCache` with the next strategies:

– `get(key: int) -> int`: Return the worth of the important thing if it exists, in any other case return -1.

– `put(key: int, worth: int) -> None`: Replace the worth of the important thing if it exists, or insert the key-value pair if it doesn’t. When the cache reaches its capability, it ought to invalidate the merchandise that has been used least not too long ago.

The cache ought to function in O(1) time complexity for each operations.

o3 Code: 

class Node:
    """Doubly‑linked‑listing node holding a cache entry (key, worth)."""
    __slots__ = ("key", "worth", "prev", "subsequent")

    def __init__(self, key: int, worth: int):
        self.key   = key
        self.worth = worth
        self.prev  = None
        self.subsequent  = None


class LRUCache:
    def __init__(self, capability: int):
        if capability <= 0:
            increase ValueError("Capability have to be constructive")
        self.cap = capability
        self.map: dict[int, Node] = {}

        # Dummy head/tail simplify edge‑case dealing with
        self.head = Node(0, 0)          # Most‑not too long ago‑used sentinel
        self.tail = Node(0, 0)          # Least‑not too long ago‑used sentinel
        self.head.subsequent = self.tail
        self.tail.prev = self.head

    # ───────────────────────── Inner helpers ──────────────────────────
    def _add_to_front(self, node: Node) -> None:
        """Insert node proper after head (MRU place)."""
        node.prev        = self.head
        node.subsequent        = self.head.subsequent
        self.head.subsequent.prev = node
        self.head.subsequent      = node

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

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

    def put(self, key: int, worth: int) -> None:
        """
        Insert/replace (key,worth).  
        If capability exceeded, evict the least‑not too long ago‑used entry.
        """
        if key in self.map:                        # Replace present
            node = self.map[key]
            node.worth = worth
            self._remove(node)
            self._add_to_front(node)
            return

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

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

o4-mini Code:

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

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

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

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

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

    def put(self, key: int, worth: int) -> None:
        if key in self.cache:
            # Replace present node and transfer it to entrance
            node = self.cache[key]
            node.worth = worth
            self._remove(node)
            self._add_to_front(node)
        else:
            # Insert new node
            new_node = Node(key, worth)
            self.cache[key] = new_node
            self._add_to_front(new_node)
            # Evict LRU if over capability
            if len(self.cache) > self.capability:
                # 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 listing."""
    def __init__(self, key, worth):
        self.key = key
        self.worth = worth
        self.prev = None
        self.subsequent = None

class LRUCache:

    def __init__(self, capability: int):
        if capability <= 0:
            increase ValueError("Capability have to be a constructive integer")
        self.capability = capability
        self.cache = {}  # Shops key -> Node mapping
        # Initialize sentinel nodes for the doubly linked listing
        self.head = Node(0, 0) # Dummy head
        self.tail = Node(0, 0) # Dummy tail
        self.head.subsequent = self.tail
        self.tail.prev = self.head

    def _remove_node(self, node: Node):
        """Removes an present node from the linked listing."""
        prev_node = node.prev
        next_node = node.subsequent
        prev_node.subsequent = next_node
        next_node.prev = prev_node
        # Optionally available: clear node pointers for rubbish assortment if wanted
        # node.prev = None
        # node.subsequent = None

    def _add_to_front(self, node: Node):
        """Provides a node proper after the pinnacle (most not too long ago used)."""
        node.prev = self.head
        node.subsequent = self.head.subsequent
        self.head.subsequent.prev = node
        self.head.subsequent = node

    def get(self, key: int) -> int:
        """
        Return the worth of the important thing if it exists, in any other case return -1.
        Strikes the accessed node to the entrance (most not too long ago used).
        """
        if key in self.cache:
            node = self.cache[key]
            # Transfer accessed node to the entrance
            self._remove_node(node)
            self._add_to_front(node)
            return node.worth
        else:
            return -1

    def put(self, key: int, worth: int) -> None:
        """
        Replace the worth of the important thing if it exists, or insert the key-value pair.
        Strikes the up to date/inserted node to the entrance.
        Evicts the least not too long ago used merchandise if capability is exceeded.
        """
        if key in self.cache:
            # Key exists, replace worth and transfer to entrance
            node = self.cache[key]
            node.worth = worth
            self._remove_node(node)
            self._add_to_front(node)
        else:
            # Key would not exist, insert new node
            new_node = Node(key, worth)
            self.cache[key] = new_node
            self._add_to_front(new_node)

            # Test if capability is exceeded
            if len(self.cache) > self.capability:
                # Evict the least not too long ago used merchandise (node earlier than tail)
                lru_node = self.tail.prev
                self._remove_node(lru_node)
                # Take away from cache as nicely
                del self.cache[lru_node.key]

# Instance Utilization:
strive:
    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 may not replicate 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 discovered)
    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 discovered)
    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()]}")

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

Output Comparability

Last Verdict

o3 affords the most effective stability of readability, security, design, and efficiency, making it probably the most appropriate for manufacturing and long-term use.

Job 4: Webpage Creation

Enter immediate: Design a responsive private portfolio webpage utilizing HTML and CSS. The web page ought to embody the next sections:

1. Header: Show the consumer’s title and a quick tagline.

2. About Me: A brief paragraph describing the consumer’s background and abilities.

3. Initiatives: Showcase at the least three tasks with titles, descriptions, and hyperlinks.

4. Contact: Present contact info or a contact kind.

Make sure the design is clear and mobile-friendly. Use semantic HTML components and embody fundamental CSS styling to boost the visible enchantment. Keep away from utilizing exterior CSS frameworks; write customized CSS for styling.

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

o3 Output:  

o4-mini Output:

Gemini 2.5 Output:

Output Comparability

Last Verdict:

It includes a extra polished UI, improved responsiveness, trendy CSS methods (corresponding to variables and clamp), and an expert total presentation. It’s production-ready and showcases a deeper mastery of CSS.

Job 5: Picture evaluation

Enter Picture:

Enter immediate: Clarify the picture to me in easy phrases, round 60 phrases

o3 Output:  

o4-mini Output:

Gemini 2.5 Output:

Output Comparability

Last verdict:

The o3 output gives the most effective stability of readability, completeness, and ease, making it perfect for a basic viewers. It explains the method of a rainbow clearly, with out overwhelming the reader with extreme particulars, whereas nonetheless masking important facets like refraction, inside reflection, and the way a number of drops create the rainbow impact. Its concise fashion makes it straightforward to digest and perceive, making it the simplest selection for explaining the phenomenon of a rainbow.

General Assessment

O3 is the most effective total performer throughout all dimensions. It strikes the right stability between being scientifically correct and straightforward to grasp. Whereas Gemini 2.5 is good for very fundamental understanding and O4-mini for extra technical readers, O3 matches greatest for a basic viewers and academic functions, providing an entire and interesting clarification with out being overly technical or oversimplified.

Benchmark Comparability 

To higher perceive the efficiency capabilities of cutting-edge AI fashions, let’s examine Gemini 2.5 Professional, o4-mini, and o3 throughout a variety of standardized benchmarks. These benchmarks consider fashions throughout numerous competencies, starting from superior arithmetic and physics to software program engineering and sophisticated reasoning.

Key takeaways

• Mathematical reasoning: o4‑mini leads on AIME 2024 (93.4%) and AIME 2025 (92.7%), barely outperforming o3 and Gemini 2.5 Professional.
• Physics information: Gemini 2.5 Professional scores highest on GPQA (84%), suggesting sturdy area experience in graduate‑stage physics.
• Complicated reasoning problem: All fashions wrestle on Humanity’s Final Examination (<21%), with o3 at 20.3% as the highest performer.
• Software program engineering: o3 achieves 69.1% on SWE-Bench, edging out o4‑mini (68.1%) and Gemini 2.5 Professional (63.8%).
• Multimodal duties: o3 additionally tops MMMU (82.9%), although variations are marginal.

Interpretation & implications

These outcomes spotlight every mannequin’s strengths: o4‑mini excels in structured math benchmarks, Gemini 2.5 Professional shines in specialised physics, and o3 demonstrates balanced functionality in coding and multimodal understanding. The low scores on “Humanity’s Final Examination” reveal room for enchancment in summary reasoning duties. ​

Conclusion

Finally, all three fashions, o3, o4‑mini, and Gemini 2.5 Professional, signify the slicing fringe of AI reasoning, and every has totally different strengths. o3 stands out for its balanced prowess in software program engineering, deep analytical duties, and multimodal understanding, because of its picture‑pushed chain of thought and sturdy efficiency throughout benchmarks. o4‑mini, with its optimized design and decrease latency, excels in structured arithmetic and logic challenges, making it perfect for prime‑throughput coding and quantitative evaluation.

The Gemini 2.5 Professional’s large context window and native help for textual content, photographs, audio, and video give it a transparent benefit in graduate-level physics and large-scale, multimodal workflows. Selecting between them comes all the way down to your particular wants (for instance, analytical depth with o3, speedy mathematical precision with o4‑mini, or intensive multimodal reasoning at scale with Gemini 2.5 Professional), however in each case, these fashions are redefining what AI can accomplish.

Steadily Requested Questions