o3 vs o4-mini vs Gemini 2.5 professional: The Final Reasoning Battle

AI fashions maintain getting smarter, however which one really causes underneath strain? On this weblog, we put o3, o4-mini, and Gemini 2.5 Professional by means of a collection 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 pondering energy. We’ll break down how every mannequin performs in superior reasoning throughout totally different domains. Whether or not you’re monitoring the newest in AI or simply wish to know who comes out on prime, 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 working a deeper, longer inside “chain of thought.” They will agentically invoke the total suite of ChatGPT instruments and excel at STEM, coding, and logical deduction.

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

You possibly can entry each in ChatGPT and through the Responses API.

Key Options of o3 and o4-mini

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

• Agentic Conduct: They exhibit proactive problem-solving talents, autonomously figuring out the very best method to complicated duties and executing multi-step options effectively.​
• Superior Instrument Integration: The fashions seamlessly make the most of instruments like internet shopping, code execution, and picture era to reinforce their responses and successfully deal with complicated queries.​
• Multimodal Reasoning: They will course of and combine visible info immediately into their reasoning chain, which permits them to interpret and analyze photos alongside textual information.​
• Superior Visible Reasoning (“Considering with Pictures”): The fashions can interpret complicated visible inputs, resembling diagrams, whiteboard sketches, and even blurry or low-quality photographs. They will even manipulate these photos (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 collection and represents the Professional-tier model, which strikes a stability between energy and value 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, photos, video, audio, and code repositories. It may thus deal with a various vary of inputs and outputs, making it a flexible software throughout totally different domains.
• Superior Reasoning System: On the core of Gemini 2.5 Professional is its subtle reasoning system, which permits the AI to research info earlier than producing responses methodically. This deliberate method 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 data concurrently.
• Enhanced Coding Efficiency: The mannequin demonstrates important 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 newer information, marking a cutoff in information as of January 2025.

You possibly can entry Gemini 2.5 Professional through Google AI Studio or on the Gemini web site (for Gemini Superior subscribers).

o3 vs o4‑mini vs Gemini 2.5: Process 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 Just lately Used cache in code.
• Responsive Portfolio Webpage: Crafting a clear, cellular‑pleasant private website with semantic HTML and customized CSS.
• Multimodal Process Breakdown: Analyzing how every mannequin would deal with a picture‑primarily based problem.

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

Process 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 means 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

Closing verdict:

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

Process 2: Numerical Reasoning

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

o3 Output:  

o4-mini Output:

Gemini 2.5 Output:

Output Comparability

Closing Verdict:

All three fashions accurately determine the sample and arrive on the appropriate reply, 2006. Whereas Gemini 2.5 supplies an in depth, step-by-step clarification and O3 explains the logic clearly, O4-mini stands out for its magnificence and mathematical precision. By expressing the sequence as a normal system, an=an−1×n+1a_n = a_{n-1} instances n + 1an​=an−1​×n+1, O4-mini captures the underlying sample in essentially the most concise and structured manner. This makes it very best for learners who admire readability and compact reasoning. O4-mini delivers the very best general clarification.

Process 3: Coding job

Enter immediate: Design and implement an information construction for Least Just 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:
            elevate 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

    # ───────────────────────── Inside 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:
            elevate 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
        # Non-compulsory: 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 top (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 does 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 effectively
                del self.cache[lru_node.key]

# Instance Utilization:
attempt:
    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 won't mirror 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

Closing Verdict

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

Process 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 person’s identify and a quick tagline.

2. About Me: A brief paragraph describing the person’s background and expertise.

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

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

Make sure the design is clear and mobile-friendly. Use semantic HTML parts and embody primary CSS styling to reinforce the visible attraction. 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

Closing Verdict:

It contains a extra polished UI, improved responsiveness, trendy CSS methods (resembling variables and clamp), and knowledgeable general presentation. It’s production-ready and showcases a deeper mastery of CSS.

Process 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

Closing verdict:

The o3 output supplies the very best stability of readability, completeness, and ease, making it very best for a normal viewers. It explains the method of a rainbow clearly, with out overwhelming the reader with extreme particulars, whereas nonetheless protecting important points 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 best selection for explaining the phenomenon of a rainbow.

General Evaluate

O3 is the very best general performer throughout all dimensions. It strikes the right stability between being scientifically correct and straightforward to know. Whereas Gemini 2.5 is good for very primary understanding and O4-mini for extra technical readers, O3 matches greatest for a normal viewers and academic functions, providing a whole and fascinating clarification with out being overly technical or oversimplified.

Benchmark Comparability 

To raised perceive the efficiency capabilities of cutting-edge AI fashions, let’s evaluate 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‑degree physics.
• Advanced 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, symbolize the chopping 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, due to its picture‑pushed chain of thought and strong efficiency throughout benchmarks. o4‑mini, with its optimized design and decrease latency, excels in structured arithmetic and logic challenges, making it very best for top‑throughput coding and quantitative evaluation.

The Gemini 2.5 Professional’s huge context window and native assist for textual content, photos, audio, and video give it a transparent benefit in graduate-level physics and large-scale, multimodal workflows. Selecting between them comes right 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.

Continuously Requested Questions