AI fashions hold getting smarter, however which one actually causes beneath stress? On this weblog, we put o3, o4-mini, and Gemini 2.5 Professional by means of a sequence of intense challenges: physics puzzles, math issues, coding duties, and real-world IQ checks. No hand-holding, no simple wins—only a uncooked check 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 wish to know who comes out on prime, this text has you lined.
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’ll 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 photos” for direct visible reasoning; perfect 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 below are a few of the key options of those superior and highly effective reasoning fashions:
- Agentic Habits: They exhibit proactive problem-solving skills, autonomously figuring out one of the best method to complicated duties and executing multi-step options effectively.
- Superior Software Integration: The fashions seamlessly make the most of instruments like internet looking, code execution, and picture era to reinforce their responses and successfully deal with complicated queries.
- Multimodal Reasoning: They’ll course of and combine visible info straight into their reasoning chain, which allows them to interpret and analyze photos alongside textual information.
- Superior Visible Reasoning (“Considering with Pictures”): The fashions can interpret complicated visible inputs, reminiscent of diagrams, whiteboard sketches, and even blurry or low-quality images. They’ll 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 sequence 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 refined reasoning system, which allows the AI to investigate 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 knowledge 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 more moderen information, marking a cutoff in data 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, part‑velocity ordering, and on‑resonance refractive index for a dispersive gaseous medium.
- Numerical Collection 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 website with semantic HTML and customized CSS.
- Multimodal Process Breakdown: Analyzing how every mannequin would deal with a picture‑primarily based problem.
Every check probes a unique power, in deep physics reasoning, sample recognition, coding prowess, design fluency, and picture‑context understanding; so you possibly can 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 airplane 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
| Standards | O3 | O4-mini | Gemini 2.5 |
| Absorption coefficient calculation | Appropriate with derivation; makes use of discipline decay equation and Beer–Lambert regulation | Appropriate and concise; makes use of κ and hyperlinks to α clearly | Appropriate and detailed; makes use of logarithmic transformation and consists of models |
| Ordering of part velocities | Appropriate with mathematical readability and bodily rationalization | Appropriate with crisp logical reasoning | Appropriate with sturdy conceptual background and intuitive reasoning |
| On-resonance index & velocity | Exact values with unit conversion and implications | Approximate however clear; assumes background index ≈ 1 | Qualitative rationalization; barely much less quantitative |
| Readability and depth of rationalization | Deep however technical | Concise and student-friendly | Conceptually wealthy and well-structured; extremely readable |
Remaining verdict:
All three fashions present right and coherent solutions, however Gemini 2.5 stands out as one of the 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 perfect 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 sequence: 16, 33, 100, 401,?
o3 Output:
o4-mini Output:
Gemini 2.5 Output:
Output Comparability
| Standards | O3 | O4-mini | Gemini 2.5 |
| Correctness | Appropriate reply (2006) | Appropriate reply (2006) | Appropriate reply (2006) |
| Sample Identification | Clear identification of accelerating multiplier +1 | Identifies a recursive formulation concisely | Walks step-by-step by means of the logic |
| Clarification Model | Barely technical however neat | Clear and minimalistic | Most detailed and intuitive |
| Readability for College students | Good for intermediate learners | Nice for fast understanding | Wonderful for all ranges |
Remaining Verdict:
All three fashions appropriately determine the sample and arrive on the right reply, 2006. Whereas Gemini 2.5 supplies an in depth, step-by-step rationalization and O3 explains the logic clearly, O4-mini stands out for its magnificence and mathematical precision. By expressing the sequence as a common formulation, an=an−1×n+1a_n = a_{n-1} occasions n + 1an=an−1×n+1, O4-mini captures the underlying sample in essentially the most concise and structured approach. This makes it perfect for learners who recognize readability and compact reasoning. O4-mini delivers one of the best general rationalization.
Process 3: Coding activity
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 lately.
The cache ought to function in O(1) time complexity for each operations.
o3 Code:
class Node:
"""Doubly‑linked‑checklist 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 optimistic")
self.cap = capability
self.map: dict[int, Node] = {}
# Dummy head/tail simplify edge‑case dealing with
self.head = Node(0, 0) # Most‑lately‑used sentinel
self.tail = Node(0, 0) # Least‑lately‑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 checklist."""
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 lately 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‑lately‑used entry.
"""
if key in self.map: # Replace current
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 checklist."""
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 current 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 checklist."""
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 optimistic integer")
self.capability = capability
self.cache = {} # Shops key -> Node mapping
# Initialize sentinel nodes for the doubly linked checklist
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 current node from the linked checklist."""
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 lately 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 lately 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 lately 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)
# Verify if capability is exceeded
if len(self.cache) > self.capability:
# Evict the least lately used merchandise (node earlier than tail)
lru_node = self.tail.prev
self._remove_node(lru_node)
# Take away from cache as properly
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
| Standards | o3 | o4-mini | Gemini 2.5 |
| Readability | Wonderful construction and feedback | Very concise, much less readable | Clear with useful feedback |
| Design & Modularity | Nicely-modularized, makes use of personal strategies | Minimalistic, lacks modular security | Modular with helper strategies and checks |
| Efficiency | Optimum with __slots__ & sentinels | Optimum and memory-efficient | Optimum, with non-compulsory GC solutions |
| Security & Robustness | Robust validation and clear error-handling | Lacks enter checks (e.g., capability ≤ 0) | Contains validation and security guards |
Remaining Verdict
o3 affords one of the 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 embrace the next sections:
1. Header: Show the consumer’s title and a short tagline.
2. About Me: A brief paragraph describing the consumer’s background and expertise.
3. Initiatives: Showcase no less than 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 embrace fundamental 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
| Function | o3 | o4-mini | Gemini 2.5 |
| Design Aesthetic | Trendy, colourful with gradient header and constant theme colours | Clear and minimalist with fundamental borders and field shadows | Clear structure with a minimalist design. Makes use of container width and padding for responsiveness. |
| Code Construction & Semantics | Wonderful use of semantic HTML and feedback; constant BEM-style class naming | Clear semantic HTML, however much less modular or commented | Nicely-structured with clear separation of HTML, CSS, and JS. Deal with semantic HTML with correct use of parts like
|
| Responsiveness | Extremely responsive with clamp(), auto-fit grids, and media queries | Fundamental responsiveness with flex-wrap and easy media queries | Good responsive with dynamic changes for numerous display sizes, making certain a correct consumer expertise throughout gadgets. |
| Options Included | Full function set: header, about, tasks grid, contact (type & information), clear footer | Comparable sections however lacks the polish in structure, interactivity, and styling finesse | Comparable sections however lacks the polish in structure, interactivity, and styling finesse |
Remaining Verdict:
It contains a extra polished UI, improved responsiveness, fashionable CSS strategies (reminiscent of 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
| Side | Output by o3 | Output by o4-mini | Output by Gemini 2.5 |
| Readability | Clear, easy, and straightforward to know. | Barely extra detailed, nonetheless clear. | Easy and straightforward to digest. |
| Clarification Depth | Balanced rationalization with important particulars. | Extra particulars on how colours bend. | Very fundamental rationalization of the idea. |
| Tone/Model | Impartial, scientific, but accessible. | Barely conversational, nonetheless formal. | Very instructional, designed for fast understanding. |
| Size | Compact, concise, covers all key factors. | Longer, supplies a bit extra depth. | Very transient and to the purpose. |
Remaining verdict:
The o3 output supplies one of the best stability of readability, completeness, and ease, making it perfect for a common viewers. It explains the method of a rainbow clearly, with out overwhelming the reader with extreme particulars, whereas nonetheless overlaying important points like refraction, inside reflection, and the way a number of drops create the rainbow impact. Its concise type makes it simple to digest and perceive, making it the best alternative for explaining the phenomenon of a rainbow.
Total Evaluation
O3 is one of the best general performer throughout all dimensions. It strikes the proper stability between being scientifically correct and straightforward to know. Whereas Gemini 2.5 is good for very fundamental understanding and O4-mini for extra technical readers, O3 suits greatest for a common viewers and academic functions, providing a whole and interesting rationalization 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 data: 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
In the end, all three fashions, o3, o4‑mini, and Gemini 2.5 Professional, characterize 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 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 in depth multimodal reasoning at scale with Gemini 2.5 Professional), however in each case, these fashions are redefining what AI can accomplish.
Regularly Requested Questions
Gemini 2.5 professional helps a context window of as much as 2 million tokens, considerably bigger than that of O fashions.
O3 and O4-mini usually outperform Gemini 2.5 in superior coding and software program engineering duties. Nevertheless, Gemini 2.5 is most well-liked for coding tasks requiring giant context home windows or multimodal inputs.
Gemini 2.5 Professional is roughly 4.4 occasions more cost effective than O3 for each enter and output tokens. This makes Gemini 2.5 a powerful alternative for large-scale or budget-conscious purposes.
Gemini 2.5 Professional: As much as 2 million tokens
O3 and O4-mini: Usually assist as much as 200,000 tokens
Gemini’s huge context window permits it to deal with a lot bigger paperwork or datasets in a single go.
Sure, however with key distinctions:
O3 and O4-mini embrace imaginative and prescient capabilities (picture enter).
Gemini 2.5 Professional is natively multimodal, processing textual content, photos, audio, and video, making it extra versatile for cross-modal duties.
Knowledge Scientist | AWS Licensed Options Architect | AI & ML Innovator
As a Knowledge Scientist at Analytics Vidhya, I concentrate on Machine Studying, Deep Studying, and AI-driven options, leveraging NLP, pc imaginative and prescient, and cloud applied sciences to construct scalable purposes.
With a B.Tech in Laptop Science (Knowledge Science) from VIT and certifications like AWS Licensed Options Architect and TensorFlow, my work spans Generative AI, Anomaly Detection, Faux Information Detection, and Emotion Recognition. Keen about innovation, I try to develop clever methods that form the way forward for AI.
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