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 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 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 need 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 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 “pondering with pictures” for direct visible reasoning; splendid for in‑depth analytical duties.
- o4‑mini: Compact, environment friendly counterpart optimized for pace and throughput; delivers robust math, coding, and imaginative and prescient efficiency at decrease price.
You possibly can entry each in ChatGPT and by way of the Responses API.
Key Options of o3 and o4-mini
Listed here are a number of the key options of those superior and highly effective reasoning fashions:
- Agentic Conduct: They exhibit proactive problem-solving skills, autonomously figuring out the perfect method to advanced duties and executing multi-step options effectively.
- Superior Software Integration: The fashions seamlessly make the most of instruments like net shopping, code execution, and picture era to boost their responses and successfully deal with advanced queries.
- Multimodal Reasoning: They will course of and combine visible data straight into their reasoning chain, which permits them to interpret and analyze pictures alongside textual information.
- Superior Visible Reasoning (“Pondering with Photographs”): The fashions can interpret advanced visible inputs, reminiscent of diagrams, whiteboard sketches, and even blurry or low-quality photographs. They will even manipulate these pictures (zoom, crop, rotate, improve) as a part of their reasoning course of to extract related data.
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 steadiness 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, pictures, 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 investigate data 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 enables 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 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, 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 Just 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 distinct 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 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 legislation | Appropriate and concise; makes use of κ and hyperlinks to α clearly | Appropriate and detailed; makes use of logarithmic transformation and contains items |
| Ordering of part velocities | Appropriate with mathematical readability and bodily clarification | Appropriate with crisp logical reasoning | Appropriate with robust conceptual background and intuitive reasoning |
| On-resonance index & velocity | Exact values with unit conversion and implications | Approximate however clear; assumes background index ≈ 1 | Qualitative clarification; barely much less quantitative |
| Readability and depth of clarification | Deep however technical | Concise and student-friendly | Conceptually wealthy and well-structured; extremely readable |
Last verdict:
All three fashions present right and coherent solutions, however Gemini 2.5 stands out as the perfect general performer. Whereas O3 affords probably the most technical rigor, O4-mini excels in pace and readability. Gemini 2.5 strikes the optimum steadiness 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 splendid for each understanding and verification.
Job 2: Numerical Reasoning
Enter immediate: Choose the quantity from among the many given choices that may change 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 |
| Rationalization Fashion | Barely technical however neat | Clear and minimalistic | Most detailed and intuitive |
| Readability for College students | Good for intermediate learners | Nice for fast understanding | Glorious for all ranges |
Last Verdict:
All three fashions accurately establish 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 normal 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 method. This makes it splendid for learners who admire readability and compact reasoning. O4-mini delivers the perfect general clarification.
Job 3: Coding activity
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 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 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 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 top (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 does not exist, insert new node
new_node = Node(key, worth)
self.cache[key] = new_node
self._add_to_front(new_node)
# Examine 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 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 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
| Standards | o3 | o4-mini | Gemini 2.5 |
| Readability | Glorious construction and feedback | Very concise, much less readable | Clear with useful feedback |
| Design & Modularity | Properly-modularized, makes use of non-public strategies | Minimalistic, lacks modular security | Modular with helper strategies and checks |
| Efficiency | Optimum with __slots__ & sentinels | Optimum and memory-efficient | Optimum, with non-obligatory GC recommendations |
| Security & Robustness | Sturdy validation and clear error-handling | Lacks enter checks (e.g., capability ≤ 0) | Consists of validation and security guards |
Last Verdict
o3 affords the perfect steadiness 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 embrace the next sections:
1. Header: Show the consumer’s identify and a short tagline.
2. About Me: A brief paragraph describing the consumer’s background and abilities.
3. Tasks: Showcase at the least three initiatives with titles, descriptions, and hyperlinks.
4. Contact: Present contact data or a contact type.
Make sure the design is clear and mobile-friendly. Use semantic HTML components and embrace 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
| Characteristic | 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 | Glorious use of semantic HTML and feedback; constant BEM-style class naming | Clear semantic HTML, however much less modular or commented | Properly-structured with clear separation of HTML, CSS, and JS. Deal with semantic HTML with correct use of components like
|
| Responsiveness | Extremely responsive with clamp(), auto-fit grids, and media queries | Primary responsiveness with flex-wrap and easy media queries | Good responsive with dynamic changes for numerous display sizes, making certain a correct consumer expertise throughout units. |
| Options Included | Full function set: header, about, initiatives grid, contact (type & data), clear footer | Related sections however lacks the polish in structure, interactivity, and styling finesse | Related sections however lacks the polish in structure, interactivity, and styling finesse |
Last Verdict:
It contains a extra polished UI, improved responsiveness, fashionable CSS strategies (reminiscent of variables and clamp), and an expert general 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
| Side | Output by o3 | Output by o4-mini | Output by Gemini 2.5 |
| Readability | Clear, easy, and simple to know. | Barely extra detailed, nonetheless clear. | Easy and simple to digest. |
| Rationalization Depth | Balanced clarification with important particulars. | Extra particulars on how colours bend. | Very fundamental clarification of the idea. |
| Tone/Fashion | Impartial, scientific, but accessible. | Barely conversational, nonetheless formal. | Very instructional, designed for fast understanding. |
| Size | Compact, concise, covers all key factors. | Longer, gives a bit extra depth. | Very transient and to the purpose. |
Last verdict:
The o3 output gives the perfect steadiness of readability, completeness, and ease, making it splendid for a normal viewers. It explains the method of a rainbow clearly, with out overwhelming the reader with extreme particulars, whereas nonetheless protecting important features like refraction, inside reflection, and the way a number of drops create the rainbow impact. Its concise fashion makes it simple to digest and perceive, making it the simplest alternative for explaining the phenomenon of a rainbow.
Total Assessment
O3 is the perfect general performer throughout all dimensions. It strikes the proper steadiness between being scientifically correct and simple to know. Whereas Gemini 2.5 is good for very fundamental understanding and O4-mini for extra technical readers, O3 matches finest for a normal viewers and academic functions, providing an entire and fascinating clarification with out being overly technical or oversimplified.
Benchmark Comparability
To higher 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 complicated 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 robust area experience in graduate‑degree physics.
- Advanced reasoning problem: All fashions battle on Humanity’s Final Examination (
- 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 strong efficiency throughout benchmarks. o4‑mini, with its optimized design and decrease latency, excels in structured arithmetic and logic challenges, making it splendid for top‑throughput coding and quantitative evaluation.
The Gemini 2.5 Professional’s huge context window and native help for textual content, pictures, 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, fast 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.
Incessantly 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 typically outperform Gemini 2.5 in superior coding and software program engineering duties. Nonetheless, Gemini 2.5 is most well-liked for coding initiatives requiring massive context home windows or multimodal inputs.
Gemini 2.5 Professional is roughly 4.4 instances cheaper than O3 for each enter and output tokens. This makes Gemini 2.5 a powerful alternative for large-scale or budget-conscious functions.
Gemini 2.5 Professional: As much as 2 million tokens
O3 and O4-mini: Sometimes help 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, pictures, audio, and video, making it extra versatile for cross-modal duties.
Information Scientist | AWS Licensed Options Architect | AI & ML Innovator
As a Information Scientist at Analytics Vidhya, I specialise in Machine Studying, Deep Studying, and AI-driven options, leveraging NLP, pc imaginative and prescient, and cloud applied sciences to construct scalable functions.
With a B.Tech in Pc Science (Information 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. Captivated with innovation, I attempt to develop clever techniques that form the way forward for AI.
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