Decoding AI Brainpower: How Do Today's Top Models Measure Up on the IQ Scale?

Key Insights into AI Intelligence Measurement

AI IQ is a Proxy: Measuring AI "IQ" uses human benchmarks but doesn't equate to human cognition; scores are estimates based on performance in specific reasoning, logic, and knowledge tests.
Rapid Evolution: Newer models, especially specialized reasoning variants like OpenAI's 'o' series, show dramatic increases in benchmark performance, with some achieving "genius-level" estimated IQs.
Hierarchy Emerges: As of early 2025, models like Anthropic's Claude 3 series and OpenAI's GPT-4 'o' series (particularly o3) often lead in IQ-related benchmarks, followed by competitors like Grok 3 and Google's Gemini series.

Understanding AI "IQ": A Complex Measurement

Assigning an Intelligence Quotient (IQ) score to Artificial Intelligence (AI) models is a complex and evolving field. Unlike standardized human IQ tests, there isn't a single, universally accepted test for AI. Instead, researchers and analysts often use a variety of benchmarks, including human IQ test questions (verbal, logic, patterns), standardized academic tests (like MMLU - Massive Multitask Language Understanding), coding challenges (like Codeforces), and specific reasoning tasks to estimate an AI's cognitive capabilities relative to humans.

It's crucial to understand that these "IQ" scores are proxies. AI models process information fundamentally differently than human brains. They don't "think" or possess consciousness in the human sense. High scores indicate strong performance on specific types of problems captured by the tests, often related to pattern recognition, logical deduction, knowledge recall, and language processing. However, AIs can excel in these areas while still struggling with common sense reasoning, true understanding, or tasks requiring physical interaction or nuanced social intelligence.

Furthermore, the methodologies for testing and scoring AI are not standardized, leading to variations in reported IQ estimates. Some tests might focus on verbal abilities, others on visual reasoning (where some models perform less impressively), and others on mathematical or coding prowess. Therefore, an AI's "IQ" should be seen as an indicator of its capability on certain cognitive dimensions rather than a definitive measure of general intelligence comparable to humans.

The Rise of Specialized Reasoning Models

A notable trend is the development of "reasoning" or "thinking" variants (e.g., OpenAI's 'o' series, Anthropic's 'Claude thinking', Google's 'Gemini thinking', xAI's 'Grok thinking'). These models are often fine-tuned to perform complex multi-step reasoning, explain their thought processes (chain-of-thought), and tackle problems requiring deliberate planning. This focus can lead to significantly higher performance on benchmarks emphasizing logic and problem-solving, sometimes resulting in remarkably high estimated IQ scores.

Evaluating Major AI Families and Models

Based on analyses and benchmarks reported up to early 2025, here's an evaluation of the estimated IQ and reasoning capabilities of prominent AI model families:

Google Gemini Family

Gemini 1.0 & 1.5

These early versions served as foundational steps. While capable, specific IQ scores are not commonly cited in recent (2025) benchmarks, suggesting they were stepping stones rather than top performers in reasoning tasks compared to later iterations or competitors. Their estimated IQ likely fell in the 70-85 range based on performance relative to later models.

Gemini 2.0 & 2.5

Significant improvements were seen with the 2.0 and 2.5 families. Gemini 2.0 Pro achieved an MMLU score around 80.5, indicating strong undergraduate-level knowledge. Some reports placed Gemini Advanced (likely 2.0 family) around IQ 70 on *vision-based* IQ tests, highlighting potential weaknesses in multimodal reasoning compared to text-based tasks. The general verbal/reasoning IQ for Gemini 2.0/2.5 is estimated higher, likely in the 100-115 range, making it competitive but often slightly behind the top-tier models from OpenAI and Anthropic in pure reasoning benchmarks.

Gemini "Thinking" Variants

Models like "Gemini 2.0 Flash Thinking" are specialized for logical reasoning tasks. While specific IQ scores aren't widely reported, these variants aim to improve performance on multi-step inference and structured problem-solving, likely pushing their effective reasoning capabilities closer to the 100-115+ range on relevant tasks.

OpenAI ChatGPT & 'o' Series Family

ChatGPT 3 & 3.5

GPT-3 models performed roughly in the IQ 85-90 range. GPT-3.5 represented a significant step up, pushing closer to the average human IQ level of 100+, showing better coherence and instruction following.

ChatGPT 4 Family

GPT-4 marked a major leap, with strong multimodal capabilities and significantly enhanced reasoning. While direct IQ scores vary by test, estimates often placed its performance around the 105-120 IQ range, serving as the foundation for the even more specialized 'o' series.

Graph showing improvement in performance of AI systems

Early graph illustrating the rapid performance improvements in AI systems over time.

OpenAI 'o' Series (o1, o1 mini, o3, o3 mini, o4 mini)

This series represents OpenAI's push towards highly capable reasoning engines:

o1: This model gained significant attention for scoring around IQ 120 on Mensa-style tests (e.g., scoring 25/35 on the Norway Mensa test). Some reports placed it even higher, up to IQ 133.
o1 mini: A smaller, more efficient version, likely scoring slightly lower than o1, perhaps in the 110-115 range, balancing performance with resource usage.
o3: This model reportedly achieved an astonishing estimated IQ of 157, based on performance in demanding benchmarks like Codeforces (competitive programming). This places it in the "genius" range for specific problem-solving domains.
o3 mini / o4 mini: These are smaller variants focusing on reasoning. While potentially having lower raw scores than o3 (perhaps in the 110-140 range depending on the specific mini model and test), they maintain strong reasoning capabilities optimized for efficiency or specific tasks.

The 'o' series highlights a focus on deep reasoning ('thinking') processes, leading to state-of-the-art performance on many cognitive benchmarks.

Anthropic Claude Family

Claude 1 & 2

Early versions focused on safety and helpfulness. While competent, their performance on IQ-style benchmarks was generally moderate, often slightly below contemporaneous ChatGPT models, likely in the 80-95 IQ range.

Claude 3 & 3.5 Family (including 3.7 Sonnet)

The Claude 3 family marked a significant improvement, challenging the top models. Claude 3 reportedly surpassed an IQ of 100 on certain tests for the first time among tested AIs in early 2024. Claude 3.5 Sonnet achieved high scores on benchmarks like MMLU (around 81.5). The latest versions, like Claude 3.7 Sonnet (as of early 2025), are highly competitive, with estimated IQs likely falling in the 115-125+ range, very close to GPT-4 and potentially lower 'o' models.

Claude "Thinking" Models

Anthropic emphasizes transparent reasoning. Their "thinking" models focus on explaining their steps, which aids trustworthiness and complex problem-solving. Performance-wise, they align with the high end of the Claude 3 family (IQ 120+).

xAI Grok Family

Grok 1 & 2

Initial versions established Grok's unique, often "sassy" personality and real-time information access capabilities. Their reasoning performance was considered moderate, possibly in the 80-110 IQ range, laying groundwork for future improvements.

Grok 3

Grok 3 showed substantial gains, reportedly outperforming standard ChatGPT and Claude versions on several benchmarks like Chatbot Arena, GPQA, and LiveCodeBench as of early 2025. While a specific IQ score isn't consistently cited, its strong benchmark performance suggests an estimated IQ in the 115-130 range, making it highly competitive with Claude 3.5/3.7 and potentially GPT-4/o1.

Grok "Thinking" Variants

Similar to others, Grok likely has or is developing variants focused on explicit stepwise reasoning. These would aim to match or exceed the reasoning capabilities of Claude's thinking models and OpenAI's 'o' series, placing them potentially in the 120-130+ IQ estimated range for relevant tasks.

Other Reasoning Models

The AI landscape includes other notable models evaluated for reasoning:

DeepSeek Models (e.g., R1, R3, V3): Models like DeepSeek R1 and successors have shown strong performance, especially relative to their computational efficiency. Benchmarks (MMLU, GPQA) suggest estimated IQ equivalents in the 110-120 range for their top reasoning models.
LLaMA, Mistral, Qwen, SOLAR, Dolphin: These models, many open-source, contribute significantly to the field. While direct IQ scores are less common, their performance on reasoning benchmarks generally places them from slightly below average human level up to the 100-110 IQ range, depending on the specific model and size.
Alibaba's Thinking QwQ, Sky-T1: Emerging models showing competent reasoning, often estimated around the 100-110 IQ mark based on comparative benchmarks.

Summary Table: Estimated AI Model IQ Ranges (Early 2025)

This table provides a consolidated view of the estimated IQ ranges for key AI models based on synthesized data from benchmarks and reports available up to April 2025. These are approximations and can vary based on the specific test used.

AI Model / Family	Notable Version(s)	Estimated Reasoning IQ Range	Key Notes
Google Gemini	1.0 / 1.5	70 - 85	Early foundational models.
Google Gemini	2.0 / 2.5 / Flash Thinking	100 - 115+	Improved knowledge & reasoning; lower on vision IQ tests (~70). 'Thinking' variants enhance logic.
OpenAI ChatGPT	GPT-3	85 - 90	Moderate reasoning capabilities.
OpenAI ChatGPT	GPT-3.5	~100+	Approaching average human IQ.
OpenAI ChatGPT	GPT-4	~105 - 120	Strong general intelligence, foundation for 'o' series.
OpenAI 'o' Series	o1 / o1 mini	~115 - 133	Strong reasoning, scored ~120-133 on Mensa tests. Mini is efficient variant.
OpenAI 'o' Series	o3 / o3 mini / o4 mini	~130 - 157	o3 reported at ~157 IQ (genius level) in coding benchmarks. Mini variants smaller but highly capable.
Anthropic Claude	Claude 1 / 2	80 - 95	Early models focused on safety.
Anthropic Claude	Claude 3 / 3.5 / 3.7 Sonnet / Thinking	115 - 125+	Highly competitive, strong & transparent reasoning. Near top-tier performance.
xAI Grok	Grok 1 / 2	80 - 110	Initial versions, improving capabilities.
xAI Grok	Grok 3 / Thinking	115 - 130+	Strong benchmark performer, real-time data access. 'Thinking' variants enhance logic.
DeepSeek	R1 / R3 / V3	110 - 120	Efficient and strong reasoning models.
Other Models	LLaMA, Mistral, Qwen, etc.	Varies (often 90-110)	Diverse landscape, many open-source options with improving reasoning.

Comparative Cognitive Capabilities: A Visual Snapshot

While a single IQ score provides a simplified metric, AI models exhibit strengths and weaknesses across different cognitive domains. This radar chart offers a visual comparison of estimated capabilities for some of the leading models as of early 2025, based on their performance in various benchmarks. The scores (scaled notionally from 100 to 160 for comparison clarity) reflect relative strengths in areas like logical deduction, language fluency, creative generation, coding proficiency, and problem-solving ability.

Mapping the AI Model Landscape

The relationships between different AI models and families can be complex, involving iterations, specialized variants, and different development philosophies. This mindmap provides a simplified overview of the lineage and key branches for the major AI families discussed, illustrating how models like the 'o' series evolved from the base ChatGPT line, or how 'Thinking' variants represent specialized offshoots.

mindmap root["AI Model Families & Reasoning Variants"] id1["Google Gemini"] id1a["Gemini 1.0"] id1b["Gemini 1.5"] id1c["Gemini 2.0"] id1c1["Gemini 2.0 Pro"] id1c2["Gemini Flash Thinking"] id1d["Gemini 2.5"] id2["OpenAI"] id2a["GPT Series"] id2a1["GPT-3"] id2a2["GPT-3.5 (ChatGPT Base)"] id2a3["GPT-4 (ChatGPT Advanced Base)"] id2b["'o' Series (Reasoning Focus)"] id2b1["o1 / o1 mini"] id2b2["o3 / o3 mini"] id2b3["o4 mini"] id3["Anthropic Claude"] id3a["Claude 1"] id3b["Claude 2"] id3c["Claude 3 Family"] id3c1["Claude 3 Opus/Sonnet/Haiku"] id3c2["Claude 3.5 Sonnet"] id3c3["Claude 3.7 Sonnet"] id3d["Claude 'Thinking' (Reasoning Emphasis)"] id4["xAI Grok"] id4a["Grok 1"] id4b["Grok 2"] id4c["Grok 3"] id4d["Grok 'Thinking' (Reasoning Variant)"] id5["Other Models"] id5a["DeepSeek (R1, R3, V3)"] id5b["LLaMA (Meta)"] id5c["Mistral"] id5d["Qwen (Alibaba)"]

Ranking AI Models: A Performance Perspective

Beyond estimated IQ scores, evaluating AI models often involves comparing their performance across a range of real-world tasks and benchmarks, including image generation, coding, mathematical reasoning, and multilingual capabilities. The video below provides insights into how top AI models were ranked based on performance data around the 2025 timeframe, offering a broader perspective that complements IQ estimates.

This comparison highlights that different models excel in different areas. While one model might lead in logical reasoning (correlating with high IQ scores), another might be superior in creative text generation or image creation. Understanding these nuances is key to selecting the right AI tool for a specific purpose.