The AI landscape has fundamentally shifted in late 2025 with Google's release of Gemini 3 Pro, sparking intense debate about which frontier model truly leads in reasoning capabilities. After analyzing comprehensive benchmark data and real-world performance metrics, we examine how Gemini 3 compares to OpenAI's GPT-5, Anthropic's Claude 4.5 Sonnet, and xAI's Grok 4.1 across critical reasoning scenarios.

Executive Summary: Who Wins at Reasoning?

Gemini 3 Pro has emerged as the reasoning performance leader in November 2025, achieving breakthrough scores that surpass its competitors on multiple fronts. With a historic 1501 Elo score on LMArena—the first model to cross the 1500 threshold—and revolutionary performance on abstract reasoning tasks, Google's latest model represents a significant leap forward in AI capabilities.

However, the complete picture reveals that “best” depends heavily on your specific use case. Each model excels in different reasoning scenarios, making the choice strategic rather than obvious.

Benchmark Deep Dive: Pure Reasoning Power

Humanity's Last Exam: The Ultimate Reasoning Test

Humanity's Last Exam stands as one of the most challenging reasoning benchmarks, designed to push AI to its absolute limits across diverse subjects. The results tell a compelling story:

• Gemini 3 Pro: 37.5% (standard mode) | 41.0% (Deep Think mode)
• GPT-5 Pro: 31.64%
• Claude 4.5 Sonnet: Performance data suggests mid-20s range
• Grok 4.1: Comparable to GPT-5 range

Gemini 3's 37.5% score represents nearly an 11% improvement over GPT-5, marking what researchers describe as a “massive jump in reasoning depth and nuance.” The Deep Think mode pushes this even further to 41.0%, demonstrating unprecedented capability in tackling problems that require extended contemplation.

Real-World Impact: For applications requiring complex decision-making—like hypothesis generation in scientific research, multi-step legal analysis, or strategic business planning—Gemini 3's superior performance on this benchmark suggests it can handle more sophisticated reasoning chains without breaking down.

GPQA Diamond: PhD-Level Scientific Reasoning

GPQA Diamond tests models on graduate-level scientific knowledge across physics, chemistry, and biology:

• Gemini 3 Pro: 91.9% (standard) | 93.8% (Deep Think)
• GPT-5.1: 88.1%
• Gemini 2.5 Pro: 86.4%
• Grok 4: 87.5%
• Claude 4.5: Data suggests ~85-88% range

Gemini 3's nearly 4-point lead over GPT-5.1 establishes it as the current leader for scientific reasoning tasks. While this benchmark is approaching saturation (meaning further improvements will be harder), the gap remains meaningful for specialized applications.

Use Case Fit: Scientific research teams, pharmaceutical companies conducting compound analysis, and academic institutions requiring AI assistance with complex scientific queries will benefit most from Gemini 3's performance here.

ARC-AGI-2: Abstract Visual Reasoning

The Abstraction and Reasoning Corpus (ARC-AGI-2) represents perhaps the most telling benchmark for genuine reasoning capability. Unlike tests that can be “gamed” through memorization, ARC-AGI-2 presents novel visual pattern puzzles that require discovering and applying abstract rules.

• Gemini 3 Pro: 31.1% | 45.1% (Deep Think)
• GPT-5.1: 17.6%
• Gemini 2.5 Pro: 4.9%
• Claude 4.5 / Grok 4.1: Limited published data

Gemini 3's 31.1% baseline score nearly doubles GPT-5.1's performance, while the Deep Think mode's 45.1% represents an unprecedented achievement in abstract reasoning. This massive improvement suggests fundamental architectural advances in how Gemini 3 approaches novel problem-solving.

Why This Matters: ARC-AGI-2 performance correlates strongly with generalization capability—the ability to solve problems the model has never seen before. High scores here indicate Gemini 3 is better equipped for truly novel challenges rather than pattern-matching against training data.

Mathematical Reasoning: Where Speed Meets Precision

AIME 2025: Competition Mathematics

The American Invitational Mathematics Examination tests advanced high-school and early college-level mathematical reasoning:

With Code Execution:

• Gemini 3 Pro: 100%
• GPT-5: 100%
• Gemini 2.5 Pro: 88%

Without Tools (Pure Reasoning):

• Gemini 3 Pro: 95.0%
• GPT-5: ~71%

The critical differentiator emerges in tool-free performance. Gemini 3's 95% score without code execution reveals stronger innate mathematical intuition, making it less dependent on external computational aids to reach correct solutions.

Practical Application: For scenarios where tool access is limited or latency-sensitive—like real-time mathematical tutoring, rapid prototyping, or environments with restricted API access—Gemini 3's strong baseline reasoning provides significant advantages.

MathArena Apex: Frontier Mathematical Problems

MathArena Apex represents the cutting edge of mathematical challenges, with problems so difficult that most models score near zero:

• Gemini 3 Pro: 23.4%
• Gemini 2.5 Pro: 0.5%
• Other models: Generally sub-5%

This >20x improvement demonstrates Gemini 3's exceptional capability for mathematical logic and problem formulation. While 23.4% may seem modest in absolute terms, it represents genuine progress on problems that were essentially unsolvable by AI just months ago.

Coding and Algorithmic Reasoning

LiveCodeBench Pro: Competitive Programming

LiveCodeBench Pro evaluates algorithmic problem-solving through competitive coding challenges:

• Gemini 3 Pro: 2,439 Elo rating
• GPT-5.1: 2,243 Elo (~200 points lower)
• Claude 4.5 Sonnet: Strong performer, ~2,300 range
• Grok 4: 79.3% on standard LiveCodeBench

Gemini 3's commanding 200-point Elo advantage over GPT-5.1 indicates superior skill in generating novel, efficient algorithms from scratch. This isn't just about completing code—it's about creating optimal solutions to complex algorithmic challenges.

SWE-Bench Verified: Real-World Bug Fixing

For practical software engineering—fixing actual GitHub issues:

• Claude 4.5 Sonnet: 77.2% (industry leader)
• Gemini 3 Pro: 76.2%
• GPT-5: 74.9%
• Grok 4: Limited direct data

Key Insight: Claude 4.5 Sonnet maintains a narrow edge in real-world code debugging and bug fixing. Its architecture appears specifically optimized for understanding existing codebases and making surgical improvements—a different skill from algorithmic problem-solving.

Strategic Choice:

• Gemini 3: Best for from-scratch algorithm development, competitive programming, complex code generation
• Claude 4.5: Superior for code review, debugging existing projects, understanding large codebases

Long-Horizon Reasoning: Agentic Workflows

Vending-Bench 2: Sustained Strategic Decision-Making

Vending-Bench 2 simulates managing a vending machine business over a full year, testing long-term planning, coherent decision-making, and consistent tool usage:

• Gemini 3 Pro: $5,478.16 mean net worth (272% higher than GPT-5.1)
• GPT-5.1: Baseline performance
• Other models: Limited published data

This result is arguably the most indicative of practical agentic utility. Gemini 3's ability to maintain strategic focus over extended simulations suggests superior capability for autonomous workflows that require:

• Consistent decision-making over time
• Reliable tool usage without drift
• Strategic planning with delayed consequences
• Coherent goal pursuit over multiple steps

Business Applications: Enterprise process automation, complex workflow orchestration, autonomous agents managing long-running tasks, and strategic planning systems benefit directly from this demonstrated capability.

Multimodal Reasoning: Beyond Text

MMMU-Pro: Integrated Visual-Textual Reasoning

• Gemini 3 Pro: 81.0%
• GPT-5.1: 76.0%
• Claude 4.5 / Grok 4.1: ~74-76% range

Video-MMMU: Temporal Understanding

• Gemini 3 Pro: 87.6%
• GPT-5.1: ~80-82% estimated
• Others: Limited comparative data

Gemini 3's 5-point lead in multimodal reasoning demonstrates exceptional ability to process and reason across temporal and spatial dimensions simultaneously. This makes it particularly effective for:

• Analyzing video lectures or presentations
• Understanding complex UI screenshots
• Processing documents with mixed media (charts, diagrams, text)
• Real-time visual analysis combined with textual queries

Model-Specific Reasoning Strengths

Gemini 3 Pro: The Reasoning Generalist Leader

Dominant Scenarios:

• Abstract visual reasoning (ARC-AGI-2: 45.1% with Deep Think)
• Pure mathematical intuition (AIME without tools: 95%)
• Long-horizon strategic planning (Vending-Bench 2)
• Multimodal reasoning across temporal dimensions
• Novel algorithmic problem-solving

Architecture Advantages:

• Native multimodal design from inception
• 1M token context window
• Deep Think mode for enhanced reasoning
• Proven generalization on out-of-distribution tasks

Best For: Scientific research requiring multimodal analysis, complex agent workflows, novel problem domains, integrated visual-textual reasoning

GPT-5: The Efficient Reasoning Workhorse

Strengths:

• Balanced performance across most benchmarks
• Strong reasoning-to-cost ratio (60% cheaper than Claude for similar tasks)
• Enhanced reasoning modes reduce error rates significantly
• Mature ecosystem and tooling
• Fast inference speeds

Strategic Position: GPT-5 sacrifices slight performance advantages for significantly better economics and reliability. Its 86.0% on GPQA Diamond and strong showing across diverse tasks make it the “reliable generalist” choice.

Best For: High-volume analytical tasks where cost matters, general-purpose reasoning, rapid prototyping, applications requiring mature API ecosystem

Claude 4.5 Sonnet: The Code Reasoning Specialist

Distinctive Capabilities:

• Industry-leading real-world bug fixing (SWE-Bench: 77.2%)
• Extended reasoning mode with visible thought processes
• Exceptional at understanding existing codebases
• Strong focus on safe, conservative outputs
• Multi-hour autonomous runs maintaining focus

Reasoning Philosophy: Claude emphasizes reliability and transparency over peak performance. Its visible reasoning traces help developers audit decision-making processes—critical for production systems.

Best For: Code review and debugging, long-form documentation, applications requiring explainable reasoning, safety-critical systems, enterprise compliance scenarios

Grok 4.1: The Real-Time Reasoning Contender

Unique Advantages:

• Real-time information access during reasoning
• Lowest token costs for high-volume work
• Strong performance on up-to-date information tasks
• 2M token context window (extended version)

Reasoning Trade-offs: Grok 4.1 trades peak reasoning performance for breadth of information access and cost efficiency. It excels when reasoning requires current events, social sentiment analysis, or massive context.

Best For: Real-time research, trend analysis, social sentiment evaluation, cost-sensitive deployments, massive document processing

Reasoning Performance by Use Case

Scientific Research & Analysis

Winner: Gemini 3 Pro

• Highest GPQA Diamond score (91.9%)
• Superior multimodal reasoning for lab data
• Strong abstract reasoning for novel hypotheses
• Deep Think mode for complex analysis

Runner-up: GPT-5 for budget-conscious research teams

Software Development & Debugging

Winner: Claude 4.5 Sonnet

• Best SWE-Bench Verified performance (77.2%)
• Exceptional at understanding existing code
• Transparent reasoning traces for review
• Maintains focus during long refactoring sessions

Runner-up: Gemini 3 Pro for algorithm development

Business Strategy & Planning

Winner: Gemini 3 Pro

• Exceptional long-horizon planning (Vending-Bench 2)
• Consistent strategic decision-making
• Strong abstract reasoning for novel scenarios
• Multimodal capability for data visualization analysis

Runner-up: GPT-5 for cost-effective strategic analysis

Mathematical Problem-Solving

Winner: Gemini 3 Pro

• Strongest pure reasoning without tools (95% on AIME)
• Revolutionary MathArena Apex performance
• Superior innate mathematical intuition

Tied: GPT-5 and Gemini 3 with code execution (both 100% AIME)

Real-Time Information Analysis

Winner: Grok 4.1

• Native real-time data access
• Strong reasoning over current events
• Cost-effective for high-volume tasks
• Massive context for comprehensive analysis

Runner-up: Gemini 3 Pro for depth over breadth

The Deep Think Advantage

Gemini 3's Deep Think mode represents a fundamental shift in reasoning capability. By allowing the model additional processing time for complex problems, it achieves:

• +3.5 percentage points on Humanity's Last Exam (37.5% → 41.0%)
• +1.9 percentage points on GPQA Diamond (91.9% → 93.8%)
• +14 percentage points on ARC-AGI-2 (31.1% → 45.1%)

This “reasoning on demand” approach mirrors human cognitive processes—taking more time for harder problems yields better results. For applications where latency is acceptable in exchange for accuracy, Deep Think mode pushes reasoning capabilities into new territory.

Cost-Benefit Reasoning Analysis

Total Cost of Reasoning

When evaluating reasoning performance, token costs matter significantly:

Price per Million Tokens (Input/Output):

• Gemini 3 Pro: Context-tiered, premium for complex tasks
• GPT-5: $1.25/$10
• Claude 4.5 Sonnet: $3/$15
• Grok 4: Lowest base cost, scales to $300/month heavy usage

Economic Reasoning Considerations:

For high-volume reasoning tasks where slight accuracy differences matter less than cost, GPT-5 offers 60% better price-per-task than Claude while maintaining competitive performance.

For critical reasoning tasks where errors are expensive, Gemini 3's premium pricing is offset by significantly higher success rates on first attempts, reducing iteration cycles.

For exploratory reasoning and rapid prototyping, Grok 4's low costs enable experimentation without budget constraints.

Reasoning Reliability: Beyond Benchmarks

Factual Accuracy Under Reasoning

SimpleQA Verified (factual accuracy):

• Gemini 3 Pro: 72.1% (state-of-the-art)
• GPT-5: Strong performer, ~68-70% range
• Claude 4.5: Emphasizes conservative, accurate outputs

Gemini 3's leadership in factual accuracy while reasoning represents crucial progress. Many models can follow logical reasoning chains but arrive at factually incorrect conclusions—Gemini 3 demonstrates strength in both dimensions.

Hallucination Resistance During Complex Reasoning

GPT-5 shows lowest error rates in real-world traffic:

• 4.8% error rate with reasoning mode enabled
• 1.6% on difficult medical cases (HealthBench)

Claude 4.5 emphasizes conservative outputs to minimize hallucinations, particularly valuable in safety-critical reasoning scenarios.

The Verdict: Context Determines the Champion

After comprehensive analysis across reasoning benchmarks and real-world scenarios, Gemini 3 Pro emerges as the overall reasoning performance leader in late 2025. Its breakthrough scores on abstract reasoning (ARC-AGI-2), general reasoning (Humanity's Last Exam), mathematical intuition (pure AIME, MathArena Apex), and long-horizon planning establish it as the most capable reasoning model currently available.

However, optimal model selection requires matching capabilities to requirements:

Choose Gemini 3 Pro for:

• Scientific research requiring cutting-edge reasoning
• Agent workflows with complex multi-step planning
• Novel problem domains requiring generalization
• Multimodal reasoning across images, video, and text
• Applications where peak performance justifies premium costs

Choose GPT-5 for:

• High-volume reasoning tasks with budget constraints
• General-purpose analytical work
• Rapid development cycles requiring mature tooling
• Scenarios where 90% of peak performance at 60% cost makes sense

Choose Claude 4.5 Sonnet for:

• Code-heavy reasoning and debugging
• Long-form analysis requiring sustained focus
• Applications demanding explainable reasoning
• Safety-critical systems requiring conservative outputs

Choose Grok 4.1 for:

• Real-time reasoning over current information
• Cost-sensitive deployments at scale
• Massive context reasoning tasks
• Trend analysis combining reasoning with live data

The Future of Reasoning Performance

Gemini 3's achievements—particularly the 45.1% ARC-AGI-2 score and 41% on Humanity's Last Exam—suggest we're entering a new phase of AI reasoning capability. The gap between “pattern matching against training data” and “genuine abstract reasoning” is narrowing.

For organizations building AI-powered products, the reasoning race of 2025 offers unprecedented choice. The days of one-size-fits-all model selection are over. Strategic deployment requires understanding not just which model reasons best, but which reasoning profile aligns with specific business needs, cost constraints, and risk tolerances.

The reasoning revolution is here—and it's more nuanced than ever before.

Benchmark data compiled from official Google, OpenAI, Anthropic, and xAI releases, independent evaluations from LMArena, Vellum AI, and Artificial Analysis, published November 2025.