OpenAI o3 is a reasoning-centric language model engineered to deliver accurate, context-aware responses while fluidly combining browsing, multimodal analysis, and tool usage in real time.
OpenAI o3 is a reasoning-focused language model that delivers precise, context-aware answers.
СhatGPT o3 Description
OpenAI's o3 is an advanced AI reasoning model engineered for complex problem-solving and multi-step analysis. With simulated reasoning capabilities and integrated tool use, it delivers unprecedented performance in coding, mathematics, and scientific research.
Technical Specification
Performance Benchmarks
OpenAI o3 is optimized for reasoning, coding, and scientific problem-solving across multiple domains.
Context Window: 200k context with multi-modal processing with step-by-step reasoning chains
Reasoning Architecture: Simulated reasoning with internal reflection and self-analysis
The o3 model demonstrates strong performance across multiple domains, achieving 91.6% accuracy on AIME 2024 math competition, 88.9% on AIME 2025, 83.3% on PhD-level science questions (GPQA Diamond), 20.32% on expert-level questions across subjects (Humanity's Last Exam), and a 2706 ELO rating in coding competitions (Codeforces)
o3 Benchmarks
Key Capabilities
OpenAI o3 delivers advanced reasoning capabilities for complex analytical workflows.
Simulated Reasoning: Internal reflection and step-by-step logical analysis before generating responses
Visual Reasoning Integration: Processes images directly within reasoning chains for multi-modal problem solving
Advanced Coding: Excels in software engineering with 69.1% accuracy on real-world coding benchmarks
Mathematical Excellence: Superior performance on competition-level mathematics with 88.9% AIME accuracy
Scientific Research: PhD-level science question handling with 83.3% accuracy on GPQA Diamond
Tool Integration: Native access to web browsing, Python execution, and file operations for agentic problem-solving
Self-Fact Checking: Built-in verification capabilities to improve response accuracy
Optimal Use Cases
Complex Problem-Solving: Multi-step analytical tasks requiring deep reasoning and logical deduction
Software Engineering: Large-scale coding projects, debugging, and architectural decision-making
Mathematical Research: Competition-level mathematics and advanced computational problems
Scientific Analysis: Research-grade scientific reasoning and hypothesis generation
Business Consulting: Strategic analysis requiring multi-faceted evaluation and critical thinking
Creative Ideation: Novel hypothesis generation in technical and creative domains
Code Samples
Comparison with Other Models
Vs. OpenAI o1: 20% fewer major errors, superior coding performance (69.1% vs 48.9% SWE-bench), enhanced mathematical reasoning (88.9% vs 74.3% AIME)
Vs. GPT-4o: Specialized for reasoning tasks requiring deliberation time, integrated visual reasoning capabilities
Vs. Claude Opus 4: Competitive coding accuracy (69.1% vs 72.5% SWE-bench estimated), stronger mathematical performance, integrated tool use for agentic workflows
API Integration
Accessible via AI/ML API. Documentation: available here.
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