OpenAI o3

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
• Performance Benchmarks: SWE-bench Verified: 69.1%, AIME 2025: 88.9%, GPQA Diamond: 83.3%

• API Pricing:
  • Input tokens: $2.6 per million tokens.
  • Output tokens: $10.4 per million tokens.

o3 Performance Metrics

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)

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

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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.