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Name

Tison Brokenshire

Updated on 2/28/2025

GPT 4.5 vs DeepSeek R1: A Breif Survey Note on the Latest AI Models

OpenAI's o1 and DeepSeek's R1 are cutting-edge large language models designed for complex reasoning tasks, making them vital for developers, data scientists, and tech decision-makers. This comparison explores their technical specs, performance, practical use, and critical considerations to help you choose the right model for your needs.

Why This Comparison Matters

These models represent a technological leap in AI, enabling advanced problem-solving in areas like math, coding, and science. Their impact spans industries, from automating enterprise workflows to empowering startups with cost-effective solutions, reshaping how we approach AI-driven innovation.

Detailed Comparison

Technical Specifications Deep Dive

Architecture Differences

• OpenAI o1: A transformer-based model with enhanced reasoning via chain-of-thought processing. Exact details are not publicly disclosed, adding a layer of mystery to its design.
• DeepSeek R1: Utilizes a Mixture of Experts (MoE) architecture with 671 billion parameters, activating only 37 billion per token for efficiency. This design is openly shared, fostering community exploration.

Training Specifics

• Data Volume and Hardware Requirements:
  • o1: Training data details are not public, but it likely involves vast, diverse datasets with significant compute resources, given its performance.
  • R1: Built on DeepSeek V3, trained for $6 million, suggesting a cost-effective approach using high-end GPUs like Nvidia H800, with reinforcement learning enhancing reasoning.
• Training Techniques: Both use reinforcement learning, but R1 innovates with pure RL for R1-Zero, showing reasoning can emerge without supervised fine-tuning, while o1 integrates a new optimization algorithm.

Tokenization Improvements

• Both models use standard tokenization, with R1 inheriting from Qwen and Llama bases. No significant differences noted, suggesting similar multilingual and code-handling capabilities.

Benchmark Analysis

Standard and Domain-Specific Benchmark Performance

Below is a comparison based on available benchmarks (as of February 28, 2025):

• o1 shines in competitive programming and scientific reasoning, while R1 leads in math-focused tasks, showing domain-specific strengths.

Tradeoffs: Accuracy vs Latency vs Cost-per-Inference

• Accuracy: Both models are highly accurate, with R1 matching o1 in many areas and excelling in math.
• Latency: o1 is slower due to reasoning, with no exact figures; R1's latency varies, with distilled versions offering better speed on constrained hardware.
• Cost-per-Inference: o1 costs $15/$60 per million input/output tokens; R1 is cheaper at $0.55/$2.19, making it 27 times less for inputs and nearly 27.4 times for outputs X post (opens in a new tab).

Practical Comparison

Real-World Performance

• API Response Times: o1's slower responses suit planning stages, not real-time chats. R1's latency depends on hardware, with distilled versions running at 200 tokens/second on Raspberry Pi for smaller tasks.
• Fine-Tuning Requirements: R1's open-source nature allows easy fine-tuning, while o1 requires API access with potentially higher barriers.

Cost Analysis

• R1's pricing structure ($0.55 input, $2.19 output per million tokens) is significantly lower, appealing for cost-sensitive projects, with scalability implications favoring startups.

Ecosystem Support

• o1: Robust with extensive OpenAI documentation and community tools.
• R1: Growing community on Hugging Face, open-source, and supported by platforms like Fireworks AI, enhancing developer accessibility.

Use Case Breakdown

Scenario-Based Recommendations

• Complex Reasoning: Use R1 for cost-effective math and coding, o1 for scientific tasks where latency isn't critical.
• Rapid Responses: Prefer faster models for chatbots; o1's delay may frustrate users.
• Local Deployment: R1's distilled versions (1.5B to 70B parameters) suit constrained hardware, ideal for startups.

Enterprise vs Startup Considerations

• Enterprises: May opt for o1's stability and support, despite higher costs, for large-scale deployments.
• Startups: R1's cost-effectiveness and open-source flexibility align with budget constraints and innovation needs.

Specialized Applications

• Coding and Math: Both excel, with R1 showing edge in math benchmarks.
• Multimodal Tasks: o1 has image analysis, while R1 focuses on text, limiting its scope.

Critical Analysis

Limitations

• Both have knowledge cutoffs (o1: October 2023), can hallucinate, and may reflect training data biases, requiring careful validation.

Ethical Considerations

• Both must ensure responsible use, with R1's open-source nature raising concerns about misuse, while o1's closed nature limits transparency, debated in developer circles.

Environmental Impact

• Training large models like R1 (671B parameters) and o1 consumes significant energy, with DeepSeek claiming lower costs but still needing sustainability focus, a growing concern in AI ethics.

Citations

• OpenAI o1 Documentation (opens in a new tab)
• DeepSeek R1 GitHub Repository (opens in a new tab)
• DeepSeek R1 Features and Comparison (opens in a new tab)
• OpenAI Pricing Information (opens in a new tab)
• DeepSeek R1 API Documentation (opens in a new tab)
• X post on DeepSeek R1 cost comparison (opens in a new tab)