Deepseek R1 vs Llama 3.2 vs ChatGPT o1: Which AI model wins?

As far as LLMs or foundational models go, DeepSeek R1 is all the rage right now. DeepSeek R1, Llama 3.2, and OpenAI ChatGPT o1 each introduce distinctive advantages, from architectural innovations to specific improvements in reasoning and multimodal abilities. Below is a thorough comparison that delves into their technical specifications, benchmarks, and notable strengths.

DeepSeek R1 employs a Mixture-of-Experts (MoE) architecture with 671 billion parameters, activating only 37 billion per request to balance performance and efficiency. On the other hand, Llama 3.2 offers multiple parameter sizes (1B to 90B), with certain variants optimised for vision tasks and edge deployments. OpenAI o1, the latest reasoning ChatGPT model from OpenAI, is designed to excel in complex problem-solving tasks such as mathematics, coding, and science by employing advanced chain-of-thought techniques.

Also read: DeepSeek-R1, BLOOM and Falcon AI: Exploring lesser-known open source LLMs

Here is a quick comparison between DeepSeek R1, Llama 3.2 and OpenAI o1 based on the key features they bring to the table. 

1. DeepSeek R1

Mixture-of-Experts Architecture: Activates only a subset of parameters per query for efficiency.

Open Source: Available under an MIT licence for customisation and cost-effective deployment.

Reinforcement Learning Post-Training: Enhances reasoning capabilities without requiring extensive supervised datasets.

128K Context Window: Handles lengthy documents or discussions seamlessly.

Cost Efficiency: Approximately 27.4 times cheaper than proprietary alternatives.

2. Llama 3.2

Scalable Model Sizes: Ranges from lightweight (1B) to advanced (90B), with vision-enabled variants at 11B and 90B.

Edge Optimisation: Smaller models run efficiently on mobile or edge devices.

Multimodal Capabilities: Larger models can process text and images for advanced vision tasks.

128K Context Length: Suitable for handling extensive prompts or documents.

Also read: OpenAI launches Operator: How will this AI agent impact the industry?

3. OpenAI o1 (ChatGPT)

Enhanced Reasoning: Excels in multi-step problem solving through chain-of-thought processing.

Multimodal Abilities: Supports text and image inputs for complex analysis.

Advanced Training Techniques: Uses reinforcement learning and a tailored dataset to improve reasoning accuracy.

200K Context Window: The largest among the three models, ideal for highly complex queries.

Proprietary System: Accessible via subscription plans like ChatGPT Pro or API integration.

Benchmark Comparison

Benchmark	DeepSeek R1	Llama 3.2	OpenAI o1 (ChatGPT)
Mathematics	~90%+ accuracy	Strong in larger variants (e.g., 90B)	~83% on advanced benchmarks like the American Invitational Mathematics Examination
Coding	Competitive with proprietary models	Decent performance at 11B+	Top-tier debugging; ranks in the 89th percentile on Codeforces
Reasoning	Strong chain-of-thought due to RL	Varies by size; excels in vision tasks	Exceptional multi-step reasoning; surpasses GPT-4o
Multimodal Tasks	Primarily text-based	Vision-enabled at 11B+	Text and image processing capabilities
Context Window Size	128K tokens	128K tokens	200K tokens

Also read: CES 2025: Meet NVIDIA Project DIGITS, world’s first AI Super-PC with 5 interesting features

OpenAI o1 leads in reasoning tasks due to its ability to “think” before responding, while DeepSeek R1 offers competitive performance at a significantly lower cost. Llama 3.2 shines in multimodal use cases, particularly with its vision-enabled models.

What’s their cost?

DeepSeek R1: Open-source availability makes it highly cost-effective for large-scale deployments. Its pricing is significantly lower than proprietary models like o1.

Llama 3.2: Free for research purposes; smaller variants are optimised for local use on edge devices, while larger ones require substantial GPU resources.

OpenAI o1: Available through subscription plans such as ChatGPT Pro (£160/month) or API access. While powerful, it is more expensive than open-source alternatives.

Also read: OpenAI o3 model: How good is ChatGPT’s next AI version?

Model use cases

DeepSeek R1: Ideal for cost-sensitive projects requiring extensive reasoning or long-context processing (e.g., research or legal analysis).

Llama 3.2: Best suited for edge deployments or multimodal applications like image-based search or document analysis.

OpenAI o1: Excels in STEM fields such as advanced coding assistance, scientific research, and mathematical problem-solving.

Any limitations?

While DeepSeek R1 offers affordability, it lacks multimodal capabilities. Llama 3.2’s smaller variants may underperform in complex reasoning compared to larger models. OpenAI o1 requires significant computational resources due to its extended chain-of-thought processing.

Final thoughts

The choice between DeepSeek R1, Llama 3.2, and OpenAI o1 depends on specific project requirements:

• Choose DeepSeek R1 for budget-friendly deployments with strong reasoning capabilities.
• Opt for Llama 3.2 if multimodal functionality or edge optimisation is critical.
• Select OpenAI o1 for unparalleled reasoning performance in STEM fields despite its higher cost.

Each model represents a significant advancement in AI technology, catering to diverse needs across industries while pushing the boundaries of what language models can achieve.

Also read: Stargate to OpenAI: Why Elon Musk and Sam Altman are still fighting