We tested 5 top AI models—here’s the best for multimodal

So, what's the best AI model for your next project?

With a growing lineup of advanced LLMs, developers have more options than ever—which can make it tough to know which is the right one.

OpenAI, Meta, Google, and DeepSeek are all pushing the boundaries of what AI models can do, but each model has its own trade-offs. Some excel in multimodal capabilities, others in raw reasoning power, efficiency, or cost-effectiveness.

Understanding the strengths and applications of GPT-o1, Llama 3.3, Google Gemini 2.0, DeepSeek V3, and DeepSeek R1 could help you determine which model will work best for your specific needs—and give you a competitive edge in building smarter, more efficient AI-powered applications.

In today's breakdown, we'll cover:

• What sets these 5 models apart—their strengths, weaknesses, and where they shine

• Benchmarks & performance comparisons—accuracy, speed, scalability, and cost

• Real-world use cases—which models are best for coding, reasoning, multimodal AI, and more

• Key trends shaping AI in 2025—AGI development, open-source momentum, and enterprise adoption

Lots to cover today, so let's get to it.

Overview of the 5 competitors

We've chosen to compare these specific models for their balance of performance, accessibility, and greater market share. 

The AI titans of 2025—GPT-o1, Llama 3.3, and Gemini 2.0—aren’t just tools; they’re ecosystems redefining how humans interact with technology. Each model has carved out its niche, bringing unique strengths and innovations to the table.

Let’s dive deeper into what makes each of these powerhouses stand out.

1. GPT-o1

GPT-o1 is OpenAI’s most advanced released model, succeeding GPT-4 Turbo, and focuses on delivering unparalleled accuracy and deep reasoning.

It's designed for complex, context-rich tasks, and builds on the strong foundation of its predecessors with an expanded training dataset and improved optimization techniques. 

Key features:

• Enhanced reasoning capabilities, making it a top performer in benchmarks like MMLU scoring 78.2% and surpassed GPT-4o in 54 out of 57 subcategories. With its Chain of Thought (CoT) reasoning, GPT-o1 can break down complex problems into intermediate steps, improving outcomes for tasks like mathematical proofs, coding logic, and strategic planning.

• Superior programming and coding proficiency, ranking in the 89th percentile (compared to GPT-4’s 11%). 

• Optimized for scalability, allowing it to handle high-demand applications with minimal performance degradation.

Although GPT-o1 excels in accuracy and reasoning, it also has several limitations.

Limitations:

• GPT-o1 demands significant computational power, which can limit accessibility for smaller users or organizations without robust infrastructure​.

• The model’s focus on deep reasoning and deliberation results in slower response times.

• GPT-o1 is integrated into premium tools like ChatGPT Pro, which costs $200/month, targeting advanced users or professionals who need high-end AI capabilities for complex tasks​.

If you want to learn more about OpenAI and its usage in natural language processing (NLP), check out the following courses:

2. Llama 3.3

Llama 3.3's focus is on cost efficiency and optimized performance for text-based applications.

The 70B parameter Llama 3.3 is designed to be lightweight and achieves near-parity with much larger models like Llama 3.1's 405B version in benchmarks such as MMLU and HumanEval. Llama models are open-source, catering to developers and researchers seeking flexibility and customization. The previous version, Llama 3.2, integrated multimodal capabilities and mobile optimization, allowing seamless deployment on edge devices.

Key features:

• Multimodal enhancements enabling simultaneous processing of text and images.

• Advanced memory optimization for better performance on limited hardware.

• Improved adaptability, making it suitable for specialized industries like healthcare and logistics.

Limitations:

• High computational demands make it less accessible to smaller organizations or individual developers without advanced infrastructure.

• While Llama 3.3 has multilingual capabilities, its performance in less common languages can lag behind competitors.

Additional insight: Llama 3.2’s edge deployment efficiency is a major milestone, enabling AI applications in remote areas with limited connectivity or computing power.