Introduction

Artificial intelligence (AI) has become a cornerstone of technological advancement, with new models continually shaping the landscape. OpenAI’s latest o1 model reportedly has the best reasoning ability of any of their models yet, bringing us excitingly closer to human-like intelligence. This article examines and compares four top-tier AI models: OpenAI's o1, Meta's LLaMA 3.2, Anthropic's Claude 3.5 Sonnet, and Google's Gemini 1.5 Pro. We will assess their performance across key metrics, including general reasoning, code generation, mathematical problem-solving, and multilingual capabilities, while also evaluating factors such as multimodal functionalities, token limits, and API pricing. By analyzing these models, we aim to provide insights into their strengths and potential applications, helping developers and businesses select the right tools for their specific needs.

Background

1. OpenAI o1 

In September 2024, OpenAI announced the o1 model series, which represents a major step forward in AI’s reasoning abilities, marking a shift towards models that "think before responding." The series includes two models, o1-preview and o1-mini, designed to handle complex tasks in science, mathematics, and coding by focusing on more in-depth problem-solving. The models have been trained to reason more effectively, spending more time analyzing tasks before responding, unlike previous versions that prioritized speed.

Testing shows that o1-preview achieves high performance on difficult academic tasks, comparable to PhD-level students. Notably, it scored 83% on an International Mathematics Olympiad qualifying exam, significantly higher than the 13% achieved by GPT-4o. The model also excels in coding, ranking in the 89th percentile in competitive programming evaluations.

While o1-preview is designed for challenging tasks, the smaller and more cost-effective o1-mini focuses on coding, offering developers an affordable option for generating and debugging complex code. Although lacking broader capabilities such as web browsing and file uploads, these models introduce advanced reasoning to OpenAI's offerings. GPT 4o remains a more comprehensive general-purpose model.

Safety measures have also been enhanced. The new safety protocols in o1-preview allow it to reason through alignment guidelines, leading to better adherence to security standards. This has been demonstrated in "jailbreaking" tests, where o1-preview significantly outperformed earlier models.

Currently, ChatGPT Plus and Team users have access to these models, with plans for further expansion.

2. Meta LLaMA 3.2 

Meta’s LLaMA series focuses on open-source, large-scale AI models. Llama 3.2, Meta’s latest addition to its family of large language models, marks a significant leap forward in the company’s AI capabilities. Announced during Meta Connect 2024, this open-source model represents Meta’s first venture into multimodal AI, capable of processing both text and images.

Building upon the foundation laid by Llama 3.1, which was released in July 2024, Llama 3.2 introduces several key enhancements. The model comes in four variants, catering to different computational needs and use cases. Two lightweight text-only models with 1 billion and 3 billion parameters are designed for mobile and edge devices, while two larger vision models with 11 billion and 90 billion parameters offer more advanced capabilities.

One of the most notable features of Llama 3.2 is its multimodal functionality. The vision models can understand charts and graphs, caption images, and locate objects based on natural language prompts. This advancement allows developers to create more sophisticated AI applications, such as augmented reality apps with real-time video understanding, visual search engines, and document analysis tools.

Meta has emphasized the user-friendly nature of Llama 3.2, stating that developers can easily incorporate its new multimodal capabilities with minimal setup. The introduction of Llama 3.2 positions Meta competitively in the AI landscape, catching up with other tech giants like OpenAI and Google, who launched their multimodal models in the previous year. 

While Llama 3.2 brings new capabilities to the table, it is worth noting that its predecessor, Llama 3.1, still holds relevance. The 405 billion parameter variant of Llama 3.1 is expected to offer superior text generation capabilities compared to the new release.

As an open-source model, Llama 3.2 continues Meta’s commitment to fostering innovation and collaboration within the AI community. By providing access to these advanced AI models, Meta aims to accelerate the development of AI applications across various industries and use cases

3. Claude 3.5 Sonnet

Anthropic's Claude 3.5 prioritizes AI safety and ethical considerations, making it a top choice for environments requiring careful handling of sensitive information. Claude 3.5 integrates safety protocols that outperform other models in preventing unintended behaviors, particularly in contexts where harmful outputs are a concern. While it excels in reasoning and ethical applications, it also performs exceptionally well in programming tasks​.

On June 21, 2024, Anthropic introduced Claude 3.5 Sonnet, the first model from the upcoming Claude 3.5 family. This model outperforms the previous Claude 3 Opus and competitive models on intelligence benchmarks, while maintaining the speed and cost-efficiency of the earlier Claude 3 Sonnet model. Available on Claude.ai and via API, Sonnet is designed for a broad range of applications, including graduate-level reasoning and nuanced content generation. Its impressive speed—double that of Claude 3 Opus—makes it ideal for tasks requiring fast, accurate responses, such as customer support and complex workflow management.

Claude 3.5 Sonnet has been particularly lauded for its advancements in both coding proficiency and vision-based tasks. It demonstrated superior problem-solving in an internal agentic coding evaluation, surpassing Claude 3 Opus by solving 64% of problems, making it highly effective for updating codebases and legacy systems. It also excels in visual reasoning, outperforming previous models in tasks like interpreting graphs and handling image-based data.

In terms of safety, Claude 3.5 Sonnet remains at ASL-2, despite its intelligence leap. It underwent thorough pre-deployment testing with AI safety institutes in the UK and US, ensuring its robustness against misuse. As Anthropic continues to expand its Claude 3.5 lineup, with future releases like Claude 3.5 Haiku and Claude 3.5 Opus, the company is committed to maintaining strong safeguards, privacy policies, and user-focused enhancements, including features like Memory, which will allow Claude to remember user preferences.

4. Gemini 1.5 Pro

Google’s AI development has seen significant progress in 2024, beginning with the release of Gemini 1.0 Ultra in February. This model marked a leap forward in multimodal AI, optimized for advanced tasks across text, images, and more. Fast forward to September, and Google has unveiled Gemini 1.5 Pro, a major upgrade in terms of efficiency and capacity. An early version of this had been released a week after Ultra in February, but these are production-ready models with higher rate limits and lower prices.

The most notable advancements in Gemini 1.5 are its use of Mixture-of-Experts (MoE) architecture and its expanded long-context window, which can now process up to 1 million tokens compared to the 32,000 tokens of Gemini 1.0. This improvement enables the model to handle significantly larger datasets, from complex codebases to full-length documents, video, and audio in a single prompt.

Performance-wise, Gemini 1.5 Pro demonstrates a comparable level to 1.0 Ultra, but with less computational overhead. Its enhanced multimodal capabilities also allow for deeper understanding and reasoning across text, images, and video. Extensive safety testing remains a key feature, continuing Google's focus on ethical AI deployment.

Developers can now experiment with this new model via AI Studio and Vertex AI, with early testers having access to the experimental long-context window at no cost. With continued optimizations, the September release of Gemini 1.5 is set to unlock broader AI applications across industries.

Technical Comparison Summary

In this section, we provide a table to summarize the key differences across these models. To clarify the comparison, these are some of the critical terms used in the evaluation:

Parameter Size: The number of learnable variables (e.g., weights and biases) within a model that are adjusted during training. Larger models with more parameters are generally more capable of solving complex problems but require more computational resources.

Token Limits: The maximum amount of text (measured in tokens, with one token being about four characters) that a model can process in a single input. Higher token limits allow for processing larger texts in conversations or tasks. Input context limit refers to the maximum number of tokens that a model can accept as input. Output Token Limit is the maximum number of tokens that a model can generate in a single request or conversation output.

Multimodal Capabilities: The ability of a model to process and understand various types of data inputs like text, images, video, or audio, enabling more dynamic and flexible interactions.

A Note on Reasoning and OpenAI o1

OpenAI’s o1 series models introduce a novel approach to complex reasoning tasks, as already discussed. These models employ ‘reasoning tokens’ to internally process and break down problems before generating a visible response. In this section, we'll cover top things to understand in this process.

Internal Reasoning: The models generate invisible reasoning tokens to "think" through problems, discarding these tokens after producing the final output. While these tokens are not visible, they still contribute to the output tokens API cost.

Context Window: Both o1-preview and o1-mini offer a 128,000 token context window, with output limits of 32,768 and 65,536 tokens respectively.

Token Management: Use the max_completion_tokens parameter to control total token generation (reasoning + visible output) and manage costs.

Space Allocation: Reserve at least 25,000 tokens for reasoning and outputs when starting to experiment with these models.

Prompting Best Practices:

• Keep prompts simple and direct

• Avoid chain-of-thought prompts

• Use delimiters for clarity

• Limit additional context in retrieval-augmented generation

Beta Limitations: During the beta phase, many chat completion API parameters are not available, including image inputs, system messages, streaming, and tools/function calling.

By understanding these aspects, developers can effectively leverage the o1 models’ advanced reasoning capabilities while optimizing for performance and cost.

Performance Benchmarking

Evaluating Large Language Models (LLMs) typically involves benchmarking their performance across various tasks such as question answering, reasoning, and language understanding during inference. Key metrics include perplexity, accuracy, and human evaluation scores. In this comparison, we examine the performance of OpenAI o1, LLaMa 3.2, Claude 3.5 Sonnet, and Gemini 1.5 Pro across five categories: general performance, code generation, math, reasoning, and multilingual capabilities.

1. General Performance

The MMLU (0-shot, CoT) benchmark evaluates the model’s ability to answer questions from the Massive Multitask Language Understanding (MMLU) dataset without prior examples (0-shot) and using Chain-of-Thought (CoT) prompting, where the model explains its reasoning.

2. Code Generation

HumanEval (0-shot) is used to assess the ability to generate correct code for programming tasks without prior examples.

3. Math

In the MATH (0-shot, CoT) benchmark, a dataset of 12,500 competition-level math problems is used to assess the model's ability to solve complex mathematical questions. For o1 models, the evaluation is done on MATH-500 which is a newer version of MATH.

4. Reasoning

Reasoning capabilities are measured with the following benchmarks:

GPQA (0-shot, CoT): The Graduate-Level Google-Proof Q&A (GPQA) consists of 448 expert-designed, multiple-choice questions that encourage models to reason without relying on web-based answers.

DROP F1 score: Assesses reading comprehension tasks that require discrete reasoning over textual and tabular information.

5. Multilingual Capabilities

The Multilingual Grade School Math (MGSM) benchmark tests the model’s ability to answer 250 math questions from the GSM8K dataset in ten different languages, evaluating its performance across various linguistic contexts.

6. Benchmark Performance Table

7. Summary of Performances

General Performance (MMLU, 0-shot, CoT): The best performance in this benchmark was achieved by OpenAI o1-preview, scoring 90.8, with a higher score of 92.3 in its work-in-progress model. The closest second was Claude 3.5 Sonnet, which scored 88.3.

Code Generation (HumanEval, 0-shot): OpenAI o1-preview led this benchmark with a score of 92.4, while Claude 3.5 Sonnet followed closely with a score of 92.0.

Math (MATH, 0-shot, CoT): OpenAI o1-preview again outperformed the other models, scoring 85.5, and 94.8 in its work-in-progress version. The second-best score came from Claude 3.5 Sonnet, with 71.1.

Reasoning (GPQA, 0-shot, CoT): OpenAI o1-preview was the top performer in this category, scoring 73.3, with an improved score of 77.3 in the work-in-progress model. Claude 3.5 Sonnet followed as the second-best, scoring 59.4.

Reasoning (DROP F1 score): In the DROP F1 score benchmark, Claude 3.5 Sonnet performed the best with a score of 87.1. The next closest model was Gemini 1.5 Pro, which scored 78.9.

Multilingual Capabilities (MGSM): Claude 3.5 Sonnet led the performance in this benchmark with a score of 91.6. OpenAI o1-preview was the next best model, scoring 90.8.

Comparative Analysis

OpenAI o1, Meta’s LLaMA 3.2, Anthropic’s Claude 3.5 Sonnet, and Google’s Gemini 1.5 Pro represent distinct advances in AI, each excelling in different areas of performance, use case applicability, and technical innovation.

1. OpenAI o1

OpenAI o1's strength lies in its advanced reasoning capabilities, making it highly effective for complex problem-solving in domains like mathematics and coding. The model demonstrates a PhD-level ability to handle difficult academic tasks, evidenced by its 83% score on the International Mathematics Olympiad qualifying exam. It also excels in coding tasks, ranking in the 89th percentile of competitive programming evaluations. However, OpenAI o1 lacks multimodal capabilities, focusing solely on text-based tasks. This positions it as the go-to model for those requiring deep reasoning over tasks like mathematical problem-solving and programming, though its limited modality range makes it less suitable for broader applications.

2. Meta LLaMA 3.2

Meta’s LLaMA 3.2 emphasizes open-source access and multimodal functionality, which allows it to handle both text and image inputs. This positions LLaMA 3.2 competitively for applications requiring sophisticated visual analysis, such as augmented reality apps or document analysis tools. With parameter variants ranging from lightweight mobile models to large-scale vision models, LLaMA 3.2 is flexible, catering to both edge device deployments and advanced computational needs. Despite its strong multimodal performance, its text-only models may not be as powerful in specialized domains like coding or advanced reasoning compared to proprietary models like OpenAI o1.

3. Claude 3.5 Sonnet

Anthropic’s Claude 3.5 Sonnet excels in safety, reasoning, and programming tasks. Its primary focus on AI safety makes it ideal for use in environments handling sensitive information or requiring adherence to stringent ethical guidelines. Claude 3.5 Sonnet also performs exceptionally in reasoning benchmarks and has been noted for its proficiency in coding, solving 64% of problems in Anthropic’s internal evaluations. Moreover, Claude’s superior multilingual performance positions it well for international applications. While Claude 3.5 lacks open-source availability, it is particularly well-suited for users prioritizing safety and multilingual tasks.

4. Gemini 1.5 Pro

Google’s Gemini 1.5 Pro distinguishes itself with its Mixture-of-Experts architecture and a 1 million token context window, allowing it to process large datasets, including long-form documents, videos, and complex codebases. This makes Gemini 1.5 Pro ideal for handling multimodal tasks across text, images, audio, and video. While it demonstrates improved performance with reduced computational overhead compared to earlier models, it does not achieve the same level of reasoning or mathematical problem-solving prowess as OpenAI o1. However, its extensive multimodal capabilities and ability to manage large-scale data make it a strong choice for industries requiring broad, cross-modal AI solutions.

Conclusion

Each of these models brings distinct advantages based on their design priorities and targeted use cases. OpenAI o1 excels in advanced reasoning and complex problem-solving but lacks multimodal functionality. LLaMA 3.2 offers flexibility and multimodal capabilities, particularly in visual tasks, while maintaining an open-source commitment. Claude 3.5 Sonnet is the leader in safety, reasoning, and multilingual processing, making it suitable for sensitive environments. Lastly, Gemini 1.5 Pro's multimodal processing capabilities and massive context window make it a powerful tool for handling large, complex datasets across various modalities.

To conclude, in this insight, we covered OpenAI o1 and comparable models in terms of reasoning and context windows. To get a comparison of the top general performance models right now, check out this article.