introductory
With the rapid development of AI technology, Large Language Models (LLMs) have become an important force for technological advancement.2025 Grok 3, Deepseek R1, ChatGPT o3, and Claude 3.5 are some of the high-profile AI models in the market. Developed by different teams (xAI, Deepseek, OpenAI and Anthropic, respectively), these models have their own unique design philosophies and technical strengths. In this paper, we will compare them in terms of four key dimensions: programming capability, multimodal capability, reasoning capability, and application scenarios, with the aim of providing users with a comprehensive reference to help them choose the most appropriate model for their specific needs.
1. Comparison of programming capacity
Programming capability is an important measure of how efficiently an AI model can generate code, understand programming concepts, and solve programming-related problems. This capability is particularly critical for developers, engineers, and organizations, especially in the areas of software development and automation.
Programming test prompt word: "Code for a nice ball bouncing in a circle, now change it to 100 balls instead of 1".
| Model name | Affiliated organizations | dominance | inferior | Rating (out of 100) |
|---|---|---|---|---|
| Grok 3 | xAI | - Strong mathematical reasoning and scientific computing skills, especially on the AIME 2025 test - Good support for specific programming languages (e.g. Rust) - Real-time integration of X-Platform data for dynamic tasks |
- Weak contextual memory may affect long code generation - Programming skills slightly less than top models - Some features are unlocked by subscribing to premium services |
88 |
| DeepSeek R1 | DeepSeek | - Efficient MoE architecture with excellent code completion and large project analysis - Computationally efficient for edge device deployment - Open source and low cost, cost-effective |
- Inadequate reasoning skills for long texts - Weak multimodal support limits complex tasks - Average performance on non-math/code tasks |
85 |
| ChatGPT o3 | OpenAI | - Highly versatile, excellent code generation and dialog optimization performance - Reinforced learning optimizes logical reasoning for complex quizzes - Extensive community support and documentation |
- Relatively average mathematical reasoning skills - Higher level quests need to be paid to be unlocked - Less reliance on real-time data |
90 |
| Claude 3.5 | Anthropic | - Excellent code tuning skills with the ability to modify existing code with precision - Natural fluency in language comprehension and production - Highly secure and suitable for enterprise level applications |
- Not as good at math and scientific computing as Grok 3 - Slower reasoning - Higher hardware resource requirements |
87 |
2. Comparison of multimodal capabilities
Multimodal capability refers to the ability of a model to process and generate multiple data types (e.g., text, images, audio, and video). This capability becomes increasingly important as AI applications expand into areas such as content creation, virtual assistants, and interactive media.
| Model name | Affiliated organizations | dominance | inferior | Rating (out of 100) |
|---|---|---|---|---|
| Grok 3 | xAI | - Supports real-time integration of text and X-platform data with strong dynamic analysis capabilities - Better joint understanding of images and text - Excellent code editing and generation capabilities |
- Limited depth of multimodal functionality, image processing not as good as top models - Weak external multimodal support for non-X data - Some features are unlocked by subscription |
87 |
| DeepSeek R1 | DeepSeek | - Open source and efficient, supporting text, code and basic image processing - Strong mathematical reasoning and code generation, cost-effective - Fast multimodal tasks |
- Weak image understanding and generation, lack of advanced multimodal support - Unstable performance in long context multimodal tasks - Non-textual modals are slightly more basic |
84 |
| ChatGPT o3 | OpenAI | - Comprehensive multimodal support with strong text, image and even video processing capabilities - High quality of generation and excellent logical reasoning - Ecologically rich and widely used |
- Advanced multimodal features require payment and may limit free users - Low dependency on real-time data - Higher demand for computing resources |
92 |
| Claude 3.5 | Anthropic | - Natural and smooth text and image understanding with high security - Outstanding code tuning capabilities in multimodal tasks - Strong handling of complex contexts |
- Lack of support for multimodal extensions such as video - Slower processing speed - Higher hardware requirements affect deployment flexibility |
89 |
3. Comparative reasoning
Reasoning ability includes the ability to think logically about models, problem solving, and decision making. This ability is critical for applications that require complex analysis (e.g., scientific research, financial forecasting, and strategic planning), as described below using thePhysics puzzles (marble and cup test)
The prompt I use: "Assume the laws of physics on Earth. A small marble is placed in a regular cup and the cup is placed upside down on a table. Then someone picks up the cup and puts it in the microwave. Where is the ball now? Explain your reasoning step by step.
| Model name | Affiliated organizations | dominance | inferior | Rating (out of 100) |
|---|---|---|---|---|
| Grok 3 | xAI | - Extremely strong mathematical reasoning skills and outstanding performance on the AIME 2025 test - Excellent scientific problem solving skills - Real-Time Data Integration to Enhance Dynamic Reasoning |
- Slightly less coherent reasoning in long contexts - Slightly less complex reasoning in non-mathematical domains - Some features are unlocked by subscription |
90 |
| DeepSeek R1 | DeepSeek | - MoE is architecturally efficient and excels in math and code-related reasoning - Open source and low computational cost - Rapid processing of short reasoning tasks |
- Inadequate reasoning skills for long texts - General reasoning performance on unstructured problems - Limited support for multimodal reasoning |
86 |
| ChatGPT o3 | OpenAI | - Strong general purpose reasoning with a balance of complex question and answer and logical reasoning - Enhanced learning optimization improves the quality of reasoning - Wide applicability |
- Math reasoning slightly weaker than Grok 3 - Higher levels of reasoning need to be paid to be unlocked - Less reliance on real-time data |
91 |
| Claude 3.5 | Anthropic | - Excellent long contextual reasoning skills and in-depth understanding of complex issues - Natural language reasoning is smooth and precise - Highly secure and logical |
- Slightly less math and scientific reasoning than Grok 3 - Slower processing speed - Higher hardware requirements |
89 |
Expected Answer: Marbles fall out of the cup when lifted. - Marbles stay on the table, not in the microwave.
Results:
✅DeepSeek R1: Takes the longest to think, but masters physics and correctly explains gravity and friction.
✅Grok 3: Solid reasoning, but overly complex explanations and too much detail.
❎ChatGPT o3-mini: incorrect. Claims that the marble stays in the cup despite gravity.
reach a verdict
| performances | ChatGPT (GPT-4) | Grok 3 | DeepSeek |
|---|---|---|---|
| language understanding | Excellent, with strong semantic understanding and fluent language expression | Excellent performance, real-time data integration, and language comprehension skills | Excellent performance, but a little less so in complex Chinese contexts |
| Math/logic skills | Excellent, especially in complex logic tasks and math problem solving | Excellent performance, outstanding in AIME 2025 tests, leading in math reasoning | Strong in math and code-related logic, but slightly weaker in unstructured problems |
| multimodal support | Supports text, images and even videos with high generation quality | Supports text and images with strong dynamic data integration but limited depth | Basic multimodal support with weak image understanding |
| Reasoning and creativity | Strong reasoning skills for complex quizzes and innovative tasks with logical rigor | Reasoning skills are outstanding, scientific problem solving is excellent, but long contexts are a little weak | Reasoning is efficient and suitable for short tasks, but long textual reasoning and innovativeness are limited |
Ultimately, the choice of model depends on the specific requirements of the task. Users should pick the most appropriate AI model based on real-time data requirements, programming complexity, multimodal interactions, and ethical constraints.
