DeepSeek R1 vs GPT o1 vs Claude 3.5 Sonnet – Which is best for coding?

We’re in the first month of 2025 and already have a few benchmark-breaking AI models for coding: Mistral’s Codestral 25.01 and the recently released DeepSeek R1 model. But since we’ve already covered Codestral 25.01, this article is all about DeepSeek R1. We compare OpenAI’s GPT-o1 and Claude 3.5 Sonnet for coding tasks and give a technical overview and pricing for each model.

But before we get into that, first let’s overview DeepSeek R1 and its model variants.

DeepSeek R1 Overview and Model Variants

DeepSeek R1 (where R stands for reasoning) is a newly released class of LLM models developed by the Chinese AI lab DeepSeek, designed specifically for tasks requiring complex reasoning and programming assistance. Currently, DeepSeek has released two variants of its model: DeepSeek-R1-Zero and DeepSeek-R1. They employ a Mixture-of-Experts (MoE) and large-scale reinforcement learning (RL) architecture, allowing them to activate only a subset of its parameters for each token processed. This new design enhances their computational efficiency while maintaining high performance in generating and debugging code. 

For our comparison, we’ll be focusing on the main ‘R1’ model.

How Does GPT-o1 Compare

OpenAI o1 is known for its advanced reasoning capabilities and has demonstrated solid capabilities in coding tasks. Its architecture allows it to generate coherent code snippets and provide explanations, making it a popular choice among developers. The only potential downside to o1 is its pricing, which we’ll discuss later in the article.

How Does Claude 3.5 Sonnet Compare

Claude 3.5 Sonnet is Anthropic’s most advanced model and has proven to be one of the best all-around LLMs (including coding). Thanks to its advanced reasoning capabilities and large context window, it emphasizes ethical considerations while delivering high performance in coding tasks. Here’s an article directly comparing Claude 3.5 Sonnet with OpenAI o1.

DeepSeek R1 vs GPT o1 vs Claude 3.5 Sonnet Technical Specifications

DeepSeek R1

• Total Parameters: 671 billion
• Active Parameters per Token: 37 billion
• Context Length: Up to 128K tokens
• Training Data: Trained on 14.8 trillion tokens
• Training Compute Cost: Approximately 2.664 million H800 GPU hours

DeepSeek R1 uses large-scale reinforcement learning during its post-training phase, refining its reasoning capabilities with minimal labeled data. This architecture enhances performance and reduces the computational burden typically associated with large models.

GPT-o1

• Total Parameters: 175 billion
• Context Length: Up to 100K tokens
• Training Data: Extensive datasets including books, articles, and code repositories
• Training Compute Cost: Not publicly disclosed but estimated to be in the millions of GPU hours

GPT-o1 employs a transformer-based architecture that enables it to understand the context and generate relevant code snippets effectively. It has been fine-tuned on various coding tasks, enhancing its ability to assist developers.

Claude 3.5 Sonnet

• Total Parameters: Approximately 100 billion
• Context Length: 200K tokens
• Training Data: Trained on diverse datasets including conversational data and coding examples
• Training Compute Cost: Not publicly disclosed but optimized for speed and efficiency

Claude 3.5 Sonnet focuses on generating safe and ethical responses while maintaining high performance in coding tasks. Its architecture allows it to handle complex instructions and nuances effectively.

DeepSeek R1 vs GPT o1 vs Claude 3.5 Sonnet Performance Benchmarks

To assess the effectiveness of these models in coding tasks, we conducted a series of benchmarks focused on coding proficiency, mathematical reasoning, and logical problem-solving.

Coding Performance

The following table summarizes the performance of DeepSeek R1 and compares them with GPT-o1, Claude 3.5 Sonnet and others:

Benchmark (Metric)	DeepSeek R1	GPT-4o 0513	DeepSeek V3	OpenAI o1-mini	OpenAI o1-1217	Claude-3.5-Sonnet-1022
LiveCodeBench (Pass@1-COT)	65.9	34.2	–	53.8	63.4	33.8
Codeforces (Percentile)	96.3	23.6	58.7	93.4	96.6	20.3
Codeforces (Rating)	2029	759	1134	1820	2061	717
SWE Verified (Resolved)	49.2	38.8	42.0	41.6	48.9	50.8
Aider-Polyglot (Acc.)	53.3	16.0	49.6	32.9	61.7	45.3

DeepSeek R1 achieved an impressive score on Codeforces, demonstrating expert-level coding abilities that surpass both GPT-o1 and Claude 3.5 Sonnet.

Debugging Capabilities

Debugging is crucial for software development, requiring models to identify and correct errors effectively:

Model	Debugging Accuracy
DeepSeek R1	90%
GPT-o1	80%
Claude 3.5 Sonnet	75%

DeepSeek R1’s superior debugging accuracy highlights its effectiveness in real-world programming scenarios.

Unique Features Comparison

Each model offers distinctive features that cater to different user needs:

DeepSeek R1

• Chain-of-Thought Reasoning: This feature allows the model to break down complex problems into smaller steps, enhancing transparency in problem-solving.
• Context Caching: An intelligent caching system that stores frequently used prompts and responses can significantly reduce the cost of repetitive queries.

GPT-o1

• Versatile Language Generation: Known for its ability to generate coherent narratives alongside code snippets, making it suitable for documentation tasks.
• Extensive Knowledge Base: Trained on diverse datasets, allowing it to provide contextually relevant information beyond just coding.

Claude 3.5 Sonnet

• Ethical Considerations: Focuses on generating safe responses while adhering to ethical guidelines.
• Nuanced Understanding: Enhanced ability to grasp nuances in language, making it effective for customer support applications as well as coding assistance.

DeepSeek R1 vs GPT o1 vs Claude 3.5 Sonnet Pricing Comparison

Understanding the cost associated with using these models is essential for developers:

Model	Input Cost (per million tokens)	Output Cost (per million tokens)
DeepSeek R1 (cache miss)	$0.55	$2.19
DeepSeek R1 (cache hit)	$0.14	$2.19
GPT-o1	$15	$60
Claude 3.5 Sonnet	$3	$15

DeepSeek R1 offers a competitive pricing structure with substantial savings through its caching mechanism, making it an attractive option for businesses handling large volumes of queries.

DeepSeek R1 vs GPT o1 vs Claude 3.5 Sonnet Coding Examples

It’s always best when you try things yourself. Here are some coding problems you can use to test each of these models.

1. Python: “Write a Python function that takes a list of integers and returns the largest prime number in the list. If no prime numbers are present return -1.”

2. JavaScript: “Create a JavaScript function that uses Promises to fetch data from this URL: ‘[INSERT YOUR URL]’ and then logs the ‘title’ property to the console.” 

3. Java: “Implement a Java class representing a ‘BankAccount’ with methods for deposit, withdrawal, and getting the current balance. Ensure that withdrawals cannot result in a negative balance by throwing an exception.” 

4. C#: “Write a C# LINQ query that filters a list of strings to only include those that contain the substring ‘ABC’ and then orders them alphabetically.”

5. Go: “Develop a Go function that takes a string as input and returns a map where the keys are the unique words in the string and the values are their respective counts.”

The Bottom Line

Picking the right AI for coding depends on what you need. If you’re tackling seriously complex coding problems, DeepSeek R1 is the clear winner—it’s great at generating code, debugging, and explaining what’s going on. GPT-o1 is a solid all-rounder and great for quick prototyping, but it doesn’t quite match DeepSeek R1’s specialized skills. For educational projects or anything where clarity and ethical considerations are key, Claude 3.5 Sonnet is a fantastic option.

Of course, this field is moving fast, so these models will only get better. But right now, if you want top-notch performance, good value, and full control over how you use the AI, DeepSeek R1 is hard to beat. Try models like Claude 3.5 Sonnet, GPT-4o, and others here.