2024 has proven to be a solid year for AI code generation. The DeepSeek models, often overlooked in comparison to GPT-4o and Claude 3.5 Sonnet, have gained decent momentum in the past few months. And with the recent announcement of DeepSeek 2.5, an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct, the momentum has peaked. DeepSeek-V2.5 has been fine-tuned to meet human preferences and has undergone various optimizations, including improvements in writing and instruction. In this blog, we discuss DeepSeek 2.5 and all its features, the company behind it, and compare it with GPT-4o and Claude 3.5 Sonnet.
The Company Behind DeepSeek 2.5
DeepSeek is based in Hangzhou, China, focusing on the development of artificial general intelligence (AGI). The company aims to create efficient AI assistants that can be integrated into various applications through easy API calls and a user-friendly chat interface.
Overview of DeepSeek 2.5
DeepSeek 2.5 is a culmination of previous models as it integrates features from DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. This new version enhances both general language capabilities and coding functionalities, making it great for various applications. As per the Hugging Face announcement, the model is designed to better align with human preferences and has undergone optimization in multiple areas, including writing quality and instruction adherence. The benchmark table below proves this:
| Metric | DeepSeek-V2-0628 | DeepSeek-Coder-V2-0724 | DeepSeek-V2.5 |
| AlpacaEval 2.0 | 46.6 | 44.5 | 50.5 |
| ArenaHard | 68.3 | 66.3 | 76.2 |
| AlignBench | 7.8 | 8.7 | 9.1 |
| MT-Bench | 8.8 | 8.9 | 9 |
| HumanEval python | 84.5 | 87.2 | 89 |
| HumanEval Multi | 73.8 | 74.8 | 73.8 |
| LiveCodeBench(01-09) | 36.6 | 39.7 | 41.8 |
| Aider | 69.9 | 72.9 | 72.2 |
| SWE-verified | N/A | 19 | 16.8 |
| DS-FIM-Eval | N/A | 73.2 | 78.3 |
| DS-Arena-Code | N/A | 49.5 | 63.1 |
DeepSeek V2.5’s Key Features
- Integration of Models: Combines capabilities from chat and coding models.
- Performance Metrics: Outperforms its predecessors in several benchmarks, such as AlpacaEval and HumanEval, showcasing improvements in instruction following and code generation.
- Context Length: Supports a context length of up to 128K tokens.
How to Use DeepSeek 2.5?
DeepSeek 2.5 is accessible via both web platforms and APIs. Users can integrate its capabilities into their systems seamlessly. You can create an account to obtain an API key for accessing the model’s features.
Pricing Comparision: DeepSeek 2.5 vs Claude 3.5 Sonnet vs GPT 4o
When comparing DeepSeek 2.5 with other models such as GPT-4o and Claude 3.5 Sonnet, it becomes clear that neither GPT nor Claude comes anywhere close to the cost-effectiveness of DeepSeek. Below is a summary table highlighting the pricing for each model based on their usage costs:
| Model | Price per 1 Million Tokens | Key Features |
| DeepSeek 2.5 | $0.14 (input) – $0.28 (output) | General & coding capabilities |
| GPT-4o mini | $0.15 (input) – $0.60 (output) | General & writing capabilities |
| GPT-4o (08-06) | $2.5 (input) – $10 (output) | General & writing capabilities |
| Claude 3.5 Sonnet | $3 (input) – $15 (output) | Advanced reasoning, conversation, and coding |
This table indicates that DeepSeek 2.5’s pricing is much more comparable to GPT-4o mini, but in terms of efficiency, it’s closer to the standard GPT-4o.
DeepSeek 2.5 for Code Generation
DeepSeek 2.5 has been evaluated against GPT, Claude, and Gemini among other models for its reasoning, arithmetic, language, and code generation capabilities. It excels in generating code snippets based on user prompts, demonstrating its effectiveness in programming tasks.
The table below highlights its performance benchmarks.
DeepSeek 2.5 Example Prompts
Here are some example prompts used to test the code generation abilities of DeepSeek 2.5:
| Category | Example Prompt | Try the Prompt |
| Data Structures and Algorithms | Implement a binary search algorithm to find a target value in a sorted array of integers. | Try with DeepSeek Try with Claude Try with GPT-4o |
| Machine Learning | Train a linear regression model on the Boston housing dataset to predict the median value of owner-occupied homes. | Try with DeepSeek Try with Claude Try with GPT 4o |
| Web Development | Create an HTML form that allows users to input their name and email address, and then submit the data to a PHP script that saves the information to a database. | Try with DeepSeek Try with Claude Try with GPT 4o |
| Natural Language Processing | Write a Python function that tokenizes a given text using NLTK’s word_tokenize() function and removes stop words. | Try with DeepSeek Try with Claude Try with GPT 4o |
DeepSeek Reddit Feedback
Feedback from users on platforms like Reddit highlights the strengths of DeepSeek 2.5 compared to other models. Users have noted that DeepSeek’s integration of chat and coding functionalities provides a unique advantage over models like Claude and Sonnet.
Many users appreciate the model’s ability to maintain context over longer conversations or code generation tasks, which is crucial for complex programming challenges.
The Bottom Line
DeepSeek 2.5 is a nice addition to an already impressive catalog of AI code generation models. Its competitive pricing, comprehensive context support, and improved performance metrics are sure to make it stand above some of its competitors for various applications. The integration of previous models into this unified version not only enhances functionality but also aligns more effectively with user preferences than earlier iterations or competing models like GPT-4o and Claude 3.5 Sonnet.
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