For AI code generation LLM models, 2024 has been a year that keeps delivering. First, Claude 3.5 Sonnet (which still stands tall among its competitors), then DeepSeek 2.5, then OpenAI o1, and now we have Qwen Team’s latest offering in Qwen 2.5. The new offering is unique because it features a distinct version for different tasks. Besides the regular Qwen2.5, there’s Qwen2.5-coder for coding tasks and Qwen2.5-math for arithmetic and logical tasks.
This article gives a detailed overview of the Qwen 2.5 models, focusing heavily on coding tasks and comparing it with its competitors like DeepSeek 2.5, Claude 3.5 Sonnet, and more.
What is Qwen 2.5?
Developed by the Qwen Team, Qwen 2.5 succeeds the Qwen 2 model family. It caters to numerous applications, from chatbots to complex data analysis tools for coding, mathematics, etc. Here are some of its most notable features:
Model Variants and Scalability
Now, this is where it gets interesting. One of the standout aspects of Qwen 2.5 is its scalability. The model comes in various sizes, ranging from 0.5 billion parameters to 72 billion parameters. This flexibility allows developers to choose a model that fits their specific computational resources and application needs. Here’s a detailed summary of the models:
- Qwen2.5: 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B
- Qwen2.5-Coder: 1.5B, 7B, and 32B on the way
- Qwen2.5-Math: 1.5B, 7B, and 72B.
Performance Benchmarks
Qwen 2.5 has achieved commendable scores across several benchmarks:
- MMLU (Massive Multitask Language Understanding): 86.8, indicating strong general understanding.
- HumanEval: The model reportedly scored around 88.2% on the MBPP benchmark, which evaluates coding abilities, indicating its proficiency in coding tasks.
- MATH Benchmark: Scoring 83.1, highlighting its capability in mathematical reasoning.
These scores place Qwen 2.5 among the top contenders in the LLM space. It outperforms DeepSeek-2.5 in most tests and even the Llama 3.1 405B in a few.
Long Context Support
Another great feature of Qwen 2.5 is its ability to handle large contexts. Supporting up to 128K tokens (with a maximum context length of 131,072 tokens), Qwen can maintain coherence over extended conversations or complex documents, making it particularly useful for applications requiring deep contextual understanding.
Multilingual Capabilities
Qwen 2.5 supports over 29 languages. It’s great for businesses and developers that wish to reach wider audiences without language barriers.
Instruction Following and Structured Data Understanding
Qwen 2.5 exhibits an improved ability to follow user instructions accurately and understand structured data formats such as JSON and tables. This feature enhances its usability in various applications, including data analysis and automated reporting.
Cost Efficiency
One of the most attractive aspects of Qwen 2.5 is its cost-effectiveness. Running the model locally can be virtually free, making it an appealing option for developers who require powerful LLMs without incurring high operational costs.
Qwen 2.5 vs Claude 3.5 Sonnet vs GPT-4o Performance Metrics
To provide a clearer picture of how Qwen 2.5 stacks up against its competitors, here’s a detailed comparison based on performance metrics:
| Benchmark | Qwen 2.5 Score | GPT-4o Score | Claude 3.5 Score |
| MMLU-pro | 71.1 | 77 | 76.1 |
| HumanEval | 86 | 91.5 | 92 |
| MATH | 83.1 | 76.6 | 71.1 |
These scores indicate that while Qwen 2.5 may not always surpass GPT-4 or Claude 3.5 Sonnet, it offers competitive performance at a fraction of the cost.
Qwen 2.5 vs DeepSeek 2.5
DeepSeek is another emerging player in the LLM market; it has gained attention with its recent update, DeepSeek 2.5. This model integrates both chat and coding capabilities effectively.
Performance Metrics
DeepSeek 2.5 has made strides in various benchmarks:
- DS-FIM-Eval: Achieving a score of 73.2, indicating solid performance in instruction-following tasks.
- DS-Arena-Code: Scoring 49.5, showcasing its coding capabilities but still trailing behind more established models like Qwen and GPT-4.
Context Length Support
Similar to Qwen 2.5, DeepSeek supports context lengths of up to 128K tokens, allowing for extensive dialogue management and complex data processing.
Pricing Model
DeepSeek’s pricing structure is another attractive feature:
- Input: Approximately $0.14 per million tokens.
- Output: About $0.28 per million tokens.
This pricing makes DeepSeek a viable option for businesses looking for cost-effective solutions without compromising on performance.
Qwen 2.5 vs Claude 3.5 Sonnet
Claude 3.5 Sonnet by Anthropic remains a strong competitor within the LLM landscape.
Performance Metrics
Claude consistently achieves high scores across various benchmarks:
- MMLU: Approximately 82, indicating solid general understanding but slightly behind Qwen and GPT-4o.
- HumanEval: Around 80%, reflecting decent coding capabilities but again lagging behind Qwen’s performance.
Use Cases
While Claude excels in conversational AI applications—often being favored for customer service automation—Qwen’s enhanced coding capabilities make it more versatile for developers needing robust programming support.
Cost Analysis
When analyzing the cost-effectiveness of these models based on their pricing per million tokens, we observe significant differences that could influence decision-making for potential users:
| Model | Price per Million Tokens (Input) | Price per Million Tokens (Output) |
| Qwen 2.5 | $0.38 | $0.40 |
| DeepSeek 2.5 | $0.14 | $0.28 |
| GPT-4o | $5.0 | $15.0 |
| Claude 3.5 Sonnet | $3.0 | $15.00 |
This table illustrates that both Qwen 2.5 and DeepSeek offer significantly lower costs compared to GPT-4o and Claude 3.5 Sonnet, making them more accessible options for businesses and developers alike.
Qwen 2.5 Real-World Applications
The practical implications of these models are vast:
- Customer Support: Models like Claude excel in creating conversational agents that can handle customer queries effectively.
- Software Development: Qwen’s strong coding capabilities make it suitable for generating code snippets or automating software development tasks.
- Data Analysis: With robust support for structured data formats, both Qwen and DeepSeek can be employed in data analytics applications where insights need to be derived from complex datasets.
Qwen 2.5 Examples to Try
Here are a few example prompts in multiple languages you can try with Qwen 2.5 and other models to see which one performs the best:
- JavaScript: “Write a JavaScript function that takes an array of strings and returns an object with the count of how many times each string appears in the array.”
- C++: “Create a C++ class to represent a rectangle, including methods to calculate the area and perimeter, and a method to check if the rectangle is a square.”
- SQL: “Write an SQL query to retrieve the names and total sales of customers from a ‘customers’ table, where total sales exceed a specified amount, ordered by total sales in ascending order.”
- Python: “Write a Python function that accepts a dictionary and returns a list of keys that have values greater than a specified threshold.”
Parting Thoughts
Qwen 2.5 stands out as a compelling alternative in the LLM market, demonstrating competitive performance across various benchmarks while being cost-effective for users who want solutions without breaking the bank. DeepSeek 2.5 also presents strong competition with its integrated model capabilities and favorable pricing structure, while Claude 3.5 Sonnet continues to excel in conversational AI but at a higher price point.
The decision is up to you, but it shouldn’t be too difficult. To try advanced models like GPT-4o, Claude 3.5 Sonnet, and DeepSeek, and enjoy no daily limitations on queries, try Bind AI Copilot. Start your free 7-day premium trial today!