GPT-3.5 Turbo vs Qwen 2.5 72B
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for GPT-3.5 Turbo and Qwen 2.5 72B.
GPT-3.5 Turbo
GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks. Training data up to Sep 2021.
Qwen 2.5 72B
Qwen 2.5 72B is Alibaba Cloud's flagship open-weight large language model from the Qwen 2.5 generation, delivering GPT-4-class performance across general reasoning, coding, mathematics, and multilingual tasks with strong Chinese-language superiority. It supports a 131,072-token context window and is available under a permissive Apache 2.0 license for both research and commercial use, making it one of the most popular open-weight alternatives to Llama for bilingual applications.
Technical Specifications
| Specification | GPT-3.5 Turbo | Qwen 2.5 72B |
|---|---|---|
| Provider | OpenAI | Alibaba |
| Context Window | 16,385 tokens | 131,072 tokens |
| Agent Suitability | N/A | 88/100 |
| Time to First Token (TTFT) | N/A | 280 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2023-05-28 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
GPT-3.5 Turbo
$0.50
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
GPT-3.5 Turbo
$1.50
Qwen 2.5 72B
$0.80
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
GPT-3.5 Turbo Quirks & Gotchas
No developer gotchas reported.
Qwen 2.5 72B Quirks & Gotchas
- โธStrong bilingual (ZH/EN) performance โ best open model for Chinese-language tasks
- โธSelf-hostable via vLLM or Ollama with 4-bit quantization