GPT-4o-mini 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-4o-mini and Qwen 2.5 72B.
GPT-4o-mini
GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...
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-4o-mini | Qwen 2.5 72B |
|---|---|---|
| Provider | OpenAI | Alibaba |
| Context Window | 128,000 tokens | 131,072 tokens |
| Agent Suitability | 82/100 | 88/100 |
| Time to First Token (TTFT) | 150 ms | 280 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-07-18 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
GPT-4o-mini
$0.15
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
GPT-4o-mini
$0.60
Qwen 2.5 72B
$0.80
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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-4o-mini Quirks & Gotchas
- ▸Ultra-low latency — best TTFT in the OpenAI lineup
- ▸Tool calling limited to single-step — not suitable for complex agentic pipelines
Qwen 2.5 72B Quirks & Gotchas
- ▸Strong bilingual (ZH/EN) performance — best open model for Chinese-language tasks
- ▸Self-hostable via vLLM or Ollama with 4-bit quantization