Qwen 2.5 72B vs Qwen3 VL 32B Instruct
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 Qwen 2.5 72B and Qwen3 VL 32B Instruct.
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.
Qwen3 VL 32B Instruct
Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text...
Technical Specifications
| Specification | Qwen 2.5 72B | Qwen3 VL 32B Instruct |
|---|---|---|
| Provider | Alibaba | Alibaba |
| Context Window | 131,072 tokens | 262,144 tokens |
| Agent Suitability | 88/100 | N/A |
| Time to First Token (TTFT) | 280 ms | N/A |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-09-19 | 2025-10-23 |
API Pricing Comparison
Input Price per Million Tokens
Qwen 2.5 72B
$0.40
Qwen3 VL 32B Instruct
$0.10
Output Price per Million Tokens
Qwen 2.5 72B
$0.80
Qwen3 VL 32B Instruct
$0.42
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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.
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
Qwen3 VL 32B Instruct Quirks & Gotchas
No developer gotchas reported.