DeepSeek V4 Pro 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 DeepSeek V4 Pro and Qwen 2.5 72B.
DeepSeek V4 Pro
DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...
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 | DeepSeek V4 Pro | Qwen 2.5 72B |
|---|---|---|
| Provider | DeepSeek | Alibaba |
| Context Window | 1,048,576 tokens | 131,072 tokens |
| Agent Suitability | 94/100 | 88/100 |
| Time to First Token (TTFT) | 280 ms | 280 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V4 Pro
$0.43
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
DeepSeek V4 Pro
$0.87
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.
DeepSeek V4 Pro Quirks & Gotchas
- ▸MoE architecture — cold-start latency on first request, use keep-alive
- ▸Best cost-performance ratio of any frontier model — strong tool calling for agentic use
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