DeepSeek V3.1 Terminus 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 V3.1 Terminus and Qwen 2.5 72B.
DeepSeek V3.1 Terminus
DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's...
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 V3.1 Terminus | Qwen 2.5 72B |
|---|---|---|
| Provider | DeepSeek | Alibaba |
| Context Window | 163,840 tokens | 131,072 tokens |
| Agent Suitability | N/A | 88/100 |
| Time to First Token (TTFT) | N/A | 280 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-09-22 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V3.1 Terminus
$0.27
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
DeepSeek V3.1 Terminus
$0.95
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 V3.1 Terminus Quirks & Gotchas
No developer gotchas reported.
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