Gemini 2.5 Pro Preview 05-06 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 Gemini 2.5 Pro Preview 05-06 and Qwen 2.5 72B.
Gemini 2.5 Pro Preview 05-06
Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...
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 | Gemini 2.5 Pro Preview 05-06 | Qwen 2.5 72B |
|---|---|---|
| Provider | Alibaba | |
| Context Window | 1,048,576 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 | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-05-07 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 2.5 Pro Preview 05-06
$1.25
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
Gemini 2.5 Pro Preview 05-06
$10.00
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.
Gemini 2.5 Pro Preview 05-06 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