Llama 3.1 70B Instruct 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 Llama 3.1 70B Instruct and Qwen 2.5 72B.
Llama 3.1 70B Instruct
Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases. It has demonstrated strong...
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 | Llama 3.1 70B Instruct | Qwen 2.5 72B |
|---|---|---|
| Provider | Meta | Alibaba |
| Context Window | 131,072 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 | 2024-07-23 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
Llama 3.1 70B Instruct
$0.40
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
Llama 3.1 70B Instruct
$0.40
Qwen 2.5 72B
$0.80
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Llama 3.1 70B Instruct Quirks & Gotchas
No developer gotchas reported.
Qwen 2.5 72B Quirks & Gotchas
- โธStrong bilingual (ZH/EN) performance โ best open model for Chinese-language tasks
- โธSelf-hostable via vLLM or Ollama with 4-bit quantization