Llama 4 Maverick 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 4 Maverick and Qwen 2.5 72B.
Llama 4 Maverick
Meta's next-generation open weights model. Delivers premium agentic capabilities, reasoning, and tool call compliance for local or self-hosted enterprise stacks.
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 4 Maverick | Qwen 2.5 72B |
|---|---|---|
| Provider | Meta | Alibaba |
| Context Window | 1,048,576 tokens | 131,072 tokens |
| Agent Suitability | 89/100 | 88/100 |
| Time to First Token (TTFT) | 300 ms | 280 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-05-25 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Maverick
$0.15
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
Llama 4 Maverick
$0.60
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.
Llama 4 Maverick Quirks & Gotchas
- โธSelf-hostable via Ollama/Docker โ ideal for on-premise deployments
- โธRequires specific system prompt for optimal function calling reliability
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