Llama 3.1 405B vs Qwen2.5 VL 72B Instruct
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 405B and Qwen2.5 VL 72B Instruct.
Llama 3.1 405B
Llama 3.1 405B is Meta's largest open-weight language model and one of the most capable openly available models in the world. With 405 billion parameters, it achieves performance competitive with GPT-4 and Claude Opus across benchmarks spanning general knowledge, mathematics, coding, and multilingual tasks. Llama 3.1 405B is released under Meta's custom commercial license, supporting broad use cases including deployment via major cloud providers (AWS, GCP, Azure) and self-hosted inference with multi-GPU configurations.
Qwen2.5 VL 72B Instruct
Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
Technical Specifications
| Specification | Llama 3.1 405B | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Provider | Meta | Alibaba |
| Context Window | 131,072 tokens | 131,072 tokens |
| Agent Suitability | 90/100 | N/A |
| Time to First Token (TTFT) | 550 ms | N/A |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-07-23 | 2025-02-01 |
API Pricing Comparison
Input Price per Million Tokens
Llama 3.1 405B
$0.80
Qwen2.5 VL 72B Instruct
$0.80
Output Price per Million Tokens
Llama 3.1 405B
$0.80
Qwen2.5 VL 72B Instruct
$1.00
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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 3.1 405B Quirks & Gotchas
- ▸Massive model — requires 8× A100 80GB for FP16 inference
- ▸Available via Together AI, Fireworks, and Bedrock as managed API
Qwen2.5 VL 72B Instruct Quirks & Gotchas
No developer gotchas reported.