Claude Opus 4.8 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 Claude Opus 4.8 and Qwen 2.5 72B.
Claude Opus 4.8
Claude Opus 4.8 is Anthropic's most capable generally available model in the Opus family. It supports text, image, and file inputs with text output, with reasoning support and a 1M-token...
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 | Claude Opus 4.8 | Qwen 2.5 72B |
|---|---|---|
| Provider | Anthropic | Alibaba |
| Context Window | 1,000,000 tokens | 131,072 tokens |
| Agent Suitability | 97/100 | 88/100 |
| Time to First Token (TTFT) | 520 ms | 280 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2026-05-27 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
Claude Opus 4.8
$5.00
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
Claude Opus 4.8
$25.00
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.
Claude Opus 4.8 Quirks & Gotchas
- ▸Best-in-class for autonomous code repair and multi-agent orchestration
- ▸Preview model — API may introduce breaking changes without notice
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