GPT-6 Luna vs Qwen 2.5 72B
Compare GPT-6 Luna vs Qwen 2.5 72B API pricing, token costs, context windows, and latency. See which model is cheaper and better for your stack.
GPT-6 Luna charges $0.10/1M input and $0.50/1M output tokens, compared to Qwen 2.5 72B at $0.35/1M input and $0.40/1M output. GPT-6 Luna is 3.5x cheaper on input tokens. In reasoning benchmarks, GPT-6 Luna holds the higher AA Intelligence Index rating. For latency-critical streaming, throughput figures are not yet measured for at least one of these models.
Full Metric & Specification Comparison
| Metric | GPT-6 Luna | Qwen 2.5 72B | Advantage |
|---|---|---|---|
| Provider | OpenAI | Alibaba | - |
| Input Token Cost (1M) | $0.100 | $0.350 | GPT-6 Luna |
| Output Token Cost (1M) | $0.500 | $0.400 | Qwen 2.5 72B |
| Context Window | 1,050k tokens | 128k tokens | GPT-6 Luna |
| AA Intelligence Index | 28/100 | Unrated | Unrated |
| Streaming Latency | Fast | Unrated | - |
Key Architectural Takeaways
- Input Token Cost: GPT-6 Luna is $0.10/1M vs Qwen 2.5 72B at $0.35/1M
- Output Token Cost: GPT-6 Luna is $0.50/1M vs Qwen 2.5 72B at $0.40/1M
- Context Window: GPT-6 Luna supports 1,050k tokens vs Qwen 2.5 72B with 128k tokens
- Benchmark Intelligence Rating: not rated for one or both models
- Inference Latency: GPT-6 Luna is rated Fast vs Qwen 2.5 72B rated null
Calculate Your Team's Exact Bill Difference
Drag the sliders below to see real-time cost divergences based on your daily token consumption.
Deploying GPT-6 Luna over Qwen 2.5 72B saves $3.00/mo ($36/yr) at this volume.
Frequently Asked Questions
Is GPT-6 Luna cheaper than Qwen 2.5 72B?
GPT-6 Luna is significantly more economical, charging $0.10/1M input compared to $0.35/1M for Qwen 2.5 72B.
Which model should I choose for production?
If your primary constraint is API cost and high query throughput, choose GPT-6 Luna. If you require peak reasoning accuracy and complex tool use, GPT-6 Luna is recommended.
How does context window compare between GPT-6 Luna and Qwen 2.5 72B?
GPT-6 Luna offers the larger context window (1,050k tokens), allowing you to pass significantly larger codebases or documents without chunking.
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