Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). Qwen 3.8 Max (2.4T MoE) delivers a 63% cost reduction over GPT-5.6 Sol.
| Traffic Volume Tier | Qwen 3.8 Max (2.4T MoE) Monthly | GPT-5.6 Sol Monthly | Monthly Savings by picking Qwen 3.8 Max (2.4T MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $49.10 | $131.75 | Save $82.65 / mo |
| 10,000 reqs/mo (Small App) | $491.00 | $1,317.50 | Save $826.50 / mo |
| 100,000 reqs/mo (Growth Production) | $4,910.00 | $13,175.00 | Save $8,265.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $49,100.00 | $131,750.00 | Save $82,650.00 / mo |
Qwen 3.8 Max (2.4T MoE) is 63% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), Qwen 3.8 Max (2.4T MoE) costs $0.0491 per request compared to $0.1317 on GPT-5.6 Sol.
Qwen 3.8 Max (2.4T MoE) offers a context window of 256,000 tokens (max output: 32,768), while GPT-5.6 Sol offers 1,050,000 tokens (max output: 128,000).
At 100,000 requests per month, using Qwen 3.8 Max (2.4T MoE) saves $8,265.00 every month (or $99,180.00 annually) compared to GPT-5.6 Sol.
Long-context models pay off here — compare price per 1M tokens at your true document size. Summarize once, store the result; don't re-summarize unchanged documents. For batch backfills, nightly jobs can use cache-friendly request ordering.