Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Qwen 3.8 Max (2.4T MoE) delivers a 74% cost reduction over GPT-5.5 Standard.
| Traffic Volume Tier | GPT-5.5 Standard Monthly | Qwen 3.8 Max (2.4T MoE) Monthly | Monthly Savings by picking Qwen 3.8 Max (2.4T MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $87.60 | $23.04 | Save $64.56 / mo |
| 10,000 reqs/mo (Small App) | $876.00 | $230.40 | Save $645.60 / mo |
| 100,000 reqs/mo (Growth Production) | $8,760.00 | $2,304.00 | Save $6,456.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $87,600.00 | $23,040.00 | Save $64,560.00 / mo |
Qwen 3.8 Max (2.4T MoE) is 74% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), Qwen 3.8 Max (2.4T MoE) costs $0.023 per request compared to $0.0876 on GPT-5.5 Standard.
GPT-5.5 Standard offers a context window of 512,000 tokens (max output: 64,000), while Qwen 3.8 Max (2.4T MoE) offers 256,000 tokens (max output: 32,768).
At 100,000 requests per month, using Qwen 3.8 Max (2.4T MoE) saves $6,456.00 every month (or $77,472.00 annually) compared to GPT-5.5 Standard.
Output is the expensive side — prefer models with cheap output for autocomplete-style calls. Cache repository context between keystrokes; diffs change far less than the full file. Measure acceptance rate: paying for output users delete is pure waste.