Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Qwen3.5 397B A17B (OpenRouter) delivers a 26% cost reduction over GLM-5 (Z.ai).
| Traffic Volume Tier | GLM-5 (Z.ai) Monthly | Qwen3.5 397B A17B (OpenRouter) Monthly | Monthly Savings by picking Qwen3.5 397B A17B (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $12.64 | $9.36 | Save $3.28 / mo |
| 10,000 reqs/mo (Small App) | $126.40 | $93.60 | Save $32.80 / mo |
| 100,000 reqs/mo (Growth Production) | $1,264.00 | $936.00 | Save $328.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $12,640.00 | $9,360.00 | Save $3,280.00 / mo |
Qwen3.5 397B A17B (OpenRouter) is 26% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), Qwen3.5 397B A17B (OpenRouter) costs $0.00936 per request compared to $0.0126 on GLM-5 (Z.ai).
GLM-5 (Z.ai) offers a context window of 200,000 tokens (max output: 131,072), while Qwen3.5 397B A17B (OpenRouter) offers 262,144 tokens (max output: 65,536).
At 100,000 requests per month, using Qwen3.5 397B A17B (OpenRouter) saves $328.00 every month (or $3,936.00 annually) compared to GLM-5 (Z.ai).
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.