Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Qwen3 Coder Next (OpenRouter) delivers a 91% cost reduction over GPT-5.6 Sol Pro (OpenRouter).
| Traffic Volume Tier | GPT-5.6 Sol Pro (OpenRouter) Monthly | Qwen3 Coder Next (OpenRouter) Monthly | Monthly Savings by picking Qwen3 Coder Next (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $31.04 | $2.68 | Save $28.36 / mo |
| 10,000 reqs/mo (Small App) | $310.40 | $26.80 | Save $283.60 / mo |
| 100,000 reqs/mo (Growth Production) | $3,104.00 | $268.00 | Save $2,836.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $31,040.00 | $2,680.00 | Save $28,360.00 / mo |
Qwen3 Coder Next (OpenRouter) is 91% 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 Coder Next (OpenRouter) costs $0.00268 per request compared to $0.031 on GPT-5.6 Sol Pro (OpenRouter).
GPT-5.6 Sol Pro (OpenRouter) offers a context window of 1,050,000 tokens (max output: 128,000), while Qwen3 Coder Next (OpenRouter) offers 262,144 tokens (max output: 235,929).
At 100,000 requests per month, using Qwen3 Coder Next (OpenRouter) saves $2,836.00 every month (or $34,032.00 annually) compared to GPT-5.6 Sol Pro (OpenRouter).
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.