Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Qwen3 Coder Next (OpenRouter) delivers a 69% cost reduction over Groq LPU — Llama 3.3 70B.
| Traffic Volume Tier | Groq LPU — Llama 3.3 70B Monthly | Qwen3 Coder Next (OpenRouter) Monthly | Monthly Savings by picking Qwen3 Coder Next (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $8.66 | $2.68 | Save $5.98 / mo |
| 10,000 reqs/mo (Small App) | $86.60 | $26.80 | Save $59.80 / mo |
| 100,000 reqs/mo (Growth Production) | $866.00 | $268.00 | Save $598.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $8,660.00 | $2,680.00 | Save $5,980.00 / mo |
Qwen3 Coder Next (OpenRouter) is 69% 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 $0.00866 on Groq LPU — Llama 3.3 70B.
Groq LPU — Llama 3.3 70B offers a context window of 128,000 tokens (max output: 8,192), 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 $598.00 every month (or $7,176.00 annually) compared to Groq LPU — Llama 3.3 70B.
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.