Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Llama 3.1 70B Instruct (OpenRouter) delivers a 85% cost reduction over GPT-5.3 Codex.
| Traffic Volume Tier | GPT-5.3 Codex Monthly | Llama 3.1 70B Instruct (OpenRouter) Monthly | Monthly Savings by picking Llama 3.1 70B Instruct (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $37.66 | $5.60 | Save $32.06 / mo |
| 10,000 reqs/mo (Small App) | $376.60 | $56.00 | Save $320.60 / mo |
| 100,000 reqs/mo (Growth Production) | $3,766.00 | $560.00 | Save $3,206.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $37,660.00 | $5,600.00 | Save $32,060.00 / mo |
Llama 3.1 70B Instruct (OpenRouter) is 85% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), Llama 3.1 70B Instruct (OpenRouter) costs $0.0056 per request compared to $0.0377 on GPT-5.3 Codex.
GPT-5.3 Codex offers a context window of 256,000 tokens (max output: 64,000), while Llama 3.1 70B Instruct (OpenRouter) offers 131,072 tokens (max output: 16,384).
At 100,000 requests per month, using Llama 3.1 70B Instruct (OpenRouter) saves $3,206.00 every month (or $38,472.00 annually) compared to GPT-5.3 Codex.
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.