Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Llama 3.1 405B Instruct delivers a 36% cost reduction over GPT-5.4 Workhorse.
| Traffic Volume Tier | GPT-5.4 Workhorse Monthly | Llama 3.1 405B Instruct Monthly | Monthly Savings by picking Llama 3.1 405B Instruct |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $43.80 | $28.00 | Save $15.80 / mo |
| 10,000 reqs/mo (Small App) | $438.00 | $280.00 | Save $158.00 / mo |
| 100,000 reqs/mo (Growth Production) | $4,380.00 | $2,800.00 | Save $1,580.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $43,800.00 | $28,000.00 | Save $15,800.00 / mo |
Llama 3.1 405B Instruct is 36% 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 405B Instruct costs $0.028 per request compared to $0.0438 on GPT-5.4 Workhorse.
GPT-5.4 Workhorse offers a context window of 256,000 tokens (max output: 32,768), while Llama 3.1 405B Instruct offers 128,000 tokens (max output: 4,096).
At 100,000 requests per month, using Llama 3.1 405B Instruct saves $1,580.00 every month (or $18,960.00 annually) compared to GPT-5.4 Workhorse.
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.