Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Llama 3.1 405B Instruct delivers a 55% cost reduction over Perplexity Sonar Pro.
| Traffic Volume Tier | Llama 3.1 405B Instruct Monthly | Perplexity Sonar Pro Monthly | Monthly Savings by picking Llama 3.1 405B Instruct |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $4.375 | $9.75 | Save $5.375 / mo |
| 10,000 reqs/mo (Small App) | $43.75 | $97.50 | Save $53.75 / mo |
| 100,000 reqs/mo (Growth Production) | $437.50 | $975.00 | Save $537.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $4,375.00 | $9,750.00 | Save $5,375.00 / mo |
Llama 3.1 405B Instruct is 55% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Llama 3.1 405B Instruct costs $0.004375 per request compared to $0.00975 on Perplexity Sonar Pro.
Llama 3.1 405B Instruct offers a context window of 128,000 tokens (max output: 4,096), while Perplexity Sonar Pro offers 200,000 tokens (max output: 8,192).
At 100,000 requests per month, using Llama 3.1 405B Instruct saves $537.50 every month (or $6,450.00 annually) compared to Perplexity Sonar Pro.
Volume makes nano-tier models attractive — extraction rarely needs frontier reasoning. Constrain output with JSON schema modes to avoid retry loops on malformed responses. Cache shared schema instructions and few-shot examples across calls.