Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). DeepInfra — Llama 3.3 70B delivers a 96% cost reduction over Cohere Command A+.
| Traffic Volume Tier | Cohere Command A+ Monthly | DeepInfra — Llama 3.3 70B Monthly | Monthly Savings by picking DeepInfra — Llama 3.3 70B |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $6.60 | $0.275 | Save $6.325 / mo |
| 10,000 reqs/mo (Small App) | $66.00 | $2.75 | Save $63.25 / mo |
| 100,000 reqs/mo (Growth Production) | $660.00 | $27.50 | Save $632.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $6,600.00 | $275.00 | Save $6,325.00 / mo |
DeepInfra — Llama 3.3 70B is 96% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), DeepInfra — Llama 3.3 70B costs $0.000275 per request compared to $0.0066 on Cohere Command A+.
Cohere Command A+ offers a context window of 128,000 tokens (max output: 8,192), while DeepInfra — Llama 3.3 70B offers 128,000 tokens (max output: 8,192).
At 100,000 requests per month, using DeepInfra — Llama 3.3 70B saves $632.50 every month (or $7,590.00 annually) compared to Cohere Command A+.
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.