What should we reduce first?
In most teams, trimming unnecessary output tokens and duplicate calls delivers the fastest savings.
LLM Cost Comparison
Updated: 2/25/2026
Key bullet summary
- Total cost is driven less by list price and more by input/output token mix and request patterns.
- Blended routing generally reduces monthly variance more effectively than single-model operation.
- Track latency, retry rate, and cache hit rate early to keep overall spend stable at scale.
Cost scenarios by scale
| Scenario | Monthly token volume | Estimated monthly cost | Recommended operating approach |
|---|---|---|---|
| Small pilot | Approx. 5M tokens | Approx. $120 ~ $350 | Prioritize fast hypothesis validation with high-capability models |
| Growth stage | Approx. 30M tokens | Approx. $700 ~ $2,200 | Task-based model routing plus caching policy |
| Scale operation | Approx. 100M tokens | Approx. $2,500 ~ $8,000+ | SLA-driven multi-model strategy with quality/cost dashboards |
Cost formula frame
Monthly cost = (input token price × input tokens) + (output token price × output tokens) + retry/observability overhead
Clear conclusion
LLM cost optimization is not a single cheap model choice. It is an operating strategy that combines task routing with token control.
Data Basis
- Monthly cost ranges are estimated from official pricing documents by token volume.
- Input/output token mix and retry rate are included in total cost estimation.
- Operational logs such as latency and cache hit rate are used to evaluate variance.
Sources
Pricing and policies can change frequently, so monthly revalidation is recommended.
LLM Cost Comparison FAQ
How do we balance quality and cost?
Route critical tasks to high-capability models and repetitive workloads to cost-efficient models under SLA rules.
How often should we refresh this analysis?
At least monthly, review price, traffic, and retry trends together to correct budget drift early.
Next execution steps
Turn cost analysis into concrete model and rollout decisions.