In enterprise use, the total cost isn't just about price per token; agentic workflows add planning, tool calls, validation and retries, which can push overall spend higher even when per-token prices are lower.

Caching and storage strategies materially affect costs; providers offer different caching economics that can change the effective token price and create incentives to switch vendors.

Pricing among major AI model providers is in a race, with Muse Spark 1.1 priced at $1.25 per million input tokens and $4.25 per million output tokens—cheaper than some flagship options but with varying discounts by model and token type.

Buyers should budget by outcomes, not just models: pair pricing with explicit price-per-outcome modeling and plan for loop orchestration, as rate cards fall but invoices hinge on workflow design and usage patterns.

The overall cost picture includes search/retrieval, vector storage, reranking, compute sessions, observability, and human review; task evaluation should be workflow-specific rather than rely on generic benchmarks.

Future vendor talks should cover median token draw per successful task, feasibility of running steps on lower-tier models without quality loss, and how tool-call replication impacts the bill when multiple metered layers exist.

Token costs depend on four independent factors—price per token, tokens per attempt, cost per successful task, and total task volume—so spend can rise even if cost per completed task drops due to higher usage.

Agent workloads drive heavy token use because contexts are resent at each loop; expect token usage to grow substantially as enterprises deploy always-on agents.