Evidence hierarchy
Use evidence in this order:- Provider-billed (actual) costs for financial totals.
- ThinkPlatform allocated provider actuals for project, user, Agent, task, and model attribution.
- Runtime measured
estimatedCostfor diagnostic detail. - Token/request/duration proxies when direct cost is unavailable.
General formula
For each provider, model, service tier, and context tier:Metrics to calculate
- Cost share:
slice actual cost / project actual cost. - Activity share:
slice requests / project requests. - Effective cost per request:
slice actual cost / slice requests. - Effective cost per million input/output tokens: use only when token coverage is adequate and label it as blended.
- Cache ratio:
cached input tokens / input tokensonly when input tokens include cached tokens. - Output amplification:
output tokens / input tokens. - Reasoning share:
reasoning tokens / output tokensonly when the provider documents reasoning as part of output. - Daily execution rate:
requests or tasks / active days. - Schedule overhead: failed, retried, duplicated, or overlapping executions × representative task cost.
- Concentration: cost share of top model, Agent, task, user, and day.
Current pricing verification
Pricing is time-sensitive. At analysis time, verify the specific provider, model snapshot, region, service tier, and context length using official sources:- OpenAI API pricing: https://developers.openai.com/api/docs/pricing
- OpenAI prompt caching: https://developers.openai.com/api/docs/guides/prompt-caching
- OpenAI Batch API: https://developers.openai.com/api/docs/guides/batch
- OpenAI Usage API: https://platform.openai.com/docs/api-reference/usage
- Azure OpenAI pricing: https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/
- Azure OpenAI prompt caching: https://learn.microsoft.com/azure/ai-services/openai/how-to/prompt-caching
- Azure provisioned throughput billing: https://learn.microsoft.com/azure/foundry/openai/concepts/provisioned-throughput-billing
Pricing distinctions that change conclusions
- Input, cached input, cache writes, and output can have different rates.
- Long-context tiers can change rates above provider-specific thresholds.
- Standard, Batch, Flex, priority/fast, and provisioned capacity have different economics.
- Some built-in tools and storage are billed separately from model tokens.
- Azure Standard token billing and provisioned throughput unit/hour billing require different utilization analyses.
- A cheaper model that increases retries, output, or tool loops can cost more per successful outcome.
Prompt caching interpretation
Caching helps only when requests are eligible and share an exact stable prefix under the provider’s rules. Place stable instructions and common context before variable user/task content when platform behavior permits. Do not treat low cache usage as waste when:- requests are below the provider’s cache eligibility threshold;
- prompts legitimately differ at the beginning;
- traffic is too sparse or separated beyond cache lifetime;
- the provider/model/tier does not support the expected cache behavior.