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credits

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first_img OpenAI grants free API credits to ChatGPT Pro and Business

OpenAI documentation shows that eligible ChatGPT Pro and Business subscriptions can receive free API credits for building applications and agents within their own API organization. The credits are automatically issued based on the subscription, and any unused portion expires at the end of the billing cycle without carrying over; when there are valid free credits, there is no need to bind an API payment method. In terms of monthly credits, Pro's tiers of $100, $200, and $500 correspond to $100, $200, and $500 respectively; Business Standard provides $20 per paid seat, and Business Premium provides $100 per paid seat.The initial issuance is calculated proportionally based on the remaining time in the current subscription billing cycle and expires at the end of that cycle. Business API credits are shared within the workspace, with a monthly cap of $500; annual payment workspaces receive a one-time issuance of the annual credits, which expire at the end of the annual billing cycle. Users must log in to ChatGPT with an eligible subscription account, with Pro credits claimed in Settings under Billing, and Business credits claimed in the workspace billing settings or Admin Console, confirming the API organization receiving the credits.The credits cover the paid usage of associated organizations on the API Platform, including Responses API, Agents API, Decisions API, GPT-Live and Realtime API, Images API, as well as built-in tools and related costs such as web search, document search, and Code Interpreter. Using Codex via API key is not included in the benefits, and the credits will not increase the usage limits for ChatGPT or Codex that is already logged in. Free credits are prioritized over purchased credits; when exhausted and no remaining credits are available, requests will fail with HTTP 429 and error code credit_balance_exhausted.

first_img Viewpoint: The AI application layer should not be priced based on tokens, but should be anchored to "recognizable work value."

a16z partner Sarah Wang recently published an article pointing out that AI application layer products should not price based on tokens like the model layer, but rather on "recognizable work units." The article argues that token pricing anchors the value of application products to a unit whose cost is continuously declining, making it difficult for customers to predict context length, retrieval volume, or reasoning time, and improperly compares applications to raw computing power.The article suggests a tiered pricing model based on value levels: model layer priced by tokens; application layer priced by recognizable work units for customers (such as account research briefs, code modifications, completed queries), which can be encapsulated through Credits; and scenarios that are attributable and have clear value priced directly by results (such as resolved customer service conversations, qualified leads). The design of Credits should map to different levels of work difficulty to protect gross margins and distinguish "work value" from "delivery cost." The article uses Clay as an example, where its new pricing separates Data Credits (third-party data) from Actions (orchestrated work), only passing on costs for reasoning models with significant cost fluctuations without markup. The author believes that pricing anchored to value rather than computing cost allows customers to understand spending in relation to value, while also benefiting product providers in maintaining profit margins.
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