目錄
The information in this article is current as of August 2026. Notion Credits and model pricing may change as products and pricing structures are updated.
On July 30, 2026, OpenAI reduced the API price of GPT-5.6 Luna by 80%, while Terra received a 20% price cut. After the announcement, a Notion user contacted customer support directly and asked whether the lower cost of the underlying models would lead to lower pricing for Custom Agents.
Notion support initially replied that there were no plans to adjust the pricing. After the conversation was shared on Reddit, another reply appeared in the same discussion thread from someone claiming to represent the Notion team. The response stated that Notion had adjusted the relevant pricing following OpenAI’s Luna price reduction and acknowledged that the earlier customer support response had been incorrect.
As of August 2, 2026, Notion had not published a separate, comprehensive pricing announcement. What can currently be confirmed is that Notion says it has adjusted Luna-related costs, while the publicly listed purchase price remains US$10 per 1,000 Credits.
What makes this customer support controversy genuinely confusing is the question of what Notion Credits actually represent. The purchase price of 1,000 Credits, the number of Credits consumed by each Agent task and the API cost Notion pays to model providers are three different figures.
When an upstream model becomes cheaper, users may not see a direct reduction in the retail price of Credits. Instead, the change may be reflected in the number of Credits deducted for each task.
This article explains the full details of OpenAI’s price reduction, how Notion Credits are calculated and the cost-control measures workspace administrators can take now.
OpenAI’s Price Cut and Notion’s Customer Support Correction: A Complete Timeline
GPT-5.6 Luna Drops 80%, While Terra Drops 20%
On July 30, 2026, OpenAI announced new API pricing for the GPT-5.6 model family.
Luna’s input price fell from US$1 to US$0.20 per million tokens, while its output price dropped from US$6 to US$1.20 per million tokens. Both input and output pricing decreased by 80%.
Terra’s input price fell from US$2.50 to US$2 per million tokens, while its output price declined from US$15 to US$12 per million tokens, representing a 20% reduction.
The input and output pricing for the flagship Sol model remained unchanged.
| GPT-5.6 Model | Previous Input Price | New Input Price | Previous Output Price | New Output Price | Reduction |
|---|---|---|---|---|---|
| Luna | US$1 | US$0.20 | US$6 | US$1.20 | 80% |
| Terra | US$2.50 | US$2 | US$15 | US$12 | 20% |
| Sol | US$5 | US$5 | US$30 | US$30 | No change |
All prices above are calculated per million tokens.
OpenAI stated that the price reductions resulted from efficiency improvements across the models, inference systems, hardware routing, service software and context-management processes.
These improvements reduced the time, token usage and computing resources required to complete the same tasks, allowing part of the resulting cost savings to be reflected in API pricing.
GPT-5.6 Terra Is Already Used for Personal Agent Tasks in Notion
OpenAI’s price announcement directly cited test results from Hoda Noorian of Notion’s AI product team.
Notion found that Terra was suitable for workspace question answering within personal agents, as well as clearly scoped tasks where response speed was particularly important.
According to Notion’s internal evaluation, Terra delivered quality comparable to GPT-5.5 for these tasks, while costing approximately half as much per task and completing the work 60% faster.
This confirms that Notion has incorporated GPT-5.6 Terra into some Agent tasks. However, it does not mean that all Custom Agents always use Terra.
Notion’s Agent settings include an Auto mode and also allow users to select a specific model.
When Auto is selected, Notion chooses a model according to the content of each request. Selecting a specific model is more appropriate for workflows that require consistent response characteristics or controlled testing conditions.
Notion Support Initially Said There Would Be No Price Change, but the Reddit Thread Was Later Corrected
A user on Reddit’s r/Notion community said they had contacted Notion support to ask whether Custom Agents pricing would change after OpenAI reduced its prices.
Notion support initially replied that there were no current plans to adjust the pricing.
Another response later appeared in the same Reddit thread, stating that Notion had already adjusted the relevant pricing following OpenAI’s Luna price reduction and that the earlier support response had been a communication error.
Because this statement currently appears only in a Reddit comment, and Notion has not published a separate announcement or a revised Credits conversion table on its official website, it would be inaccurate to claim that Notion Credits have received a comprehensive price reduction.
A more precise description is that Notion says its Luna-related pricing has been adjusted, while the publicly listed purchase price remains US$10 per 1,000 Credits.
The change is more likely to appear in the number of Credits deducted when an Agent uses Luna, rather than through an 80% reduction in the retail price of 1,000 Credits.
How Are Notion Credits Calculated? US$10 per 1,000 Credits Is Not the Same as the API Cost
Credits Are a Pricing Unit Created by Notion
Notion Credits are the billing units used by Custom Agents, Workers and certain Notion AI features that exceed the usage included in a workspace plan.
Credits are shared across the entire workspace. Regardless of which member creates or runs an Agent, the usage is deducted from the same workspace balance.
The current publicly listed price is US$10 per 1,000 Notion Credits. Credits are available for purchase only by Business and Enterprise workspaces.
When a workspace runs out of Credits, Custom Agents stop running until the Credits reset or an administrator purchases additional Credits.
This pricing cannot be directly converted into OpenAI API costs.
Notion Credits may cover more than the token cost of text input and output. They may also include database searches, page retrieval, tool calls, permission checks, execution records, error retries and Notion’s own Agent runtime infrastructure.
In other words, an 80% price reduction for OpenAI’s Luna model does not mean that every Notion Agent task becomes 80% cheaper.
If a single execution also uses other models, reads large amounts of data or calls several external tools, the underlying model may represent only part of the total cost.
Four Factors Determine How Many Credits Each Task Uses
According to Notion, Credits usage is affected by four main factors: the amount of data read, the number of steps, execution frequency and model selection.
The more pages an Agent searches and the larger the databases it scans, the more content it must process during each execution.
A workflow that performs several consecutive searches, makes decisions, creates pages, updates properties and sends Slack messages will also use more Credits than a task that simply classifies a single record.
Schedules and triggers also affect monthly costs.
An Agent that runs once per day will execute approximately 30 times per month. An Agent triggered by every database update may run hundreds of times.
Even when the cost of each task is relatively low, a high execution frequency can quickly consume the available Credits.
Model choice also affects usage.
Notion states that more advanced models generally require more Credits. In most situations, it recommends starting with Auto so that the system can select a model according to the task.
This means that locking every Agent to the cheapest model is not necessarily the most economical approach. If the model lacks the required capabilities and repeatedly retries the task, it may ultimately consume more steps and Credits.
Notion Has Published Estimated Cost Ranges but No Fixed Conversion Table
Notion has not published a fixed conversion table showing figures such as the number of Credits deducted for reading one page or making one tool call.
However, it has provided estimated per-run costs for several common types of Custom Agents.
| Custom Agent Type | Official Estimated Cost per Run | Approximate Runs per 1,000 Credits |
|---|---|---|
| Workspace question answering | US$0.03–0.11 | Approximately 90–333 |
| Task classification and assignment | US$0.05–0.15 | Approximately 65–190 |
| Status update reports | US$0.08–0.18 | Approximately 57–133 |
| Email classification | US$0.04–0.10 | Approximately 100–250 |
| Daily briefing | US$0.10–0.30 | Approximately 33–100 |
These figures are estimates only.
Agents performing the same general type of task may still have different costs depending on their data scope, selected model, number of steps and execution frequency.
Notion also states that the actual usage shown in the Credits dashboard after each completed execution is the most accurate reference.
The company has additionally said that these cost ranges are based on current usage data and that costs are expected to fall gradually as Agent execution becomes more efficient.
This also indicates that Notion may pass on pricing improvements by reducing the Credits consumed per task rather than by lowering the retail price of 1,000 Credits.
How Can You Reduce Notion Credits Spending? Check These Four Areas First
Compare Auto and Specific Models Using the Same Set of Tasks
For most Agents, start by keeping Auto enabled and preparing several fixed test cases.
Compare the results, execution speed and Credits consumed during each run.
Tasks with clear rules, such as classification, data extraction and fixed-format organization, can be tested with Luna or another lightweight model.
Tasks involving reasoning across multiple databases, long-form analysis or ambiguous instructions may still require a more capable model.
Do not evaluate only the cost of a single run.
If a cheaper model frequently misses data, produces the wrong format or repeatedly retries the task, its total Credits usage may not actually be lower.
Limit Triggers to Events That Genuinely Require the Agent to Run
When Credits are being consumed too quickly, the first thing to examine is usually not the prompt, but the trigger conditions.
If an Agent runs every time a new Slack message appears, every time a database is updated or whenever an email arrives, it can generate a large number of unnecessary executions.
Triggers can instead be limited to cases where the Agent is mentioned, a specific property changes, a designated label appears or a defined filter condition is met.
Notion also recommends using clear, high-signal triggers to prevent Agents from running in response to every minor update and wasting execution capacity.
Restrict Which Pages and Databases the Agent Can Search
When an Agent is allowed to search the entire workspace, each task may involve reading large amounts of irrelevant content.
If the necessary data sources are already known, specify a single page, a limited number of databases or particular Slack channels.
Narrowing the data scope means the Agent does not need to search the entire workspace before deciding which information is relevant.
Execution speed and Credits usage will usually become more stable.
Notion recommends starting with a small scope and gradually adding more data sources after confirming that the results are reliable.
Remove Repeated Searches, Loops and Unnecessary Steps
If an Agent repeatedly searches the same page, checks the same information several times or continues retrying because of insufficient permissions, its instructions or tool permissions probably need to be revised.
When creating an Agent, clearly define the data sources, completion conditions, output location and what the Agent should do when no relevant information is found.
Independent sources that can be read simultaneously can also be processed in parallel, preventing the Agent from waiting for one source to finish before beginning the next.
A very long workflow can be divided into several Agents according to its objectives, but it should not be split solely to reduce Credits.
If every Agent must reread the same data, the total cost may actually increase.
The first priority should be removing ineffective triggers, repeated searches and unnecessary tool calls.
After OpenAI reduced the price of GPT-5.6 Luna by 80%, Notion’s original customer support response led users to believe that Custom Agents pricing would not change at all.
A correction later appeared in the discussion thread, stating that Luna-related pricing had been revised. However, Notion has still not published a before-and-after comparison of Credits consumption.
At this stage, the fact that 1,000 Credits still costs US$10 is not enough to determine whether Notion has passed the model price reduction on to users.
The more important question is whether the same Agent, performing the same task with the same model settings, now consumes fewer Credits per run.
Workspace administrators can maintain several fixed test cases and record the selected model, data scope, number of steps and Credits used for each execution.
Until Notion publishes a complete conversion table, these real execution records will provide a more accurate estimate of the workspace’s actual costs than the company’s average cost ranges.
Frequently Asked Questions
Since May 4, 2026, Custom Agents have been offered as an additional paid service outside the standard workspace plan.
Notion Credits are priced at US$10 per 1,000 Credits and are available only for Business and Enterprise plans.
Notion Credits are an abstract billing unit for Notion’s own execution layer and are not directly linked to the current price charged by a particular model provider.
Changes to model costs may be reflected in the number of Credits used per execution rather than in the retail price of 1,000 Credits.
Notion has not published a fixed official conversion table.
Usage depends on task complexity, the number of tools called, the selected model, the amount of data processed and the number of execution steps.
The most accurate figure is the actual Credits usage shown in the workspace dashboard after each run.
Yes.
The default Auto mode automatically selects an appropriate model, but users may also lock an Agent to a specific model, including supported Claude, GPT and Gemini models, to control costs or maintain consistent behavior.
No.
Credits reset each month and do not accumulate. Any unused Credits expire when the new billing cycle begins.