Standard file conversion reference
103.39 USDCalculated based on original reported rate snapshot; exact total of $103.3928, not actual payment.
Real Delivery · Desensitization Example
Codex usage Cost Diagnosis
Compilation of real delivery reports based on sample users. Statistical records and conditional calculations are presented separately; all dollar values follow the original reported Standard rate snapshot and do not represent actual deductions or consumption of subscription credits.
Original report specimen (Chinese)Redacted · Excerpt
Historical rate conversion, not actual bill payment 01 / 03
Let’s look at the conclusion first 01 / CONCLUSIONS
Key findings
625,898,943 tokens and 2,665 usage events were recorded in the window. The number of events does not equal the number of requests; the report does not capture actual expenditures, subscription tiers, or credit deductions.
Conversion ratio of miss input
27.67 %only accounts for 3.16% of the total tokens, but accounts for 27.67% of the rate conversion value, which deserves priority inspection.
The conditions for two cooperations are reduced
39.86 %Recalculated under specified shifting and compression assumptions, not verified by adjusted usage or actual billing.
Fee Structure 02 / OVERVIEW
Dosage and conversion structure
Total volume is 625,898,943 tokens (approximately 626 million). Cache hit rate is based on input tokens. The model bar chart shows shares of the rate-based estimate, not shares of token volume. The October 14 snapshot ends at 05:28.
Model rate conversion ratio (non-token ratio)
gpt-6.1-sol$101.8029
98.46%
gpt-6-luna$1.5899
1.54%
Evidence and methodology 03 / FINDINGS
Discovery and data basis
The following numbers are from a summary of the specified delivery report. There is no original row-level record, so there is no claim to have independently verified the call-by-call.
01 Missed input: small quantity proportion, high conversion weight
High priority＋
The miss input is 19,790,282 tokens, accounting for 3.16% of the total; the rate conversion value is $28.6067, accounting for 27.67% of the total. The absolute number of misses should be reviewed next time, not just the hit rate.
19,790,282 token · $28.6067 · Accounting for 27.67% of the converted value
02 Cache read input: hit rate and cost ratio should be looked at separately
Medium priority＋
The cache read input is 603,424,256 tokens, which is converted to $54.1922, accounting for 52.41% of the converted value. The hit rate of 96.82% is calculated based on the input token and cannot be understood as a 96.82% decrease in the total cost.
603,424,256 token · $54.1922 · Input hit rate 96.82%
03 Output: only accounts for 0.43% token, and the conversion ratio is still 19.92%
Medium priority＋
The output is 2,684,405 tokens, equivalent to $20.5939. The three types of tokens are added together to get 625,898,943, and the total converted value of the three types is $103.3928.
2,684,405 token · $20.5939 · Accounting for 19.92% of the converted value
04 The conversion value is concentrated in the main model, and the quality still needs to be checked when downgrading.
High priority＋
gpt-6.1-sol records 2,079 events, accounting for 78.01% of the number of events, 88.05% of the total tokens, and 98.46% of the converted value; gpt-6-luna has 586 events, 11.95% of the token, and 1.54% of the converted value. There is no specific task content in the summary, and tasks that are necessarily suitable for downshifting cannot be directly listed.
Sol: $101.8029 · Luna: $1.5899 · Total: $103.3928
05 Peak day and doomsday snapshots cannot be directly compared on a full day basis.
Medium priority＋
On October 9, it was 162,743,800 tokens, accounting for approximately 26% of the total window amount. On October 14, there were only 1,959,258 tokens and 16 events before 05:28; it cannot be declared that the daily usage has dropped significantly based on this. The average daily volume for the entire six days was 103,989,948 tokens.
Peak: 10.09 · Doom: 10.14 05:28 Snapshot · Interpret the full day and snapshot separately
06 The missing original records and actual deductions cannot be filled in by estimation.
Low priority＋
The original report is based on the collection summary, and the original row-level records are not provided. The actual expenditure, currency, account period, subscription tier, quota window, processing mode, real number of requests, and per-request input volume are not collected; the event difference does not represent the request size, and long-context pricing cannot be determined based on this.
Data quality tip: 19 pieces of inherited metadata, 154 pieces of duplicate accumulated snapshots, 2 incomplete rounds; unable to independently verify source deduplication.
07 Rate snapshot: Use original report, do not declare current price
Low priority＋
The Standard rate used in the original report is USD/million tokens: gpt-6.1-sol input 2, cache read 0.10, output 10; gpt-6-luna input 0.10, cache read 0.01, output 0.50. Rate snapshot date is 2026-10-14, this page has not been repriced at current prices. The actual processing mode and per-request input volume are unknown, so long context pricing is not determined based on event differentials.
Historical rate snapshot ≠ actual deduction; event difference ≠ per-request input amount.
Optimization direction 04 / SUGGESTIONS
Suggestions and conditional estimates
The following are originally reported conditional scenarios and are neither realized results nor a commitment of cash savings. The rate follows the 2026-10-14 snapshot and is not recalculated based on the current price.
Give replaceable tasks to gpt-6-luna first
The original report models routing about one-third of Sol usage to Luna, using the same token volumes. The rate-based estimate falls from $103.3928 to $72.0470, a reduction of $31.3458. Start validation with formatting, simple questions and repeated edits; retain the original model for demanding reasoning. Quality effects and specific client settings have not been verified.
- Priority
- High
- Conditional estimation
- About 30.32% · $31.3458 (conditional value)
- Difficulty of implementation
- Verify replaceable tasks and output quality first
Compress unnecessary miss input
Average cache-miss input was 7,425 tokens per event, and average total input was 233,851 tokens. Under proportional assumptions, reducing cache-miss input to 50%, 30%, or 20% of its original volume reduces the rate-based estimate by 13.83%, 19.37%, or 22.13%, respectively. At 50%, the estimated amount is $89.0895. Over-compression can harm quality; assess absolute volume and output quality together, rather than hit rate alone.
- Priority
- High
- Conditional estimation
- 13.83%—22.13% (depending on assumed amplitude)
- Difficulty of implementation
- Bring background as needed and review task quality
Follow-up arrangements 05 / ACTIONS
Review plan for next period
The execution status is not fictitious. Whether the adjustment and verification are completed needs to be confirmed by subsequent data of the same measurement basis and task quality records.
01 Select tasks first and verify quality
To be verified＋
- Use actual tasks to check the output quality after shifting and context compression; tasks that really require strong reasoning will not be replaced in full.
- The original report did not verify the client setting entry, nor did it measure the impact of downshifting on quality; conditional scenarios cannot be written as execution results.
02 After 3-5 days of adjustment, collect again in the same format.
To be reviewed＋
- When the statistical window is comparable to the task volume, review whether the gpt-6-luna events are higher than 586 in this period, and whether the proportion of gpt-6.1-sol events is lower than 78.01% in this period.
- The average miss should be lower than this period's 7,425; the average input 233,851 should drop or stop rising.
- Under the comparable statistical window and task scale, use the 2,684,405 output tokens of this period as a reference; if there is a significant decrease, you need to check whether excessive compression or shifting affects the quality.
- First unify the statistical window, complete day and snapshot measurement basis, and then compare the absolute quantity and structure; daily and hourly summaries do not add up.
03 Complete the evidence that really affects the conclusion
Data gap＋
- If you want to analyze the actual payment or quota consumption, add the actual deduction, currency, account period, subscription level and quota window; this report cannot derive these values.
- To verify long context charges or specific task divisions, the actual number of requests, per-request input volume, and task type are required; event differential quantification is not a substitute for per-request data.
- The original collection materials and row-level records are not released with the public examples; only the compiled summary and condition description are retained on the public page.
Data and estimation boundaries
This is a redact sensitive informationd example of a real delivery report organized with the authorization specified by the user. The report object is only displayed as "example user". The statistical window is from 2026-10-08 to 2026-10-14 05:28 (UTC+8), and the end of the day is not a complete day. All dollar values are conversion references from the original report based on the 2026-10-14 Codex Standard rate snapshot and are not actual expenditures, subscription deductions or cash savings. The original row-level records are not provided, and the sequential calls and deduplication cannot be independently verified; the actual gear, processing mode, and per-request input volume are unknown. Conditional degradation and quality impact have not yet been verified. The daily amount may be different from the total one-time conversion value after rounding.
Start with your bill
Find the analysis basis from your usage records
Submit available billing or usage materials, describing the platform, statistical period and usage scenarios. The scope of analysis and conclusions are subject to actual data.