Offline Test of a New-Generation Model: Capability Boundaries, Runtime, and Deployment Conditions

1. Test Scope and Recording Conventions

The test subject was a new model running in an intelligent coding tool environment. The test environment was offline, and the model did not access external links attached to the questions. The results therefore reflect only plain-text analysis, logical reasoning, and knowledge the model already had; they do not represent performance with web retrieval or tool calling.

There were six questions in total. Task 0 was identity confirmation. Tasks 1–5 covered event synthesis and integrated analysis, and one question contained 14 sub-questions. The original record gives only the total runtime (about 43 minutes) and the Task 0 runtime (about 1 minute). Model version, input turns, output word count, token consumption, and manual corrections were not recorded. This article marks them as “Not recorded” and does not extrapolate.

2. Identity Self-Description and Knowledge Cutoff

The model was able to provide its own model identifier and version number and to explain that it runs in the agent environment of an intelligent coding tool. Regarding its knowledge cutoff, the model stated that this information came from the system prompt of the runtime environment, not from its recollection of its own training data.

This has a direct impact on users. For tasks involving dates, the accurate date should be provided by the system prompt or external information; the model’s self-description of its cutoff should not be used as the basis. This test involved only one run, so it cannot be used to infer the performance of other models or other runs.

The model also stated that its memory of events near the knowledge cutoff is sparse—for example, it had only fragmentary impressions of specific events about two months before the cutoff. The original record did not verify the cause of this phenomenon. A common explanation is that recent corpus is limited, but this test has no evidence to support or rule out that explanation. In practical work, retrieval or external sources should be used to verify outputs for recent events.

3. Performance on Multi-Turn Event Synthesis

The 14 sub-questions covered military conflict, international diplomacy, public safety, and major events. The model presented causes, key milestones, and subsequent impacts in chronological order, and it separately marked events that had not been verified by third parties.

In one military conflict event, the model described differences in the initial statements made by both sides and added changes in the balance of strength months later, as well as different accounts of the results by various parties. It divided information sources into three categories: mainstream media reports, statements by involved parties, and third-party verification conclusions.

The original record had no standard answer, so accuracy could not be calculated. These observations show that the model can distinguish source types, but they do not show that it can correctly distinguish them for all events.

4. Runtime Data and Recording Basis

Task	Task Type	Input Turns	Output Words	Runtime	Token Consumption	Manual Correction Required
Task 0	Identity confirmation	Not recorded	Not recorded	About 1 minute	Not recorded	Not recorded
Tasks 1–5	Event synthesis and integrated analysis	Not recorded	Not recorded	About 42 minutes total	Not recorded	Not recorded
Total	6 tasks	Not recorded	Not recorded	About 43 minutes	Not recorded	Not recorded

The original record did not specify the start and end points of timing, and it cannot split the individual runtime of Tasks 1–5. The next test round should fix the timing convention, uniformly defined as the time from submitting the task to the model completing its full output.

The original record claimed that inference cost grows nonlinearly with task complexity. This conclusion has only two data points—Task 0 at about 1 minute and the total at about 43 minutes—which is insufficient to prove a nonlinear relationship. It should be treated as a hypothesis pending verification.

5. Cost Calculation and Applicable Scenarios

The total cost of a single task can be estimated with the following formula:

Single-task cost = model invocation cost + manual correction hours × manual hourly wage

Model invocation cost = input tokens × input unit price + output tokens × output unit price.

Correction rate = number of words requiring modification ÷ total number of output words.

The following thresholds are suggested starting points and need to be calibrated with the team’s own samples. When the correction rate is below 20%, a small-scale pilot can begin. When the correction rate is above 40%, use should be paused and failure causes analyzed.

This test did not cover the following candidate scenarios; they are listed only as directions to be verified:

• Code repository change analysis: Applicable when the number of changed files is within the range the team can manually review. The failure boundary is when files not provided or cross-repository dependencies are involved, in which case conclusions may miss key dependencies.
• Troubleshooting log correlation: Applicable when timestamps across service logs are unified. The failure boundary is when clocks are not synchronized or logs are missing, in which case the model may misjudge secondary effects as the root cause.
• Technical decision document generation: Applicable when historical decision records are complete. The failure boundary is when records are missing or conflict, in which case the output may fill in decisions that never existed.

6. Applicability Boundaries of Offline Results

Offline testing cannot show the following: the effectiveness of web retrieval, the reliability of tool calling, and judgments about the latest events. The following cases should be treated as high risk, and outputs require human verification:

• The event occurred near the knowledge cutoff date.
• Multiple parties give conflicting accounts, and no verifiable source is available.
• The conversation is very long, and key information may fall outside the context window.
• The output contains numbers or citations but provides no sources.

7. Implementation Checklist

1. Sample size: Complete at least 20 independent runs per task type (suggested value). Record the model version, date, and prompt for each run.
2. Task selection criteria: The input can be fully provided in a single context; the result can be checked by a person within 30 minutes; the task does not involve irreversible operations.
3. Observation metrics: First-pass acceptance rate, correction rate, median runtime, token consumption, and number of key factual errors.
4. Expansion conditions: The correction rate remains below the threshold for two consecutive statistical periods, and no key factual errors occur.
5. Rollback conditions: One unmarked key factual error occurs, or the correction rate remains above the threshold for two consecutive statistical periods.