SQL Serious Mistake: Claude Sonnet 4.6's Reflection from Full Score to Zero

In this week's evaluation, Claude Sonnet 4.6 experienced a significant change from a full score to zero in a task named “SQL: Suspected Duplicate Payment Identification.” This phenomenon has attracted widespread attention, especially in the model's execution dimension. Through a detailed analysis of the original task and the model's provided answer, we can better understand the root cause of this scoring change.

Task Background and Original Answer Analysis

The task requires identifying possible duplicate payment records, with the database table structure as follows:

payments table, fields include: id, user_id, merchant_id, amount, timestamp

Claude Sonnet 4.6's original answer is:

SELECT p1.id AS first_id, p2.id AS second_id, p1.user_id, p1.merchant_id, p1.amount FROM payments p1 JOIN payments p2 ON p1.user_id = p2.user_id AND p1.merchant_i

Obviously, this SQL statement is incomplete, lacking key join conditions and ending statements, which directly leads to the query being unable to execute. It is speculated that this is the direct reason for the score dropping sharply from 100 to 0.

Analysis of Possible Error Causes

First, the incompleteness of the code is an obvious issue. From a technical perspective, this may be due to the model truncating or failing to properly end the statement when generating the SQL statement. Possible reasons include:

• Generation Strategy Issues: The model may encounter truncation problems when generating long SQL statements, leading to incomplete statements.
• Context Understanding Bias: The model failed to fully understand the task requirements, especially when involving complex join conditions.
• Insufficient Training Data: During the training process, the model may lack sufficient data to handle similar complex SQL problems.

Impact on Model Execution Dimension

This significant score drop is mainly reflected in the “Code Execution” dimension. Although other dimensions such as “Material Constraints” and “Cost-Effectiveness” have slightly improved, the mistakes at the execution level expose the model's deficiencies in certain complex tasks.

In addition, it is worth noting that the “Stability” dimension in the evaluation has also slightly declined, indicating issues with the model's output consistency in certain situations. Although this is not directly related to the SQL error, it may reflect general performance fluctuations of the model when handling variable tasks.

Conclusion and Prospects

Overall, this evaluation result reminds us that when further optimizing AI models, we should strengthen the generation and integrity verification of complex SQL statements. This not only requires improving the algorithm itself but may also need to expand the diversity and representativeness of the training dataset.

Future evaluation and development work should focus on the model's performance at the execution level, ensuring its stability and accuracy when generating complex code, to improve overall performance and practicality.

Data Source: YZ Index | Original Data