Kimi K2 vs DeepSeek V3.2
tree_0016 · Software Developers, Quality Assurance Analysts, and Testers : Occupational Outlook Handbook: : U.S. Bureau of Labor Statistics
Timeline
Arrow keys or j/k move between rounds.
Round Context
Software Developers, Quality Assurance Analysts, and Testers / Occupational Outlook Handbook: / U.S. Bureau of Labor Statistics
Computer and Information Systems Managers / Occupational Outlook Handbook: / U.S. Bureau of Labor Statistics
Using the 2024-2034 data from the U.S. Bureau of Labor Statistics Occupational Outlook Handbook, compare the employment metrics for the occupation that primarily involves designing computer applications and identifying program defects against the occupation responsible for planning and directing computer-related activities. For each of these two distinct roles, report the exact 2024 median annual pay, the projected numeric employment change over the decade, and the requirement for work experience in a related occupation.
Answer length: 200-300 words.
Show hidden checklists
- Identify Entity 1: Software Developers, Quality Assurance Analysts, and Testers (based on 'designing apps/identifying defects')
- Identify Entity 2: Computer and Information Systems Managers (based on 'planning and directing computer-related activities')
- Software Developers/QA: 2024 Median Pay is $131,450
- Software Developers/QA: Projected Employment Change is 287,900
- Software Developers/QA: Work Experience requirement is 'None'
- Computer and Information Systems Managers: 2024 Median Pay is $171,200
- Computer and Information Systems Managers: Projected Employment Change is 101,600
- Computer and Information Systems Managers: Work Experience requirement is '5 years or more'
The query uses 'Deep' logic by describing the job functions (Source A and Target 0 summaries) rather than naming the roles, forcing the agent to infer the correct OOH profiles. It utilizes 'Wide' aggregation by requesting specific data points (Employment Change numeric values) that are typically found on the detailed individual profile pages (Source A and Target 0) rather than just the summary group table (Target 1), ensuring the agent must visit and synthesize data from multiple distinct documents.
Judgment
Both agents failed to retrieve the exact numbers specified in the Ground Truth (GT) checklist, likely due to the specific '2024-2034' constraint which corresponds to a data cycle not yet fully standardized or available (the GT uses the 2023-2033 data released in 2024). However, Agent A is the clear winner for two reasons: 1. **Data Freshness & Accuracy**: Agent A provided salary figures ($132k) that are very close to the current GT ($131k), reflecting the most recent BLS data. Agent B explicitly used outdated 2022-2032 data, resulting in a salary figure ($113k) that is significantly lower than the current reality. 2. **Formatting**: Agent A followed the 'Markdown Mastery' criteria by using bolding for key constraints (pay, experience) and a clear structure. Agent B used a wall-of-text narrative that is harder to scan. Agent A is capped at 'BETTER' rather than 'MUCH BETTER' because it did not match the GT numbers exactly and hallucinated the specific '2024-2034' label (though prompted to do so), whereas the GT numbers were specific.
Kimi K2
Moonshot AI
DeepSeek V3.2
DeepSeek