Sonar Reasoning Pro vs Kimi K2
tree_0005 · Racing game
Timeline
Arrow keys or j/k move between rounds.
Round Context
Racing game
Wayback Machine
When researching the early web presence of 1990s racing games through the Wayback Machine, historians rely in part on large-scale web crawl data donated by a separate web analytics company. Identify the organization that has been donating its crawl data since 1996 to the nonprofit digital archive that operates the Wayback Machine, and name the specific crawl dataset associated with this donation that is not publicly accessible. Explain the relationship between the donor organization, the archive, and the restricted dataset.
Answer length: 200-300 words.
Show hidden checklists
- Alexa Internet identified as the organization donating crawl data since 1996 + logical connection to supporting historical website preservation (e.g., racing game sites) via the Wayback Machine
- Alexa Crawls DR Crawl data identified as the specific collection donated by Alexa Internet + correctly described as not publicly accessible
- States that the donating organization began contributing crawl data in 1996
- Explains that the data are donated to the Internet Archive (operator of the Wayback Machine)
- Identifies the specific dataset name: Alexa Crawls DR Crawl data
- Notes that this dataset is currently not publicly accessible
- Describes the functional relationship between the donor organization and the Internet Archive, including the embargo period before data appear in the Wayback Machine
The question is anchored in the racing game domain by framing the task around researching archived racing game websites. The deep logic requires identifying the specific web analytics company that has donated crawl data since 1996 to the Internet Archive. The wide component requires aggregating multiple facts: the organization’s name, the start date of donations, the name of the specific crawl dataset, its accessibility status, and the nature of its relationship with the Wayback Machine. Answering correctly demands cross-referencing information about the donor organization, the archive, and the dataset.
Judgment
First, Deep Logic: Agent A completely failed to identify the donating organization (Alexa Internet) and provided no substantive answer. Agent B correctly identified Alexa Internet as the donor since 1996 and tied it logically to the Internet Archive and Wayback Machine. Thus, B passes Deep Logic; A fails. Second, Width/Completeness: Agent B explains the relationship between Alexa Internet and the Internet Archive, states that crawl data donations began in 1996, and notes that the dataset (Alexa Crawl) is not publicly accessible. While B does not explicitly use the full phrasing “Alexa Crawls DR Crawl data” or clearly describe the embargo period before public Wayback access, it captures the core required elements without hallucination. Agent A provides none of the required checklist items. Finally, User Experience: Agent B delivers a direct, structured, citation-supported answer with contextual background (founder connection, acquisition by Amazon, dataset restrictions). Agent A offers a refusal-style response with no attempt to synthesize relevant knowledge. In terms of helpfulness, completeness, and information density, B is vastly superior. Because Agent B correctly identified the core entity and substantially addressed the required details, while Agent A failed both Deep and Wide criteria, the correct judgment is B_MUCH_BETTER.
Sonar Reasoning Pro
Perplexity
Kimi K2
Moonshot AI