Qwen3-235B vs Sonar Reasoning Pro
tree_0005 · Racing game
Timeline
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Round Context
Racing game
Wayback Machine
In researching the preservation of early racing game websites from the mid‑1990s, historians often rely on large-scale web crawl datasets that were systematically donated to a digital preservation nonprofit beginning in 1996. Identify the organization responsible for donating this crawl data, explain the name of the specific crawl collection associated with these donations, describe how the data are incorporated into the nonprofit’s web archive (including any delay before public availability), and clarify whether the underlying crawl dataset itself is directly accessible to the public.
Answer length: 200-300 words.
Show hidden checklists
- Alexa Internet + Identified as the organization that has been donating web crawl data since 1996 to the Internet Archive
- Alexa Crawls DR Crawl data + Recognized as the specific collection donated and linked to the Wayback Machine after an embargo period
- Organization name: Alexa Internet
- Donation start year: 1996
- Recipient institution: Internet Archive (Wayback Machine)
- Embargo period before data are added to the Wayback Machine
- Collection name: Alexa Crawls DR Crawl data
- Statement that the crawl dataset is not publicly accessible
The question uses the context of preserving early racing game websites to indirectly point to large-scale historical web crawl donations (Deep logic). It avoids naming the entities directly, requiring the agent to identify the organization known for donating crawl data since 1996 to the Internet Archive. The query then demands multiple distinct facts—organization, start date, collection name, embargo process, archive integration, and access status—ensuring broad information aggregation across sources (Wide scope).
Judgment
First, Deep Logic: Agent A correctly identifies Alexa Internet as the donating organization (beginning in 1996) and the Internet Archive as the recipient. Agent B refuses to answer and does not identify any relevant entity, failing Deep Logic entirely. Next, Width/Completeness: Agent A addresses nearly all checklist items—organization (Alexa Internet), start year (1996), recipient (Internet Archive/Wayback Machine), embargo/delay before public availability, and the fact that the raw crawl dataset is not publicly accessible. However, the specific collection name is imprecise (“Alexa Crawls or the Alexa Internet Archive Collection”) rather than the more exact “Alexa Crawls DR Crawl data,” making this a minor WIDE miss. Agent B provides none of the required details, failing Width completely. Finally, User Experience & Presentation: Agent A delivers a structured, citation-supported, comprehensive explanation that directly answers the query. Agent B provides a refusal with procedural commentary, offering no substantive help. Because Agent A has a minor sub-point inaccuracy (collection naming), the score is capped at BETTER rather than MUCH_BETTER. Agent B fails both DEEP and WIDE criteria.
Qwen3-235B
Alibaba
Sonar Reasoning Pro
Perplexity