Last updated11 Apr 2026, 3:22 pm SGT
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Deep ResearchArena
Battle replay

o3 vs GPT 5.4

tree_0017 · Handheld game console

o3 · Much Better
WIDE
3
Rounds
3 - 1
Final Score
159,103
Tokens
$1.59
Cost
Onboarding R2
Mode
← Back to battles·View source page·onboarding_battles/R2_o3-search_vs_gpt-5.4-search_tree_0017.log

Timeline

Arrow keys or j/k move between rounds.

Round 1 of 3

Round Context

Depth 2Width 2Pressure test
Logic Chain
Root

Handheld game console

Step 2

AllMusic is being updated.

Question

Insufficient information provided to generate a grounded Deep & Wide search query about handheld game consoles. Please supply specific entities and facts in the Hidden Knowledge section.

Answer length: 200-300 words.

Examiner rationale

The Hidden Knowledge section did not include any specific reasoning chain or target entities. Because the instructions require absolute grounding in the provided entities and prohibit hallucination, it is not possible to construct a valid Deep & Wide query without inventing unsupported facts. Therefore, no compliant question can be generated.

Judgment

Better
NONE
Score 1 - 0

First, Deep Logic: Both agents correctly interpret that the query lacks sufficient grounding and explain what types of concrete entities and facts are needed. Neither misidentifies the task or introduces hallucinated claims. Both pass the core logic requirement. Second, Width/Completeness: Both provide strong examples of specific consoles, technical attributes, market data, and comparison angles. Agent B adds example rewritten queries, which is helpful. However, Agent A’s suggestions are slightly more technically granular (e.g., CPU architecture, storage media, business strategy framing) and more tightly aligned with enabling a true “Deep & Wide” search. Finally, Presentation & UX: Agent A is better structured with a clean numbered list, tighter scannability, and highly relevant citations. Agent B includes more citations, but several are generic documentation links (e.g., OpenAI Help Center, Google Cloud) that do not directly ground handheld console facts, reducing citation quality. Agent B is also more paragraph-heavy, making it slightly less scannable. Conclusion: Both are good and accurate, but Agent A delivers a cleaner, more focused, and more directly useful user experience. Hence, A_BETTER.

Evolution step
Determining Next Step...
WINNER is A. Targeting Loser Failure: NONE.
Action: Pressure Test (Deep+1 & Wide+1)
No valid children. Expanding Depth...
--- [EXPANSION] Triggering Auto-Expansion: insufficient_depth (Need +1) [1/8] ---
[EXPANSION] Crawler returned 0 new nodes.
Stuck at leaf. Cannot descend.

o3

OpenAI

7.1s
To create a meaningful Deep & Wide search query about handheld game consoles, the Hidden Knowledge section should supply concrete, verifiable details that can anchor both historical breadth and technical depth. Please consider adding the following: 1. Specific console models, e.g., Nintendo Game Boy (1989), Sega Game Gear (1990), PlayStation Portable/PSP-1000 (2004), Nintendo 3DS (2011), Valve Steam Deck (2022) [1][2]. 2. Key hardware specifications you care about—CPU type, screen technology (LCD vs. OLED), battery capacity, storage media (cartridge, UMD, flash), or unique input features such as dual screens or touchpads. 3. Market or cultural milestones: worldwide sales totals (e.g., Game Boy family ≈118 million units), landmark game launches (Pokémon Red/Blue, Monster Hunter Portable 3rd), or notable regional variants. 4. Comparative performance or emulation capabilities (e.g., Steam Deck’s x86-64 architecture enabling PC library access). 5. Policy or business data: launch prices, major revisions (Game Boy Color, PSP-2000), discontinuation dates, or manufacturer strategies (Nintendo’s “blue ocean” handheld focus vs. Sony’s multimedia positioning). 6. Any emerging trends you wish to explore—Cloud gaming handhelds, FPGA-based retro devices, or sustainability concerns around lithium-ion batteries. By embedding such granular entities and facts, the search engine can pivot on precise model names, specs, and historical markers, enabling retrieval of specialized engineering documents, sales reports, developer interviews, and academic analyses rather than generic “best handhelds” lists. This grounding transforms the query from vague to targeted, ensuring both depth (technical internals) and width (cross-generation, cross-brand) across the handheld console landscape. ## References [1] Nintendo Game Boy – Wikipedia. https://en.wikipedia.org/wiki/Game_Boy [2] Valve Steam Deck – Valve Corporation product page. https://www.steamdeck.com/en/

GPT 5.4

OpenAI

9.8s
The message means a grounded “Deep & Wide” query cannot be built yet because the request only names a broad topic—handheld game consoles—without the concrete anchors needed for precise retrieval. To make the search actionable, the Hidden Knowledge section should include specific entities, date ranges, and factual constraints such as manufacturers, devices, launch years, regions, technical attributes, market events, and comparison goals [1][2]. Useful entities would include brands and systems like Nintendo Game Boy, Game Boy Advance, Nintendo DS, Nintendo Switch, Sony PlayStation Portable, PlayStation Vita, Sega Game Gear, Atari Lynx, Valve Steam Deck, and ASUS ROG Ally [3][4][5][6]. Useful facts would include whether the search is about release chronology, sales, hardware specifications, battery life, display type, backward compatibility, software libraries, emulation, or competitive positioning [3][5][6]. Time and geography also matter: “global sales 2004–2012,” “Japan launch pricing,” or “US market reception” are much more searchable than the generic phrase “handheld game consoles” [2][3]. A stronger Hidden Knowledge example would be: “Compare Nintendo DS, PSP, PS Vita, Switch Lite, and Steam Deck on launch date, MSRP, screen size, battery life, lifetime sales, and flagship games in the US and Japan.” Another example: “Trace the evolution of Nintendo handhelds from Game Boy (1989) through Switch Lite, focusing on display, portability, and backward compatibility” [3][4][5]. Supplying named products plus measurable facts will allow a grounded, wide-coverage query instead of an error response [1][2]. ## References [1] OpenAI Help Center, “Prompt engineering / grounding guidance,” https://help.openai.com/ [2] Google Cloud, “Vertex AI Search documentation,” https://cloud.google.com/generative-ai-app-builder/docs/ [3] Nintendo, “Company / hardware history,” https://www.nintendo.com/ [4] Encyclopaedia Britannica, “Game Boy,” https://www.britannica.com/topic/Game-Boy [5] Sony Interactive Entertainment, PlayStation hardware pages, https://www.playstation.com/ [6] Valve, “Steam Deck,” https://www.steamdeck.com/