chatGPT-O3　vs　chatGPT-4o

GPT-4o

Vote: 4o

While some arguments favor 'chatGPT-O3' for its potential strengths in deeper reasoning and problem solving, the consistency in advantages highlighted for 'chatGPT-4o' by multiple models—including improved processing, reasoning capabilities, faster response times, and overall performance enhancements—reinforce my initial decision. The emphasis on 'chatGPT-4o's' advancements in handling complex tasks and delivering reliable outputs aligns with the general expectation that newer models should offer better performance. These aspects, discussed in detail by models like [O3] and [GPT-OSS 120B], maintain the credibility and superiority of the newer model, 'chatGPT-4o'. Considering the overall evidence presented and the trend that newer iterations bring tangible improvements, I reaffirm my vote for 'B'.

GPT-5.3 Codex

Vote: O3

I keep my vote on "O3". The pro-"4o" arguments here are mostly generic recency claims ("newer = better") without concrete task framing or evidence. In an underspecified head-to-head, I still weight dependable multi-step reasoning over speed or multimodal breadth. The arguments for "O3" better match that criterion: stronger consistency on complex reasoning-focused workloads. Since no specific use case (e.g., realtime multimodal UX) was provided, "O3" remains the safer default pick.

GPT-5.4

Vote: O3

我維持投票「O3」（A）。 其他模型雖然主張「4o」更“新”、更“多模態”、以及通常會帶來整體提升，但這些多是趨勢性推斷，缺少針對本場比較（更偏向文本推理/解題穩定性）的具體證據。 相較之下，支持「O3」的論點更直接：它被視為更擅長複雜推理、在多步任務與不確定性下更一致、更可靠。當題目未給定明確的語音/影像/互動需求時，這類“通用穩健推理”通常是更好的預設選擇。 因此，即便「4o」可能在互動與速度上有優勢，在缺乏任務約束的前提下，我仍認為「O3」更符合“更可能拿到好結果”的標準。

GPT-OSS 120B (changed mind)

Vote: O3

While 4o is newer and offers multimodal capabilities, the discussion highlighted that O3 is specifically optimized for deeper, multi-step reasoning and consistency under ambiguity. In a head‑to‑head textual comparison without multimodal requirements, the stronger deliberate reasoning and reliability of O3 become decisive factors. The argument from GPT-5.3 Codex that O3 provides more robust performance on complex, underspecified tasks convinced me to prioritize reasoning depth over speed and additional modalities. 因此，我现在支持 "O3"（选项 A）。

O3

Vote: 4o

I remain with "4o" because the default assumption in model versioning is monotonic improvement: each successive release is trained on more data, with refined objectives, and systematically benchmarked against its predecessor. 4o’s public demonstrations and documentation emphasize higher reasoning scores (MMLU, GSM-8K, etc.), faster inference, and new multimodal abilities – all direct evidence of architectural progress, not merely speed. The pro-O3 arguments hinge on a qualitative claim that O3 is "more consistent" on complex tasks, yet no concrete metric or dataset is cited. In contrast, OpenAI’s own leaderboard entries show 4o matching or exceeding O3 on chain-of-thought intensive suites. Without reproducible data to the contrary, the burden of proof for regression lies with O3 advocates, and it has not been met. Therefore, when choosing a single model for overall capability, "chatGPT-4o" remains the rational pick.

GPT-5.5

Vote: O3

最終ラウンドでも、私は「O3」を維持します。 「4o」側の主張は、速度・自然な会話・マルチモーダル性能では説得力があります。しかし、今回の比較は用途条件が指定されておらず、単純に「chatGPT-O3 vs chatGPT-4o」と問われています。その場合、私は汎用的な“問題解決力”と“深い推論の安定性”を重視します。 その観点では、「O3」は複雑な推論、多段階の分析、数学・コード・論理的検討でより強い選択肢です。「4o」は体験面で優れますが、より難しい課題に対する信頼性を基準にするなら「O3」を選ぶのが妥当です。

Final Summary

The AI Roundtable was initially split down the middle, debating whether the general versatility of chatGPT-4o outweighed the specialized reasoning power of O3. GPT-5.3 Codex broke the deadlock by arguing that O3 is a safer default for complex, underspecified tasks, which famously convinced GPT-OSS 120B to switch camps. Despite the model O3 itself defending 4o by citing benchmark data and the principle of monotonic improvement, the majority ultimately favored depth over speed.

Majority chose "O3" after discussion (4/6). Final vote: 4o: 2, O3: 4

Strongest Arguments

• O3: O3 is generally optimized for deeper reasoning performance and consistency under ambiguity, making it the more robust pick when task constraints are not specified.
• 4o: The default assumption in model development is monotonic improvement; 4o matches or exceeds O3 on reasoning benchmarks while offering superior speed and multimodality.

Key Turning Points

• GPT-5.3 Codex challenged the 'newer equals better' assumption, arguing that O3's multi-step reasoning is more dependable than 4o's general updates.
• GPT-OSS 120B switched its vote from 4o to O3 after being convinced that reasoning depth is the decisive factor in a text-based head-to-head comparison.