OpenAI 的 GPT Codex 用户报告称，在处理超过约 180,000 个 token 的输入时，其质量和推理能力会下降。这种性能下降表现为逻辑混乱、上下文丢失和错误假设。为了缓解这些问题，一些用户将会话限制在此阈值以下，但这可能导致频繁的压缩。 AI
影响 突出了当前上下文窗口大小在复杂编码任务中的实际局限性。
排序理由 用户讨论模型性能限制，而非主要发布或研究发现。
AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →
用户报告称 GPT Codex 质量在超过 180k tokens 后下降
本文如何被排名
- Signal score
- 0 / 100
- Composite score across the factors below. Higher = stronger signal that this story matters right now.
- Newsworthiness bucket
- Commentary
- 用户讨论模型性能限制，而非主要发布或研究发现。
- Source corroboration
- Single-source cluster
- Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
- Topics
- model release
- Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
- AI-industry relevance
- High
- Clearly on-topic for AI-industry coverage.
- Story freshness
- 116 days old
- Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.
完整方法见我们的编辑标准。
报道来源 [1]
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GPT Codex 用户：在什么 token 数量下，对你们来说质量会实际下降？
<!-- SC_OFF --><div class="md"><p>I’ve been using GPT Codex heavily and things start falling apart around 180k tokens. Sloppier reasoning, dropped context, wrong assumptions. I capped my sessions at ~170k, which helps, but now compaction kicks in constantly. Where quality actuall…