English(EN) 1.04M Tokens of Context: What You Can Actually Do With It

大语言模型突破100万Token上下文限制，可进行整个代码库分析

作者 PulseAugur 编辑部 · [2 个来源] · 2026-09-01 01:09

新的大语言模型正以约100万Token的上下文窗口涌现，显著扩展了它们在单次请求中处理和理解大量信息的能力。Kimi k3 和 GLM-5.3-Flash 等模型能够执行诸如分析整个代码库、处理长篇文档以及处理冗长转录内容等任务，而无需分块或检索增强生成（RAG）等复杂变通方法。虽然这些大型上下文窗口为开发人员和研究人员带来了显著的好处，但用户仍需考虑相关的成本和输出Token数量上限的限制。 AI

影响能够在单次请求中实现整个代码库分析和复杂文档合成，减少对RAG和分块的依赖。

排序理由 AI实验室正在发布具有显著增加的上下文窗口（100万+ Token）的新大语言模型。[lever_c_demoted from frontier_release: ic=2 ai=1.0]

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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: Significant; AI实验室正在发布具有显著增加的上下文窗口（100万+ Token）的新大语言模型。[lever_c_demoted from frontier_release: ic=2 ai=1.0]
Source corroboration: 2 independent sources; Multiple independent publishers reporting the same story raises confidence that it's real and newsworthy.
Topics: model release, product; 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: 4 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 [2]

dev.to — LLM tag TIER_1 English(EN) · Daniel Dong · 2026-09-01 13:55

1,000,000 词的上下文。这究竟能解锁什么。

Most models force you to choose: summarize the document or keep the detail. A 1M-token context window lets you stop choosing. <div class="highlight js-code-highlight"> <pre class="highlight shell"><code>curl https://aibridge-api.com/v1/chat/complet…
dev.to — LLM tag TIER_1 English(EN) · Lola Lin · 2026-09-01 01:09

104万个Token的上下文：你实际上能用它做什么

A million tokens is enough to read a mid-sized codebase in a single request. GLM-5.3-Flash gives you 1,040,000 of them — and at the pricing Zhipu announced in August 2026, filling that window costs less than a coffee. That combination is the interesting part, not the number it…

本文如何被排名

报道来源 [2]

1,000,000 词的上下文。这究竟能解锁什么。

104万个Token的上下文：你实际上能用它做什么

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