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

大型语言模型突破100万Token上下文限制，开启新用例

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 01:09

GLM-5.3-Flash、DeepSeek V4和Kimi K3等新的大型语言模型提供了约100万Token的上下文窗口，远超通常的128K-200K范围。这种扩展的上下文允许执行诸如分析整个代码库、处理带有配套材料的长篇转录稿以及一次性综合大量文档集等任务。虽然这使得更可靠的多文件代码重构和长周期软件工程成为可能，但用户仍需考虑填充这些大型上下文窗口的成本以及输出Token预算的限制。 AI

影响支持整个代码库分析和多文件重构，可能加速软件开发周期。

排序理由新模型发布，能力显著提升（上下文窗口大小）。[lever_c_demoted from frontier_release: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 41 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Significant; 新模型发布，能力显著提升（上下文窗口大小）。[lever_c_demoted from frontier_release: ic=1 ai=1.0]
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, 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: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

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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

<p>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

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104万个Token的上下文：你实际上能用它做什么

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