Million-Token Context Windows: Effective Limits Still Differ
A Juejin AI analysis says Gemini 3.1 Pro, GPT-5.5, Claude Opus 4.7 Adaptive, and DeepSeek V4 Pro each advertise roughly 1 million tokens of context, while their output ceilings range from 64,000 to 384,000 tokens. BenchLM data cited by the report places Gemini 3.1 Pro strongest for effective context between 500,000 and 1 million tokens, although GPT-5.5 leads the LongBench v2 and MRCRv2 scores. Community reports cited in the article also describe reliability declining as inputs grow, while Codex reportedly limits GPT-5.6 to a default 272,000-token window.
The comparison suggests that nominal context size alone is insufficient for model selection: output limits, effective long-context reliability, pricing, caching, and application-level restrictions may matter more in real workloads.
2026 年的头部大模型，在上下文窗口这个数字上几乎打成了平手，旗舰产品清一色停在 100 万 token 上下。真正拉开差距的，是模型能把多少内容用好、一次能写多长，以及开发者实际能拿到多大的窗口。下面把官方数据、第三方评测和社区声音放在一起梳理。
📏 先把概念讲透
上下文窗口与最大输出是两个不同的数
上下文窗口指模型单次请求里能同时看到的 token 总量。系统提示、对话历史、上传文件、工具返回结果，连同模型自己的回答，都要挤进这个空间。最大输出（常见参数名是 max_tokens）则是模型单次回答最多能写多少。一个模型能读 100 万 token，单轮却可能只能写 12.8 万 token。
可以把这层关系写成一个简单的预算式：
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