English(EN) RT @nutlope: Ran 10 more tests comparing GLM 5.2 & Opus.

Together AI的GLM 5.2在速度和成本上优于Anthropic的Opus

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-23 18:50

Together AI发布了10项测试结果，将其GLM 5.2模型与Anthropic的Opus模型进行了比较。测试表明，GLM 5.2产生的token数量是Opus的两倍，速度更快，成本降低了三分之二，同时保持了相似的质量。Together AI计划开源测试代码、提示和成本统计数据。 AI

影响这项研究表明，对于寻求更快、更便宜的LLM推理的用户来说，可能节省成本并提高性能。

排序理由该项目详细介绍了AI模型的比较基准测试结果，属于研究范畴。[lever_c_降级自研究：ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 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: Tool; 该项目详细介绍了AI模型的比较基准测试结果，属于研究范畴。[lever_c_降级自研究：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: 80 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。

X — Together (inference / OSS) TIER_1 English(EN) · togethercompute · 2026-06-23 18:50

RT @nutlope: 运行了另外 10 次 GLM 5.2 和 Opus 的对比测试。

RT @nutlope: Ran 10 more tests comparing GLM 5.2 & Opus. On average, GLM 5.2 produced 2x the tokens but was still faster + 3x cheaper with…