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English(EN) 📊 Kimi K2.5 (Reasoning): GPQA 87.9%, HLE 30.7%, Long Context 73%, SciCode 49% — all at 30 int points/dollar. Independently measured. https:// olud.ai/leaderboar

Kimi K2.5 在具有竞争力的定价下实现了强劲的基准分数

Kimi K2.5 在包括 GPQA、HLE、长上下文和 SciCode 在内的多个基准测试中取得了显著的性能。该模型在这些评估中的定价具有竞争力,每美元 30 个整数点。这些结果是独立测量和发布的。 AI

影响 在关键的 LLM 基准测试中展示了具有竞争力的性能和成本效益。

排序理由 模型基准测试结果由独立测量来源发布。[lever_c_demoted from research: ic=1 ai=1.0]

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

Kimi K2.5 在具有竞争力的定价下实现了强劲的基准分数

本文如何被排名

Signal score
17 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
模型基准测试结果由独立测量来源发布。[lever_c_demoted from research: 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

报道来源 [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · opensourceaitech ·

    📊 Kimi K2.5 (推理): GPQA 87.9%, HLE 30.7%, 长上下文 73%, SciCode 49% — 每美元 30 个 int 点。独立测量。https:// olud.ai/leaderboar

    📊 Kimi K2.5 (Reasoning): GPQA 87.9%, HLE 30.7%, Long Context 73%, SciCode 49% — all at 30 int points/dollar. Independently measured. https:// olud.ai/leaderboard.html # LLM # Benchmarks # OpenSource # AI