English(EN) Tri-21B-Think hits 60.1% GPQA and 54.6% IFBench in independent testing — a 21B model performing like much larger ones. 📊 Full breakdown on olud.ai https:// olud

Tri-21B-Think 模型在基准测试中取得强劲表现

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 20:00

Tri-21B-Think 模型是一个拥有 210 亿参数的语言模型，在独立基准测试中取得了显著的性能。它在 GPQA 基准测试中得分 60.1%，在 IFBench 基准测试中得分 54.6%。这些结果表明该模型可以与明显更大的语言模型相媲美。 AI

影响证明了较小的模型可以在复杂的基准测试中取得有竞争力的性能，从而可能降低人工智能应用的计算成本。

排序理由一个开源模型的独立基准测试结果。[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 →

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Signal score: 12 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 一个开源模型的独立基准测试结果。[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, other; 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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Mastodon — mastodon.social TIER_1 English(EN) · opensourceaitech · 2026-10-19 20:00

Tri-21B-Think 在独立测试中 GPQA 达到 60.1%，IFBench 达到 54.6% — 21B 模型表现堪比更大模型。📊 完整分析请访问 olud.ai https:// olud

Tri-21B-Think hits 60.1% GPQA and 54.6% IFBench in independent testing — a 21B model performing like much larger ones. 📊 Full breakdown on olud.ai https:// olud.ai/leaderboard.html # LLM # Benchmarks # OpenSource # AI

链接 olud.ai/leaderboard.html

本文如何被排名

报道来源 [1]

Tri-21B-Think 在独立测试中 GPQA 达到 60.1%，IFBench 达到 54.6% — 21B 模型表现堪比更大模型。📊 完整分析请访问 olud.ai https:// olud

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