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English(EN) 📊 Mistral Medium 3 — the actual numbers GPQA: 57.8% MMLU-Pro: 76% Humanity's Last Exam: 4.1% Long Context Reasoning: 31.7% 💰 15.6 intelligence points per dollar

Mistral Medium 3 基准测试显示出强劲的性能和成本效益

作者 PulseAugur 编辑部 · [1 个来源] ·

Mistral Medium 3 在多项基准测试中表现出色，在 GPQA 上达到 57.8%，在 MMLU-Pro 上达到 76%。该模型在 Humanity's Last Exam 上得分 4.1%，在 Long Context Reasoning 上得分 31.7%。值得注意的是，它每美元提供 15.6 智能点，表明其具有成本效益。 AI

影响 提供了 Mistral Medium 3 的性能指标，有助于比较 AI 模型的能力和成本效益。

排序理由 该项目报告了 AI 模型的基准测试结果，属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]

在 Mastodon — mastodon.social 阅读 →

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

Mistral Medium 3 基准测试显示出强劲的性能和成本效益

本文如何被排名

Signal score
16 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
该项目报告了 AI 模型的基准测试结果，属于研究范畴。[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。

报道来源 [1]

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

    📊 Mistral Medium 3 — 实际数据 GPQA: 57.8% MMLU-Pro: 76% Humanity's Last Exam: 4.1% Long Context Reasoning: 31.7% 💰 每美元 15.6 智能点

    📊 Mistral Medium 3 — the actual numbers GPQA: 57.8% MMLU-Pro: 76% Humanity's Last Exam: 4.1% Long Context Reasoning: 31.7% 💰 15.6 intelligence points per dollar Measured independently, not self-reported → https:// olud.ai/leaderboard.html # LLM # Benchmarks # OpenSource # AI

    链接 olud.ai/leaderboard.html

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