English(EN) 📊 Independently measured, Seed-OSS-36B-Instruct delivers 40.3 intelligence points per dollar — GPQA 72.6%, MMLU-Pro 81.5%. Cost-efficiency that puts bigger mode

Seed-OSS-36B-Instruct 在基准测试中实现高成本效益

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-09 12:00

根据独立测量，Seed-OSS-36B-Instruct 的每美元智能点成本效益达到了 40.3。该模型在 GPQA 基准测试中得分 72.6%，在 MMLU-Pro 中得分 81.5%。这些结果表明，Seed-OSS-36B-Instruct 相对于其成本提供了具有竞争力的性能，有可能挑战更大、更昂贵的模型。 AI

影响展示了大型语言模型成本效益的新基准，可能影响未来的模型开发和部署策略。

排序理由该集群报告了一个开源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]

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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: 2 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群报告了一个开源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, 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: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

Mastodon — mastodon.social TIER_1 English(EN) · opensourceaitech · 2026-09-09 12:00

📊 独立测量，Seed-OSS-36B-Instruct 每美元提供 40.3 智能点 — GPQA 72.6%，MMLU-Pro 81.5%。成本效益超越大型模型

📊 Independently measured, Seed-OSS-36B-Instruct delivers 40.3 intelligence points per dollar — GPQA 72.6%, MMLU-Pro 81.5%. Cost-efficiency that puts bigger models on notice. 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]

📊 独立测量，Seed-OSS-36B-Instruct 每美元提供 40.3 智能点 — GPQA 72.6%，MMLU-Pro 81.5%。成本效益超越大型模型

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