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GLM-5.1 (Non-reasoning) achieves 83.9% on GPQA benchmark

By PulseAugur Editorial · [1 sources] ·

A new benchmark evaluation shows that GLM-5.1 (Non-reasoning) achieved an 83.9% score on the GPQA benchmark. This performance was measured independently and highlights the model's efficiency, delivering 13.3 intelligence points per dollar spent. AI

IMPACT Independently verified benchmark results provide crucial data for comparing model capabilities and efficiency.

RANK_REASON The cluster reports on a benchmark result for an AI model, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GLM-5.1 (Non-reasoning) achieves 83.9% on GPQA benchmark

How we ranked this

Signal score
13 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
The cluster reports on a benchmark result for an AI model, which falls under research. [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, paper
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.

Full methodology in our editorial standards.

COVERAGE [1]

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

    GLM-5.1 (Non-reasoning) delivers 83.9% GPQA at 13.3 intelligence points per dollar — independently measured, not self-reported. See the full benchmark set. http

    GLM-5.1 (Non-reasoning) delivers 83.9% GPQA at 13.3 intelligence points per dollar — independently measured, not self-reported. See the full benchmark set. https:// olud.ai/leaderboard.html # LLM # Benchmarks # OpenSource # AI

    LINKS olud.ai/leaderboard.html

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