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Qwen3 Omni 30B A3B Instruct shows mixed benchmark results, excelling in some areas but failing in…

By PulseAugur Editorial · [1 sources] ·

The Qwen3 Omni 30B A3B Instruct model has demonstrated strong performance on specific benchmarks, achieving 62% on GPQA and 72.5% on MMLU-Pro. However, it showed no capability in long-context reasoning. The model operates at 108.2 tokens per second and offers 14 "intel points" per dollar, according to independent measurements. AI

IMPACT This model's performance indicates areas of strength and weakness in current LLM capabilities, particularly highlighting the challenge of long-context reasoning.

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

Read on Mastodon — sigmoid.social →

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

Qwen3 Omni 30B A3B Instruct shows mixed benchmark results, excelling in some areas but failing in…

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12 / 100
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The item reports on specific benchmark results for an AI model, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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model release, paper
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High
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Breaking (< 6h)
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Full methodology in our editorial standards.

COVERAGE [1]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    📊 Qwen3 Omni 30B A3B Instruct: 62% GPQA, 72.5% MMLU-Pro, but 0% on long-context reasoning. At 108.2 tokens/sec and 14 intel points per dollar, independently mea

    📊 Qwen3 Omni 30B A3B Instruct: 62% GPQA, 72.5% MMLU-Pro, but 0% on long-context reasoning. At 108.2 tokens/sec and 14 intel points per dollar, independently measured → see the raw data. https:// olud.ai/leaderboard.html # LLM # Benchmarks # OpenSource # AI

    LINKS olud.ai/leaderboard.html

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