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qwen3.8-flash-next-iq3_s outperforms -27B-GGUF:UD-Q4_K_XL in benchmark tests

By PulseAugur Editorial · [1 sources] ·

A comparison of two language models, qwen3.8-flash-next-iq3_s and -27B-GGUF:UD-Q4_K_XL, was conducted using several benchmarks including AIME26, IFEval, BFCL v4, and LiveCodeBench v6. The qwen3.8-flash-next-iq3_s model outperformed the -27B-GGUF:UD-Q4_K_XL model in total solved problems across these benchmarks, achieving 151 correct answers compared to 145. While both models faced issues with token limits, the -27B-GGUF:UD-Q4_K_XL model had more instances of incorrect answers. AI

IMPACT Provides comparative performance data for specific language models on various benchmarks.

RANK_REASON Comparison of two language models on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

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

qwen3.8-flash-next-iq3_s outperforms -27B-GGUF:UD-Q4_K_XL in benchmark tests

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Comparison of two language models on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/politerate ·

    qwen3.8-flash-next-iq3_s vs -27B-GGUF:UD-Q4_K_XL

    <!-- SC_OFF --><div class="md"><p>Sampling: temp 1.0, top_p 0.95, top_k 20 for both. Run with evalscope over the OpenAI-compatible API: Qwen3.8-Next on Strata (MI50 32GB), Qwen3.8-27B on llama.cpp (RX 7900 XTX 24GB).</p> <p>Caveat: These are only a portion of the full sets, since…

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