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Alibaba's Qwen3-Coder-Next achieves 70.6 on SWE-Bench with sparse MoE

By PulseAugur Editorial · [1 sources] ·

Alibaba's Qwen3-Coder-Next, an 80 billion parameter model with 3 billion active parameters, has achieved a 70.6 score on the SWE-Bench Verified benchmark. This performance is notable as it rivals top closed-source models while offering downloadable weights under the Apache 2.0 license. The model employs a sparse Mixture-of-Experts architecture and a hybrid attention mechanism, combining linear attention for long contexts with standard attention for global context reconstruction. AI

IMPACT Sets a new SOTA for open-source coding models on SWE-Bench, making advanced coding assistance more accessible.

RANK_REASON The cluster details a new open-source model release with benchmark performance metrics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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

Alibaba's Qwen3-Coder-Next achieves 70.6 on SWE-Bench with sparse MoE

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The cluster details a new open-source model release with benchmark performance metrics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Thousand Miles AI ·

    Qwen3-Coder-Next: 80B total, 3B active, 70.6 on SWE-Bench

    <p>Qwen3-Coder-Next runs 3 billion parameters per token. It scores <strong>70.6 on SWE-Bench Verified</strong> with the SWE-Agent scaffold. Both numbers are true at the same time, and the gap between them is where the interesting architectural ideas live.</p> <h2> TL;DR </h2> <ul…

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