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Alibaba's Qwen3-Coder-Next achieves 70.6% on SWE-bench with efficient MoE architecture

By PulseAugur Editorial · [1 sources] ·

The Qwen3-Coder-Next model, an 80 billion parameter Mixture-of-Experts model from Alibaba's Qwen team, has demonstrated impressive efficiency by achieving 70.6% on the SWE-bench Verified benchmark with only approximately 3 billion active parameters per inference pass. This allows it to offer performance comparable to frontier coding agents while requiring hardware resources similar to a 7 billion parameter model. The model supports a 256K context window, making it suitable for complex coding tasks, and can be set up locally using Ollama for an OpenAI-compatible API. AI

IMPACT Sets a new bar for efficient coding models, potentially lowering hardware barriers for advanced AI-assisted development.

RANK_REASON New model release with benchmark performance from a major lab. [lever_c_demoted from frontier_release: 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 efficient MoE architecture

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Significant
New model release with benchmark performance from a major lab. [lever_c_demoted from frontier_release: ic=1 ai=1.0]
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model release, product
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High
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93 days old
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Jovan Chan ·

    Qwen3-Coder-Next Local Setup Guide 2026: Ollama and GGUF

    <blockquote> <p>This article was originally published on <a href="https://aifoss.dev/blog/qwen3-coder-next-local-setup-guide-2026/" rel="noopener noreferrer">aifoss.dev</a></p> </blockquote> <p><strong>TL;DR</strong>: Qwen3-Coder-Next hits 70.6% on SWE-bench Verified with only ~3…

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