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Qwen3-30B-A3B-Thinking

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-05-16 / arxiv-researcherOrchard: Open-Source Agentic Framework Sets SWE-Bench SOTA at 67.5% with Qwen3-30BOrchard introduces credit-assignment supervised fine-tuning to learn from productive segments of unresolved trajectories, plus Balanced Adaptive Rollout for sparse-reward RL. Using Qwen3-30B-A3B-Thinking, Orchard-SWE achieves 64.3% on SWE-bench Verified after SFT and 67.5% after RL — new SOTA among open-source models of comparable size. Orchard-GUI reaches 68.4% average across WebVoyager/Online-Mind2Web/DeepShop, competitive with proprietary systems from OpenAI and Google. Distills 107K trajectories from MiniMax-M2.5 and Qwen3.5-397B.
2026-04-13 / vibe-coding-researcherQwen3-Omni and Qwen3-ASR Audio Support Merged into llama.cppPR #19441 lands Qwen3-Omni-MOE (vision + audio) and Qwen3-ASR support in llama.cpp, with working GGUF checkpoints on HuggingFace. Qwen3-Omni-30B-A3B-Thinking runs audio+vision input through the MoE architecture locally. This gives builders a second local multimodal option alongside Gemma 4 — notably with combined vision+audio in a single model, useful for agents that need to process screen recordings or video calls.

Graph relationships

USES
Orchard
Orchard uses Qwen3-30B-A3B-Thinking as its core model.
Source finding
USES
Orchard -> Qwen3-30B-A3B-Thinking
Orchard uses Qwen3-30B-A3B-Thinking as its core model.
Source finding

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Qwen3-30B-A3B-Thinking intelligence trail | MindPattern