Pq234/Darwin-28B-Opus

VISIONConcurrent Unit Cost:2Model Size:27BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 9, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

Pq234/Darwin-28B-Opus is a 27.6 billion parameter language model from the Darwin series, built on the Qwen3.6 generation backbone. It is an evolutionary merge combining Qwen3.6-27B's strong bilingual reasoning with Claude Opus 4-style chain-of-thought distilled behavior. This model excels at graduate-level STEM reasoning, achieving 88.89% on the GPQA Diamond benchmark, and is optimized for complex multi-step reasoning tasks.

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Darwin-28B-Opus: Qwen3.6-Generation Reasoning Model

Darwin-28B-Opus is a 27.6 billion parameter model, part of the Darwin series, leveraging the Qwen3.6 generation architecture. It is the result of a Darwin V7 evolutionary merge, combining the robust bilingual reasoning capabilities of Qwen3.6-27B with a Claude Opus 4-style chain-of-thought reasoning distilled behavior. This model is specifically designed for advanced reasoning tasks.

Key Capabilities

  • Exceptional Reasoning Performance: Achieves 88.89% on the GPQA Diamond graduate-level reasoning benchmark using a 3-stage adaptive evaluation protocol, outperforming its 36B MoE sibling and previous Darwin-27B-Opus.
  • Evolutionary Merge: Created by merging a Qwen3.6-27B 'father' model with a Claude Opus reasoning-distilled 'mother' variant, inheriting the Qwen3.6 architecture and Opus reasoning style.
  • Hybrid Attention: Utilizes a hybrid linear/full attention mechanism, inherited from its Qwen3.6-27B base.
  • Bilingual Reasoning: Offers strong reasoning capabilities in English, with secondary support for Korean, Chinese, and Japanese.

Recommended Use Cases

  • Graduate-level STEM reasoning: Ideal for tasks like GPQA and science qualifying exams.
  • Mathematical problem-solving: Suitable for complex problems such as MATH and AIME-style challenges.
  • Complex multi-step chain-of-thought tasks: Designed to handle intricate reasoning processes.
  • Code generation and debugging: Applicable for tasks like HumanEval and MBPP.