Qwen3.6 27B x Claude Opus 4.x - v2

Benchmarks

Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2
         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.665,0.831,0.910,0.790,0.456,0.813,0.772

Qwen3.6-27B
         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.647,0.803,0.910,0.773,0.450,0.806,0.742

Provided by @nightmedia. All benchmarks were done in mxfp8 precision

🧬 Datasets:

• TeichAI/Claude-Opus-4.6-Reasoning-887x
• TeichAI/claude-4.5-opus-high-reasoning-250x

⚡ Use cases

• Coding
• Creative Writing
• Visual Understanding
• General Purpose

Citations and Contributions

• @unsloth - This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.
• @Qwen - Providing a fantastic, native-multimodal base model

Usage

If you need help setting up and configuring this model please follow the Qwen team's instructions in the original model's README