MuXodious/Qwythos-9B-Claude-Mythos-5-1M-MTP

VISIONConcurrent Unit Cost:1Model Size:9BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 6, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

Qwythos-9B is a 9 billion parameter reasoning model developed by Empero, built on a deeply uncensored Qwen3.5-9B base. It features an extended 1,048,576-token context window and native function calling capabilities. Post-trained on over 500 million tokens of high-quality Claude Mythos and Fable traces, it excels in complex reasoning tasks across domains like cybersecurity, biomedical science, and quantitative analysis.

Loading preview...

Qwythos-9B: A Specialized Reasoning Model

Qwythos-9B, developed by Empero, is a 9 billion parameter model based on a deeply uncensored Qwen3.5-9B architecture. It has been extensively post-trained on over 500 million tokens of high-quality Claude Mythos and Fable traces, specifically focusing on chain-of-thought reasoning generated by Empero AI's internal rethink tool.

Key Capabilities

  • Extended Context Window: Features a 1,048,576-token (1M) context window, enabled by YaRN rope-scaling, making it suitable for whole-codebase reasoning, multi-document research, and long agentic trajectories.
  • Enhanced Reasoning Performance: Demonstrates significant improvements over its base model, with +34.3 points on MMLU, +30 points on gsm8k-strict, and +19 points on gsm8k-flex under matched evaluation conditions.
  • Native Function Calling: Supports OpenAI/Qwen3.5-style function calling out of the box, enabling seamless integration with tools like Python executors and web search.
  • Self-Correction with Tools: Proven to self-correct and provide source-cited, factually accurate answers on complex prompts when paired with appropriate tools, making it deployment-ready for retrieval-augmented agentic settings.
  • Uncensored Design: Intentionally uncensored to engage seriously with technically demanding questions in domains like cybersecurity, red-teaming, biology, pharmacology, and clinical medicine, where over-aligned models often refuse or hedge.

Good for

  • Complex Reasoning Tasks: Excels in multi-step problem-solving and detailed analysis.
  • Long-Context Applications: Ideal for tasks requiring extensive context, such as code analysis or synthesizing multiple research papers.
  • Tool-Augmented Agents: Designed for integration into agentic workflows that leverage function calling for factual verification and computation.
  • Specialized Technical Domains: Particularly strong in cybersecurity, biomedical sciences, and quantitative reasoning.

Finetunes
1 models