vip9908/Qwythos-9B-Claude-Mythos-5-1M

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

Qwythos-9B by Empero is a 9 billion parameter reasoning model built on a Qwen3.5-9B base, post-trained on over 500 million tokens of Claude Mythos and Fable traces. It features a 1,048,576-token context window via YaRN rope-scaling and native function calling with self-correction capabilities. This model excels in technically demanding questions across cybersecurity, biomedical, and quantitative reasoning domains, demonstrating significant performance gains over its base model in MMLU and GSM8K benchmarks.

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Qwythos-9B: A Specialized 9B Reasoning Model

Developed by Empero, Qwythos-9B is a 9 billion parameter model built upon a deeply uncensored Qwen3.5-9B base. It has been extensively post-trained on over 500 million tokens of high-quality Claude Mythos and Fable traces, with chain-of-thought generated by Empero AI's internal tool, rethink. This training approach results in a compact, fast, and significantly more capable reasoning model.

Key Capabilities & Differentiators

  • 1M-Token Context Window: Features a 1,048,576-token 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 substantial improvements over its base model, with +34 pts on MMLU and +30 pts on GSM8K-strict under matched evaluation conditions.
  • Native Function Calling & Self-Correction: Supports OpenAI/Qwen3.5-style function calling out-of-the-box, enabling self-correction with tools like Python executors and web search for factually correct, source-cited answers.
  • Uncensored & Domain-Specific Expertise: Intentionally uncensored to engage seriously with complex questions in cybersecurity, red-teaming, biology, pharmacology, and clinical medicine, where other models might refuse or hedge.

Ideal Use Cases

Qwythos-9B is particularly well-suited for applications requiring deep reasoning and factual accuracy in specialized domains. Its long context window and tool-use capabilities make it excellent for retrieval-augmented agentic settings, complex problem-solving, and detailed analysis in technical fields.

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