Summary
Fahd Mirza introduces and benchmarks Qwythos 9B, a reasoning-focused open-source model fine-tuned on over 500 million tokens of Claude Mythos traces. Built on the Qwen 3.5 9B base, the model ships with a 1 million token context window via YaRN scaling and supports multi-token prediction (MTP), enabling faster generation by drafting and verifying multiple tokens per forward pass without a separate draft model.
Mirza serves the model locally using llama.cpp on a system with an NVIDIA A6000 GPU (48GB VRAM), testing both Q4KM (approximately 7GB VRAM) and Q8 quantizations (approximately 12GB VRAM) to give viewers a sense of hardware requirements at different quality tiers. He wires Qwythos into the Hermes agent framework and evaluates it on a structured debugging task: a broken full-stack call center helper application with four intentionally planted bugs across backend and frontend code. The model correctly identifies and fixes three of the four — a SQL typo, a wrong HTTP method, and a miscalculated average handle time — while missing a frontend port mismatch it had read but failed to flag. It also correctly ignores a red herring comment in the codebase, which Mirza notes is exactly the right call.
A second test asks the model to generate a working animated simulation from a single natural language prompt with no additional hints, probing one-shot code generation ability. Mirza’s overall verdict is cautiously positive — impressive results for a 9B distilled model, but with a consistent reminder that human oversight remains essential and that buzzwords like “loop programming” should not replace careful review of agentic output.
📺 Source: Fahd Mirza · Published June 27, 2026
🏷️ Format: Benchmark Test
