V4-Flash-0731 - vibes after first weekend of use

Spent way too much time with V4-Flash-0731 this weekend and wanted to share my vibes as briefly as possible. I sent it through a bit of real-work and some of my personal benchmarks. My quick thoughts are: Quantization hits this thing like a truck - I've tried a bunch of the Q2 and Q3 weights and it behaves like an entirely different model. Reasoning looks/feels different and the results are a full tier down from the official/served V4-Flash-0731. Did not get much time with Q4. Q3 can finally be your Qwen3.6-27B replacement (if you've got the VRAM..) - it does the same work as Qwen3.6-27B, just more reliably. In simple one-shots they're about even but as you bring them into larger repos or large harnesses (Claude Code with tools starting around 30k system prompt tokens..) V4-Flash-0731 at Q3 pulls well ahead of Qwen3.6-27B at Q8. Q2 is a bit too much - in every use-case with Q2 I ended up preferring Qwen3.6-27B Q8 weights. Q2_K_XL is questionable but that's some 4GB smaller than IQ3_XXS so I wouldn't even recommend it. Full Precision is the real deal - I'd say it's approaching GLM 5.2 levels which is incredibly exciting. Yes it reasons a lot on complex tasks but the final cost is still mind-bogglingly low. Saying that it beats GLM 5.2 (let alone Opus 5, Fable, etc..) is a bit silly.. but focusing on the price this thing is in a class all its own. It's clearly very focused on agentic-work - I always considered Deepseek's releases as flagships for "general-purpose" models but V4-Flash-0731 is a bit weak in the knowledge department. This is a non-issue if you're using tool-calls as the model is extremely clever at using them and reasoning with what it finds, but something to consider if you have an airgapped use-case.