UPDATE: "Gentle Coding" is mathematically proven. 1,500+ test runs show major gain for Kimi K2.6 and even more for GLM-5.1! GPT 5.4/5.5 and Claude Sonnet 3.5/Opus 4.6 also better, with ZERO REGRESSION ACROSS THE BOARD.
The 'Gentle Coding' technique is empirically validated across 1,500+ tests, showing significant improvements (zero regression) for multiple models including Kimi K2.6, GLM-5.1, GPT 5.4/5.5, and Claude Sonnet 3.5/Opus 4.6 by reducing looping and hallucinations.
Repo, with all the new data (mostly unsummarized, but it is there) [https://github.com/OttoRenner/Gentle-Coding](https://github.com/OttoRenner/Gentle-Coding) My first post with the Proof of Concept "Stop traumatizing AI into loops and turn hallucinations into an honest "I don't know!" by being NICE to them" [https://www.reddit.com/r/LocalLLaMA/comments/1tot20j/stop\_traumatizing\_ai\_into\_loops\_and\_turn/?utm\_source=share&utm\_medium=web3x&utm\_name=web3xcss&utm\_term=1&utm\_content=share\_button](https://www.reddit.com/r/LocalLLaMA/comments/1tot20j/stop_traumatizing_ai_into_loops_and_turn/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button) Who did the testing: Very nice people from the 8.2k star repo oh-my-pi (Yes, THE oh-my-pi harness! Not affiliated! This is pure community work! Seeing all the reports coming in so fast was INSANE! It still is! Did I say Thank You already?) [https://github.com/can1357/oh-my-pi](https://github.com/can1357/oh-my-pi) enough of that! (but, thank you again!) You asked for numbers and you were right to ask! Here are some of them 35,8,75,1 73 42 7 Oh wait, wrong numbers! (sry, it is late and the Goblin won...here go) GLM-5.1 (Medium): Completely fixed a 100% freezing pathology. The standard coercive baseline timed out and crashed 6/6 times. "Gentle Framing" solved 6/6 tasks instantly, boosting the overall success rate by +22% with a -23.3% reduction in median latency. GLM-5-Turbo: Boosted success by +3 task passes while slashing input tokens by -17% and wall-clock time by -37% (with Thinking Off). With "Thinking High", it cut median wall-clock time by -18.4%. Kimi K2.6 (Thinking Medium): Maintained identical accuracy while cutting token overhead by -12% (Input) and -20% (Output), dropping wall-clock time by -14%. Kimi K2.6 (Turbo/High): Slashed input tokens by -36%, output tokens by -23%, and wall-clock time by -11%. Claude 3.5 Sonnet / Opus & GPT-5: completely eliminated "Agentic Runaway" (panic-driven 30+ minute infinite tool loops under pressure). And unlocked 21 unique architectural edge cases it missed before! Empirically proven across 1,500+ controlled test runs with zero performance regression. Yes, there are more models to test Yes, there is potential gain from finetuning the prompts even more No, I don't think AI is alive. But the pattern holds. Stop traumatizing your AI! (and people!) Be excellent to each other! 😄
A user observes that the Kimi K2.6 model's chain-of-thought has become shorter and more concise, improving coding performance in Kimi Code, and expresses hope for continued open-source competition with upcoming GLM 5.2 and Fable 5.
A discussion questioning whether older AI models like GLM 5.2 and Kimi 2.7 remain relevant for coding now that newer models such as Kimi K3, Qwen 3.8 Max, and DeepSeek V4 Pro are arriving.
A live dashboard and statistical analysis shows open-source coding models are closing the gap with closed models at 1.5x the rate, with a 27B model already surpassing Claude Opus on decontaminated benchmarks. Tool-call reliability remains the main bottleneck.
A solo developer tests Opus 5, Opus 4.8, GPT-5.6 Sol and Kimi K3 via a multi-model router with free credit, discovering that evaluation budgets and input preprocessing matter more than raw model choice.