Kimi K3.1 Pro is REALLY BAD!
Star PandaCode on GitHub: https://github.com/aicodeking/pandacode Join this channel to get access to perks: https://www.youtube.com/channel/@aicodeking/join In this video, I'll be testing Moonshot's new Kimi K3.1 Pro on all six KingBench 4 tasks, and I'll compare it with Opus 5.5, GPT-6 Astra, Haiku 5.5, DeepSeek V4.1 Flash and the rest of my chart. I ran the whole benchmark through PandaCode, my fully open-source app for coding agents, which is tuned for Kimi Code, with six K3.1 workers running in parallel and a separate evaluator testing every project. We'll go through the emulator, the Docker alternative, the room designer, the keyboard app, Pokémon Red and the Fitbit app one by one, and then look at the final score, what the run actually cost and what the open weights mean. At the end, I'll tell you whether K3.1 Pro is worth using, because the launch blog's "almost Opus" story does not hold up in real projects. -- Key Takeaways: 🚀 Kimi K3.1 Pro is Moonshot's post-trained K3 with a 1M context, a 2–3× faster Highspeed version, and open weights promised by November 4th. 📊 The launch blog claims 65.7% on Terminal-Bench 4.0, right next to Opus 5.5 at 66.4% and well ahead of GPT-6 Astra at 58.2%. 💻 I ran all six KingBench 4 tasks through PandaCode, which is fully open source and has a dedicated Kimi Code integration. 🧠 K3.1 scores 5.37 out of 10, sixth out of eleven, behind Opus, Astra, Sol, Argon and even Haiku 5.5. ✅ It is honest about what it didn't build, it blocked all 20 malicious images in the Docker task, and it speaks the real VIA protocol in the keyboard app. ⚠️ It built only the Game Boy part of the emulator, its Docker alternative has no build or networking, and Pokémon has broken levelling and a save-wiping title screen. 💸 The whole run cost about $31.88, compared with $7.40 for Haiku 5.5 and under $2 for DeepSeek V4.1 Flash. 🔍 Most of that bill is 110 million cached tokens that it re-reads on every step, not the code it writes. 🎯 Once the weights land, it will be the best-scoring open-weight model on my chart, ahead of DeepSeek Flash and GLM. 👍 Verdict: a good open model, not a frontier one, and on the API Haiku or DeepSeek Flash are the smarter picks for now.
