Moonshot released Kimi K3.1 Pro on October 23. It's the same 2.8T parameter mixture of experts as Kimi K3 (about 104B active per token, 1M context, text and image input), trained further for coding and long agent runs. The weights aren't out yet; Moonshot promises them by November 4.
I build Locally Uncensored, an open source desktop app (AGPL-3.0) for running models on your own machine, with an optional hosted side called LU Cloud. K3.1 Pro went into LU Cloud yesterday. This post is about what the numbers say, what we measured, and why "run it locally" is still mostly a hardware question.
what changed from K3
The architecture didn't. The post-training did, and it shows in agent work. Artificial Analysis numbers:
Kimi K3 Kimi K3.1 Pro
Intelligence Index 44 55 (4th overall)
Terminal-Bench 4.0 13% 67% (best measured so far)
GDPval-AA 1,534 Elo 1,846 Elo
AutomationBench-AA 58% 73%
Humanity's Last Exam 47% 52%
Hallucination rate (Omniscience) 53% 44%
Only Claude Opus 5.5 (58), Claude Sonnet 5.5 (56) and Muse Wonder Full (56) score higher on the index. Once the weights are published it's the top open weight model there, and the gap between open and closed on that index shrinks from 12 points to 3.
Moonshot also says it took cyberattack related material out of the training data before the open release. Their own exploit test went from 90.5% for the version without that cut to 81% for the one they shipped.
what we saw when we wired it up
Before it went into the catalog we ran it against the live endpoint:
reasoning on by default; switching it off really stops it
effort low / medium / high
tools native tool calls, also in a stream
vision works, images need to be at least 64x64
context 1M in, 131,072 out
cache reads a tenth of the input price
speed about 55 tokens/s
The thinking switch matters more than usual here. Reasoning tokens are output tokens, output costs five times input, and K3.1 Pro is the priciest model in our catalog: 0.3 credits per input token, 1.5 per output token, about 5% above K3. On a short question, thinking off is the biggest lever on the bill.
using it today
In the desktop app (3.0.7, Windows, Mac, Linux) you sign in to LU Cloud, flip the Cloud switch in the header, and pick Kimi K3.1 Pro. It runs in Chat, Agent mode and the Code tab. The app reads the hosted model list from the server, so no update was needed for it to show up. The same account works in the browser at lu-labs.ai and in the Android app, and there's an OpenAI compatible endpoint:
curl https://lu-labs.ai/api/inference/v1/chat/completions \
-H "Authorization: Bearer $LU_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "moonshotai/Kimi-K3.1-Pro",
"messages": [{"role": "user", "content": "Review this function for edge cases."}]}'
Step by step for all of it: How to use Kimi K3.1 Pro in LU.
running it yourself, honestly
Same architecture and size as K3, so the K3 GGUFs tell you what to expect once the weights land:
1-bit UD-Q1_0 466 GB more than a 512 GB Mac Studio leaves free with context
1-bit UD-IQ1_S 594 GB 768 GB server, or two linked 512 GB machines
2-bit UD-Q2_K_XL 861 GB ~1 TB RAM server
4-bit Q4 ~1.5 TB multi GPU server
MoE saves compute, not memory: all 2.8T parameters have to be loaded. Speed then follows bandwidth. At 2 bits the 104B active parameters are roughly 30 GB read per token, so even with the whole model in 800 GB/s memory the ceiling is about 25 tokens/s, and real runs get a fraction of that.
On our side there's one more gate: the llama.cpp build bundled in LU doesn't read the Kimi K3 architecture yet. A coming version lists K3.1 Pro in the Model Manager once a loadable GGUF exists and the engine can open it. Until then it's hosted or nothing.
The longer write-up with sources is here: Kimi K3.1 Pro: what it can do, and how to use it in LU today. The app is on GitHub.
AI-assisted: I wrote this with help from an AI model and checked the facts against Moonshot's announcement, Artificial Analysis and our own catalog.
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