Kimi K3.1 Pro Open Weights: A Developer's Checklist Before You Self-Host a 2.8T Model

Moonshot AI published the full weights of Kimi K3.1 Pro on Hugging Face on November 4, 2026 — the date it promised at launch. The repo is moonshotai/Kimi-K3.1-Pro: 96 files, 1.56 TB, no access gate. The model is 2.8 trillion parameters total with 104 billion active per token, a 1,048,576-token context window, and a 131,072-token max output.

Before you start the download, run through this checklist. Most of the mistakes people will make with this release are made in the first hour.

1. Check whether you can actually run it

The honest numbers first. The official weights are ~1.56 TB. The simplest vLLM setup takes eight data-centre GPUs. At 2-bit precision you need roughly 861 GB of VRAM; at 4-bit, roughly 1,509 GB. There is no GGUF build on Hugging Face yet, so the consumer-GPU path does not exist today.

If you do not have a small GPU cluster, do not download 1.56 TB to find that out. Use the API instead — it is live in Kimi.ai, Kimi Work, Kimi Code, and the Kimi API, and the weights release changed nothing about its price: $2 per million input tokens, $10 per million output, cached input at $0.20.

2. Know what the benchmarks actually say

Moonshot reports 65.7% on Terminal-Bench 4.0, against Claude Opus 5.5 at 66.4% and GPT-6 Astra at 58.2%. LiveBench overall is 71.3, measured October 26, 2026.

These are vendor-run numbers: Moonshot ran its model in the Claude Code harness and took rivals' scores from published reports. That is normal for a launch, and it is also why you should verify before migrating anything. The weights are public now, so independent runs are coming — build your eval harness first, migrate after the independent numbers land.

3. Understand the architecture you are downloading

This is a mixture-of-experts model: 896 experts, 16 selected per token. Vision runs through a 401-million-parameter MoonViT-V2 encoder, and the model accepts text, image, and video input with text output. The weights are MXFP4, trained with quantization awareness — the model was trained knowing it would live at 4-bit precision, so this is not a post-hoc compression.

One asymmetry to note: Kimi K3.1 Pro Highspeed, the 2–3× faster variant, has no weights repository of its own. The download covers the base Pro model only. If you need Highspeed, that is an API-only path.

4. Read the licence before you ship anything on it

The weights ship under the Kimi K3.1-Pro License, described as MIT-style. For most developers it behaves like a permissive licence — use, copy, change, fine-tune, sell, as long as you keep the copyright notice. But it carries scale conditions: the terms change for large hosts and very large products.

If you are building a hosted service on these weights, read the licence text itself. "Free to use" has an asterisk at scale, and the asterisk is the kind of thing that only shows up in a terms review, never in a launch tweet. Also note the precision point: this is open-weight, not fully open source. The training data and training code are not published.

5. Do the cost math before you choose API vs self-host

Worked from Moonshot's published rates. A heavy coding-agent session — 200,000 input tokens, 20,000 output — costs $0.40 + $0.20 = $0.60. A small team at 50M input + 10M output tokens a month pays $200/month; with aggressive prompt caching the input side drops to $10 and the bill becomes $110.

Self-hosting only wins on cost if your volume is high and your GPU time is already paid for. For most teams the rational move is API now, weights as leverage — for evaluation, for negotiation, and for the option to self-host later when quantization and hardware catch up.

6. Evaluate on your own code, not on a leaderboard

This is the single most useful thing about an open-weight release: you can benchmark K3.1 Pro on your own codebase, your own tasks, your own harness, with no per-token meter running while you test. Build a small eval set — ten real tasks from your repo, with pass/fail criteria you trust — and run the model against it before you believe any leaderboard, including Moonshot's.

7. Watch for what comes next

Expect GGUF builds, quantization experiments, and fine-tunes within days. Expect the first independent benchmark runs to confirm or revise the 65.7% figure. And treat the unconfirmed report that K3.1 Pro trained on GPT-6 Astra's outputs as rumour until Moonshot or an independent audit addresses it.

Bottom line

A near-frontier coding model you can download is a real event. But the download is the easy part. The work is the eval harness, the licence read, the cost math, and the deployment decision — and none of those come in the 96 files.

Shivam Kumar is the founder of VisionQuantech. Sources: Moonshot AI model card for moonshotai/Kimi-K3.1-Pro (Hugging Face); Moonshot AI technical blog, October 24, 2026. Benchmark figures are Moonshot's own measurements; independent verification is pending. Cost calculations are the author's own arithmetic from Moonshot's published rates ($2/$10 per million tokens, cached input $0.20).