Kimi K3.1 Pro’s full weights are free to download as of November 4, 2026 — and for almost every freelancer and small team reading this, that download is a trophy, not a tool. The model is 2.8 trillion parameters, needs roughly 1.5 terabytes of VRAM at usable precision, and has no GGUF build yet. The good news: the API costs $2 per million input tokens and $10 per million output tokens, which makes a serious coding job cost less than two dollars. This article gives you the license terms, the real hardware math, and the decision that actually makes sense for your budget.
What Actually Dropped on November 4
Moonshot AI launched Kimi K3.1 Pro on October 23, 2026, as a hosted model. The launch got attention because the benchmarks looked strong and the price undercut the big proprietary models. But “open weights” was a promise at launch, not a fact. That changed on November 4.
The full weights went public on Hugging Face as moonshotai/Kimi-K3.1-Pro, ungated — no form, no waitlist, no approval email. The repository’s first commit landed November 2, the weights went public around 15:31 UTC on November 4, and Moonshot announced it on X at 16:00 UTC the same day. The download is 96 safetensors shards totaling about 1.56 terabytes — on a typical 100 Mbps home connection, roughly a day and a half of continuous downloading, and that’s before you discover you have nowhere to run it.
K3.1 Pro is the first model in Moonshot’s K3.1 series, following K3, which launched in July 2026. Our earlier piece covered the October 23 launch and the promise of open weights; this is the weights-day verdict — the files are out, the license is readable, and the hardware requirements are now measurable rather than speculative.
The Model, in Plain English
Strip away the marketing and here’s what you’re looking at:
- 2.8 trillion total parameters, 104 billion active per token. It’s a mixture-of-experts (MoE) model with 896 experts across 93 layers. Only a fraction of the model fires for any given token, which is why the “active” number matters more than the total for speed — and why the total number matters more than the active one for memory.
- 1 million tokens of context (1,048,576, to be exact). That’s roughly 750,000 words of context in a single conversation — enough to hold a large codebase, a pile of research documents, or a very long transcript without the model forgetting the beginning.
- Natively multimodal. It handles text and images together, not as a bolt-on. You can feed it screenshots, diagrams, and documents alongside your prompts.
- Stored as MXFP4 weights with MXFP8 activations, the product of quantization-aware training. In plain terms, Moonshot trained the model knowing it would be stored in a compressed format, so the compression costs less accuracy than it would on a model trained at full precision and squeezed afterward.
- Vision encoder: MoonViT-V2, 401 million parameters. That’s the part that reads images.
The recommended runners are vLLM, SGLang, and TokenSpeed — serious inference engines, not laptop apps.
Can You Actually Run It? The VRAM Table
This is the question that matters, and the answer is no — not on anything you own, and probably not on anything your company owns either. The estimates below come from VRAMGlass, calculated from the K3 files at the same bit width. The weights themselves are published on Hugging Face at moonshotai/Kimi-K3.1-Pro — ungated, 96 files totalling about 1.56 TB. There are no GGUF builds yet, so these are the numbers for the formats that exist today.
| Precision | VRAM needed | What machine that actually is |
|---|---|---|
| 1-bit | No estimate published yet | Doesn’t exist in a usable form — and 1-bit would still be enormous |
| 2-bit | ~861 GB | A small data-center cluster; far beyond any workstation |
| 4-bit | ~1,509 GB | vLLM needs 8 data-center GPUs with 288 GB VRAM each |
| 8-bit | ~1,561 GB | Same 8-GPU cluster, with even less headroom |
Read that table again. The smallest realistic number is 861 GB of VRAM at 2-bit precision — and 2-bit is a heavily degraded format you’d only use if you had no other choice. At 4-bit, the precision most people would actually want, you’re at about 1,509 GB.
For comparison, a high-end consumer GPU in 2026 ships with 24 GB of VRAM, and a top workstation card might have 48 GB. You would need more than thirty of those top workstation cards to hold this model at 4-bit, and consumer cards can’t be pooled that way in practice. The realistic path is eight data-center GPUs with 288 GB of VRAM each — hardware that lives in a rack and costs more than a car per card.
There is one escape hatch, and it’s barely an escape: expert offloading. VRAMGlass estimates you could run it with 32 GB of VRAM plus about 1,480 GB of system RAM, by keeping most of the experts in RAM and swapping them in as needed. That is technically possible on a very beefy workstation — but “possible” is doing a lot of work there. You’d need nearly 1.5 terabytes of RAM, the model would run slowly because RAM is far slower than VRAM, and you’d spend your time managing infrastructure instead of doing client work. For a freelancer, that’s a hobby project, not a business tool.
The honest summary: no single GPU or workstation on the market can hold Kimi K3.1 Pro. If someone tells you they run it locally, ask them how many GPUs are in the rack behind them.
Three Ways to Use It: The Decision Table
So the weights are free, but running them is out of reach. That leaves three practical ways to actually use Kimi K3.1 Pro — and the right one depends entirely on who you are.
| Option | What it costs | Who it suits |
|---|---|---|
| API (Moonshot) | $2 per million input tokens, $10 per million output tokens; $0.20 cached input | Freelancers, small teams, and anyone who wants the model’s quality without infrastructure |
| Self-host the weights | 1.56 TB download + 8 data-center GPUs with 288 GB VRAM each, plus power, cooling, and your time | AI labs, large companies, and hosting providers who serve the model to others |
| Free tier on kimi.ai | Free, with limits | Trying it out, light personal use, and deciding whether the paid API is worth it |
The API is the obvious winner for almost everyone reading this blog. You get the full model — the same weights, the same quality — without the download, the hardware, or the maintenance. Moonshot also offers a highspeed tier at $4 per million input tokens and $20 per million output tokens, which they say runs two to three times faster. For most freelance work, the standard tier is fine; the highspeed tier is there for the day a deadline makes speed worth double the price.
The free tier on kimi.ai is the right starting point if you’ve never used Kimi before. It exists, it works, and it has limits — which is fair for a free product. One thing worth knowing: on October 25, Moonshot apologized on X for rate limits and quota problems on the subscription plan, and gave every subscriber a one-time reset restoring 25% of their monthly quota.
Self-hosting is for a different species of user entirely. If you’re building a product that resells model access, or you’re a research lab, or you have compliance reasons to keep inference in-house — then yes, download the weights, that’s what they’re for. If you’re a freelancer who writes code or content for clients, self-hosting is the most expensive way to get the worst version of the experience.
The Math: A Real Freelance Job on the API vs Self-Hosted
Let’s stop talking in abstractions and price a real job. If you want the broader picture first — what the model can actually do for client work — our Kimi K3.1 Pro open model guide for freelancers covers the use cases; this section prices one of them. Say a client hires you to refactor a medium-sized codebase and add a feature. You feed the model the relevant code and documentation, it reasons through the changes, and it writes the new code. A realistic token budget for that job:
- 400,000 input tokens — the codebase, the docs, your instructions, and a few rounds of back-and-forth context
- 100,000 output tokens — the new code, explanations, and revisions
On Moonshot’s standard API pricing:
- Input: 400,000 tokens × $2 per million = $0.80
- Output: 100,000 tokens × $10 per million = $1.00
- Total: $1.80
And it gets cheaper if you use the API well. Cached input costs $0.20 per million tokens instead of $2. If 300,000 of those input tokens are repeated context the API has already seen — the same codebase sent across multiple calls, for instance — that portion costs $0.06 instead of $0.60. The same job then costs about $1.26. Either way, you’re talking about the price of a coffee for one job, and a cheap coffee at that.
Now the same job self-hosted. You can’t price the hardware exactly, because nobody publishes a clean “8× 288 GB GPU server” price tag — but a conservative back-of-envelope estimate puts eight data-center GPUs of that class, plus the server to hold them, at roughly $80,000 before you pay for power, cooling, storage, and the day you lose to setup. Treat that $80,000 as a rough estimate, not a quote; the exact number moves with the market, but the order of magnitude doesn’t.
Divide the two numbers: $80,000 ÷ $1.80 per job = about 44,000 jobs before the hardware pays for itself. At one such job per working day, that’s roughly 170 years. And that math generously assumes the hardware costs you nothing to run — no electricity bill, no failed drives, no time spent updating drivers and inference engines, and no model going stale while a newer version launches. In reality, the break-even is further away than that, not closer.
The conclusion isn’t close. For any normal freelance workload, the API wins by orders of magnitude. Self-hosting only starts to make sense when your token volume is so large and so constant that the API bill exceeds the cost of a hardware team — and if that’s your situation, you already know it, because you’re looking at five-figure monthly API invoices.
The License: The $20M Clause, Explained Honestly
Open weights doesn’t mean “no strings attached,” and Kimi K3.1 Pro’s license is the part most coverage skims past. It’s worth reading carefully, because it’s genuinely permissive for almost everyone — with two conditions that sound scarier than they are.
The license is called the Kimi K3.1 Pro License. It’s MIT-style: you’re permitted to use, copy, modify, and sell the model and things built with it. But it is not a plain MIT license. It has two conditions:
Condition 1: the model-as-a-service revenue cap. If you run a model-as-a-service business — meaning you sell access to the model itself — and your aggregate revenue, counting your affiliates, exceeds $20 million over any 12-month period, you must sign a separate agreement with Moonshot before continuing commercial use. Read that again: it only applies if you resell model access and you make more than $20 million a year doing it. If you use the model to write code for clients, draft content, or build apps that happen to call an AI, this is not you. You are not a model-as-a-service business; you’re a customer of one.
Condition 2: the attribution requirement. If your product has more than 100 million monthly active users, or more than $20 million in monthly revenue, you must display the model name. Again: this is a threshold for companies the size of a small country. If your app has 100 million monthly users, you have lawyers who already read this license before you did.
Here’s the check to run, and it’s a short one. Ask yourself: am I selling access to this model as my product? If the answer is no — and for freelancers, agencies, and small SaaS teams it almost always is — the license is effectively permissive for your use. If the answer is yes, or might become yes, read the full license text on the Hugging Face repo before you build a business on it, and talk to a lawyer when your revenue starts approaching that $20 million line. The trap isn’t that the license is restrictive; it’s that people either ignore it entirely or panic about it unnecessarily. The truth sits in the middle: generous terms, with a tripwire drawn far above where most of us will ever stand.
The Benchmarks: Read Them Like a Skeptic
Moonshot’s headline number is a Terminal-Bench 4.0 score of 65.7%, against 66.4% for Claude Opus 5.5 and 58.2% for GPT-6 Astra. That’s a strong result — essentially tied with the best proprietary model on an agentic coding benchmark. But note the label: these are vendor figures, run by Moonshot on their own benchmark choices. Treat them as a claim, not a measurement.
The independent numbers tell a more complicated story. Artificial Analysis gives Kimi K3.1 Pro an Intelligence Index of 55, ranking it 4th out of 215 models — about three points behind the best proprietary model. That’s genuinely impressive for an open-weights model. But Artificial Analysis also rates it slow, at roughly 50 tokens per second, and very verbose. Verbosity matters more than people think: a model that writes twice as many words to say the same thing costs you twice as much on output tokens, and it costs you time reading the extra words. At $10 per million output tokens, a verbose model is a more expensive model, even at the same list price.
There’s also a wrinkle in the training data: Moonshot removed offensive-cyber training data from K3.1 Pro. Their in-house xdev-bench-hard test shows the cost: 81.0% for the redacted model versus 90.5% for an unredacted ablation. If your work involves legitimate security research, you should know the model has been steered away from that territory. For most freelance work — building websites, writing content, automating business tasks — it won’t matter.
And here’s the most important caveat of all: no independent runs on the real weights exist yet. The benchmarks floating around were run by Moonshot or on the hosted API. Nobody outside Moonshot has yet downloaded the 1.56 TB, served the actual weights, and published independent results. That will change in the coming weeks, and when it does, the numbers may move. Until then, everything you read about this model’s performance is either a vendor claim or an API measurement. Don’t make a business decision — like switching your whole stack — on numbers nobody independent has reproduced yet.
The community reaction so far reflects that caution. Threads on r/LocalLLaMA have been skeptical: one poster was unimpressed, arguing the model doesn’t justify its cost against much cheaper options like DeepSeek’s 4.1 Flash; another argued that the Artificial Analysis index itself can be gamed, and claimed K3.1 Pro costs roughly ten times more per task than DeepSeek on the Vals Index. That last claim comes from a commenter, not a verified measurement, so take it as a signal of sentiment rather than a fact. The sentiment is real, though: the open-weights community expected a model they could run, and what they got is a model they can admire from a distance.
The Free Tier: Try Before You Pay
If you haven’t used Kimi before, start on kimi.ai’s free tier. It exists, it gives you the real model, and it’s the cheapest way to answer the only question that matters for your work: does this model do your job well? A benchmark score tells you how a model does on Terminal-Bench. An afternoon with your actual codebase tells you how it does on your actual work.
If you find yourself using free AI tools regularly, it’s also worth knowing how the free tiers fit together — for example, our guide on how to create a Muse AI account with a USA IP and get 1 billion free tokens covers another generous free offer, and comparing the two is a reasonable afternoon’s work if you’re building a low-cost AI toolkit.
Who This Is Actually For — and Who Should Skip It
Use the API if you want a genuinely strong coding and reasoning model, you work with large codebases or long documents, and you like the idea of a 1-million-token context window. At $2 per million input tokens, the price is low enough that trying it costs almost nothing.
Try the free tier if you’re curious, you want to compare it against what you use now, or you only need AI help occasionally. It’s the zero-risk way to find out whether the model’s style fits your work.
Download the weights if you’re a researcher, you’re building a hosted AI product, you need inference inside your own infrastructure for compliance reasons, or you just want to study a frontier-scale open model. That’s who this release is really for.
Skip it entirely if you need something that runs on your own machine, you want the cheapest possible model for simple tasks, or your work is mostly short prompts where a smaller, faster model would do. Kimi K3.1 Pro is a heavyweight, and hiring a heavyweight to carry a light load is bad economics.
The Bottom Line
Kimi K3.1 Pro’s open-weights release is a genuine milestone: a frontier-class model, free to download, with a license that lets almost anyone use it commercially. It’s also a reality check. The weights are free; running them costs a data center. For freelancers and small teams, the smart move is the same as it was on launch day: use the API, pay about two dollars for a serious coding job, and let someone else own the eight GPUs. The download is a gift to researchers and hosting providers. The API is the gift to the rest of us.
Frequently Asked Questions
Is Kimi K3.1 Pro really free to download?
Yes. The full weights are published on Hugging Face as moonshotai/Kimi-K3.1-Pro, ungated, and free to download. The download is about 1.56 TB across 96 safetensors shards. Free to download is not the same as free to run — you’ll need roughly 1,509 GB of VRAM at 4-bit precision, which means eight data-center GPUs with 288 GB of VRAM each.
What is the Kimi K3.1 Pro license?
It’s the Kimi K3.1 Pro License: MIT-style permission to use, copy, modify, and sell, but not a plain MIT license. It has two conditions — a model-as-a-service business earning over $20 million in any 12 months must sign a separate agreement with Moonshot, and products with over 100 million monthly active users or over $20 million in monthly revenue must display the model name. For almost all freelancers and small teams, neither condition applies.
How much VRAM do I need to run Kimi K3.1 Pro?
About 861 GB at 2-bit precision, about 1,509 GB at 4-bit, and about 1,561 GB at 8-bit, according to VRAMGlass estimates. No single GPU or workstation can hold it. The only partial workaround is expert offloading with 32 GB of VRAM and around 1,480 GB of system RAM, which would run slowly and require a very expensive machine.
How much does the Kimi K3.1 Pro API cost?
Moonshot charges $2 per million input tokens and $10 per million output tokens, with cached input at $0.20 per million. A highspeed tier costs $4 per million input and $20 per million output and runs two to three times faster. A realistic 500,000-token freelance coding job costs about $1.80 on the standard tier.
Is Kimi K3.1 Pro better than Claude or GPT?
On Moonshot’s own Terminal-Bench 4.0 figures, it scores 65.7%, essentially tied with Claude Opus 5.5 at 66.4% and ahead of GPT-6 Astra at 58.2%. Artificial Analysis ranks it 4th out of 215 models with an Intelligence Index of 55. But these are vendor figures or API measurements — no independent runs on the real weights exist yet, so treat the numbers as promising rather than proven. Artificial Analysis also notes it is slow at roughly 50 tokens per second and very verbose.
Can I use Kimi K3.1 Pro for free?
Yes, kimi.ai offers a free tier with limits. It’s the best way to try the model before paying. Just be aware that Moonshot had quota and rate-limit problems on the subscription plan in late October 2026 and issued a one-time quota reset to subscribers, so the free tier may have capacity limits during busy periods.