Xiaomi MiMo V2.6 Locally: Run the 9B on Windows and Kali

Xiaomi MiMo V2.6 arrived on September 22, 2026 as four models, and only one of them runs on a normal computer: MiMo-V2.6-Distill-Qwen-9B, a 9B model that fits on an 8 GB graphics card as a 5.84 GB download. The Pro (1.02 trillion parameters) and Flash (309 billion) are data-center models. Here is the surprise almost every headline skipped: the MiMo you can run at home is not a small MiMo at all. It is Alibaba's Qwen3.5-9B, fine-tuned on answers written by Xiaomi's big models, and Xiaomi itself released it as a starting point for research, not a finished assistant. This guide shows how to run it on Windows and Kali Linux with LM Studio, Ollama or llama.cpp. It also covers what the whole Xiaomi MiMo AI family costs, and which parts are free.

The night Jake downloaded 60 GB of the wrong MiMo

Jake's phone-repair shop runs on a spreadsheet and a Python script his cousin wrote years ago. The script breaks roughly every other month, and each time it does, Jake loses a morning. So when his feed filled up with "Xiaomi's new open model beats the big labs at coding," he did the sensible thing and decided to run it himself. Free, private, and on the shop PC.

He found the Hugging Face page for MiMo-V2.6-Flash, noticed a GGUF version, and clicked download. At midnight the progress bar said 60 GB, with a long way to go. That was when he read the file size properly: about 126 GB for the smallest version, on a PC with 32 GB of RAM and an 8 GB graphics card. The download ate half his monthly data allowance, and the model could never have run anyway.

"I thought open source meant I could run it," he told Ethan the next day, a bit embarrassed.

"Open source means you're allowed to run it," Ethan said. "Whether you can is a different question, and the answer depends on the size. Xiaomi released one model in this family that's built for a machine like yours. It just isn't the one in the headlines."

If you made the same download, you are in very good company. The model pages list the giants first, and nothing on them says "not for your laptop." This guide is the sign that should have been there.

What is Xiaomi MiMo AI?

MiMo is the AI model family from Xiaomi, the phone and electronics company. The team is led by Luo Fuli, who worked at DeepSeek before joining Xiaomi. The family has grown fast, and knowing its history makes the search results much less confusing:

Two things stand out. First, MiMo-7B was the original "MiMo you can run locally," and plenty of older tutorials and Ollama tags still refer to it. If a guide says "MiMo 7B," it is about the 2025 model, not V2.6. Second, Xiaomi moved from proprietary back to open weights between V2 and V2.5. The V2.6 Pro and Flash are open under the MIT license, which is part of why this release drew so much attention.

MiMo Studio is Xiaomi's chat website for the big models, a bit like the web chat apps from other AI labs. MiMo Desktop is its desktop app. The Xiaomi MiMo API Open Platform is the paid developer service behind them. When people search "xiaomi mimo ai," they usually mean one of those three. When they search "mimo gguf" or "mimo local," they mean the 9B, and that is where most of this guide goes.

The MiMo V2.6 family: which model runs where

That table is why Jake's download could never have worked. Flash's "active parameters" figure of 15B sounds laptop-sized, but it is a mixture of experts model. Only 15B of its parameters do the work on any one token, yet all 309B have to sit in memory, because the model picks different experts for every token. Active parameters decide speed. Total parameters decide whether it fits. Even the community's squeezed 2-bit GGUF of Flash is about 126 GB.

"So the 9B is the only one I can have," Jake said.

"It's the only one you can own," Ethan said. "The big ones you can rent by the token, or use free in the browser. We'll do the money in a minute. First you should know what the 9B actually is, because it isn't what the name suggests."

The shock: the local MiMo is Qwen 3.5 9B wearing MiMo's training

Look closely at the full name: MiMo-V2.6-Distill-Qwen-9B. Xiaomi's model card says it plainly. The model was built by supervised fine-tuning of Qwen3.5-9B, Alibaba's 9B model, on data generated by MiMo's own large models. Its architecture, its tokenizer and its chat format (the <|im_start|> style) are all Qwen's. What Xiaomi added is the training data, and a lot of it:

"Distill" is the word for this. A large, expensive teacher model writes answers, and a small student model learns to imitate them. It is how a lot of the best small models are made in 2026, and there is nothing sneaky about it. The name says so. But it changes what you should expect: this is a Qwen 9B with MiMo's habits, not a miniature of the 1-trillion-parameter Pro.

The second half of the surprise is more important. Xiaomi's announcement describes the 9B as a starting point for reinforcement learning (RL) research. Alongside it, Xiaomi open-sourced "7k+" RL task environments and an end-to-end RL training framework, which one detailed write-up describes as built on verl, uni-agent and mini-swe-agent. Then Xiaomi showed what happens when you run that RL recipe on the 9B:

The right-hand column is not a model you can download. The weights on Hugging Face are the SFT checkpoint in the middle column, which the model card labels as such, and one review summarizes it as an "SFT starting point for RL research, not a finished assistant." The bigger numbers show what the recipe can produce if you run the RL training yourself, on a real GPU budget.

"So the headline numbers are from a model that doesn't exist?" Jake asked.

"They're from a model that exists in Xiaomi's lab," Ethan said. "What they gave you is the model before its final exam, plus the exam papers and the teaching method. It's generous, it's honest, and it's labeled. It's just not what the social posts implied."

MiMo 9B benchmarks: how good is MiMo coding, really?

Here is the full comparison from Xiaomi's model card, the SFT checkpoint you actually download against the Qwen3.5-9B it started from. All numbers are Xiaomi's own.

Read it carefully, because the gains are not spread evenly. The biggest jumps are on Xiaomi's own "mini" tests, where MiMo-style data would naturally help most, and on agent benchmarks like AutomationBench, where the base model barely functioned. On the most widely used independent coding benchmark, SWE-bench Verified, the gain is small: 60.0 to 61.1. The meaningful public result is SWE-bench Pro, 32.0 to 44.6, a harder, more realistic test where a 12.6-point jump for a 9B model is a big deal.

So "how good is MiMo coding" gets an honest answer in two parts. At 9B, it is among the best local coding helpers you can run on an 8 GB card, especially for multi-step, agent-style work. It is not a replacement for the large cloud models. Xiaomi's own V2.6 Pro is in a different league, and it is priced like it.

Is Xiaomi MiMo free? Studio, API and pricing

This is where most "xiaomi mimo ai" searches end up, so here is the whole picture in one table.

Xiaomi says the V2.6 series keeps the same API prices as V2.5, so older pricing pages still apply. The API is OpenAI-compatible: point any OpenAI-style client at https://api.xiaomimimo.com/v1 with your API key from the open platform console. Xiaomi also offers an Anthropic-compatible endpoint for coding tools built around that format. Prices and plan names change often, and the subscription figures above come from third-party roundups, so check the platform's own pricing page before you commit money.

"So the big one is free in the browser," Jake said, "and the small one is free on my PC."

"And the difference is who can read your prompts," Ethan said. "MiMo Studio runs on Xiaomi's servers, like every web chat does. The 9B runs in your shop and tells nobody anything. For your customer spreadsheet, that's the one."

Which MiMo 9B GGUF to download for your machine

The two GGUF sources most people use are bartowski's repository (every size from 3.54 GB to 17.92 GB, quantized with llama.cpp release b10964) and ggml-org's, the llama.cpp project's own conversion (a single Q8_0 at 9.53 GB, with a Q8_0 vision projector). Here are the sizes that matter, from bartowski's table:

For vision you also need a projector file: bartowski offers mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf and a bf16 version. Without it the model runs text-only.

No GPU at all? A 9B at Q4_K_M runs on a modern CPU with 16 GB of RAM. It works, but answers arrive at reading pace rather than in a rush, and thinking mode makes the wait longer. For coding questions, where you read every line anyway, that is often fine.

How to run MiMo V2.6 9B in LM Studio on Windows

LM Studio is the gentlest route on Windows, and bartowski's GGUFs are built to work in it.

1. Download LM Studio from lmstudio.ai and install it. The default options are fine.
2. Open the model search (the magnifying-glass icon on the left).
3. Search for MiMo-V2.6-Distill-Qwen-9B. Choose the bartowski repository.
4. Pick Q4_K_M for an 8 GB card, or Q6_K for 12 GB or more, and click download.
5. When it finishes, load the model from the top bar. For image input, make sure the vision projector is downloaded too. LM Studio shows a vision badge on models that have one.
6. Start with a coding question, since that is what this model was trained for. For example: "Here is my Python script and the error it prints. What is wrong?"

"That's it?" Jake asked.

"For chat, that's it," Ethan said. "The one place LM Studio trips on this model is tools. Hold that thought for the troubleshooting section."

If you use LM Studio's agent features, know that there is an open bug report, filed September 22, 2026 against LM Studio Bionic 1.1.4 on Windows: with MiMo-V2.6-Distill-Qwen-9B, a tool request hangs on "Working..." indefinitely, while plain Qwen3.5-9B handles the same request fine. Plain chat is unaffected. Until it is fixed, use the model for chat in LM Studio and use Ollama or llama.cpp for tool calling.

How to run MiMo 9B with Ollama on Windows and Kali

At the time of writing there is no official Ollama library tag for MiMo V2.6. The community build most people use is maternion/mimo-v2.6. Its GGUFs carry what its author calls a "community-fixed chat template (JSON tool-call arguments + prefilled reasoning boundary)." That fix matters, because it is exactly what the LM Studio tool bug and the llama.cpp template note are about.

On Windows: download the installer from ollama.com, run it, then open PowerShell. On Kali: install with Ollama's official script, then use the same commands.

curl -fsSL https://ollama.com/install.sh | sh

Then pull and run:

1. Thinking mode, the default: ollama run maternion/mimo-v2.6:9b
2. Instruct mode for quick answers without reasoning: ollama run maternion/mimo-v2.6:9b-instruct --think=false
3. Higher quality if you have the memory: the 9b-thinking-q6_K or 9b-instruct-q6_K tags (7.4 GB), or the q8_0 tags (9.5 GB).
4. Type your question, or /bye to exit.

You can also point Ollama straight at a Hugging Face GGUF, for example ollama run hf.co/bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q4_K_M. That works for chat, but you get bartowski's file with the template as shipped, not the community tool-calling fix. For agent work, prefer the maternion tags.

Running MiMo 9B with llama.cpp (Windows and Kali), with vision

Plain llama.cpp gives you the most control and the most reliable vision support. Unlike some brand-new models, MiMo 9B needs no special fork, because its Qwen3.5 architecture is already in mainline llama.cpp. bartowski's quants were made with release b10964, so use that build or newer.

1. Download a current release from github.com/ggml-org/llama.cpp/releases. On Windows, take the CUDA build for NVIDIA or the Vulkan build for AMD and Intel. On Kali (and macOS), bartowski's model page also points to a one-line installer: curl -LsSf https://llama.app/install.sh | sh. As with any piped install script, read it first if you like to know what runs on your machine.
2. Let llama.cpp fetch the model for you: llama-server -hf bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q4_K_M -ngl 99 -c 16384
3. For vision with a file you downloaded yourself, add the projector: --mmproj mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf
4. For tool calling, start the server with --jinja so it uses the model's chat template properly.
5. Open http://localhost:8080 for the built-in chat, or connect apps to http://localhost:8080/v1.

llama-server -hf bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q4_K_M -ngl 99 -c 16384 --jinja --port 8080

The model pages from ggml-org and bartowski also show a shorter form, llama serve -hf ..., used by newer llama.cpp launchers. Both do the same job.

MiMo on Kali Linux: the cybersecurity model angle

If you are a Kali user, one number in Xiaomi's training table should catch your eye: 14.2% of the training data, 11.0 billion tokens, is cybersecurity material, and the model's score on Xiaomi's cyber test rose from 5.7 to 31.3 over the Qwen base. It is one of very few open 9B models deliberately trained for security work, which makes it a natural lab partner on Kali.

Setting it up on Kali follows the Ollama or llama.cpp steps above. On an NVIDIA machine, install the driver first with sudo apt install -y nvidia-driver nvidia-cuda-toolkit and reboot. Then:

curl -fsSL https://ollama.com/install.sh | sh
ollama run maternion/mimo-v2.6:9b-instruct --think=false

Good uses in a Kali workflow: explaining what an nmap or Nikto output means, drafting a report section from your notes, reading an unfamiliar exploit's source code to understand it, writing a small parser for tool output, or talking through why a CTF challenge isn't yielding. Running locally matters here more than anywhere. Scan results and client findings from an engagement should never be pasted into a cloud chatbot, and with a local 9B they never leave your machine.

Kali users on VirtualBox or VMware usually cannot pass a GPU through to the virtual machine, so the model runs on the CPU there. It still works for this kind of occasional, read-and-think use. For heavier work, run Ollama on the host and point the Kali VM at it with OLLAMA_HOST set to the host's address.

Thinking vs instruct mode, and settings that help

Like its Qwen base, MiMo 9B can "think out loud" in a reasoning block before it answers. Xiaomi's model card enables this with enable_thinking in the chat template. The community Ollama build makes it the default for the 9b tag.

• Use thinking mode for debugging, multi-step coding tasks and anything with a chain of logic. It is slower and much better.
• Use instruct mode (9b-instruct with --think=false) for quick lookups, rewording and short answers. It is faster and uses less context.
• Give it room. Thinking uses tokens. If long answers stop halfway, raise the context (-c 16384 or more in llama.cpp, or the context setting in LM Studio) before you blame the model.
• Keep a system prompt short and specific. "You are a senior Python developer. Answer with code first, then explanation." A 9B model follows short instructions better than long ones.

"Which mode for my script?" Jake asked.

"Thinking, the first time, when you don't know what's wrong," Ethan said. "Instruct, when you already know and just want it written. It's the difference between asking a mechanic to diagnose the noise and asking them to fit the part."

Xiaomi MiMo API key: your first call in five minutes

If you want the big V2.6 Pro or Flash inside your own scripts, the API is the route, and because it is OpenAI-compatible, the code is almost boring. That is a compliment.

1. Sign in to the Xiaomi MiMo API Open Platform with a Xiaomi account and open the console.
2. Add a payment method or buy credits. The API is pay as you go, and a Token Plan subscription is a separate product aimed at coding tools.
3. Create an API key in the console and copy it somewhere safe. Treat it like a password, because anyone holding it spends your money.
4. Store it in an environment variable rather than in your code. In PowerShell: $env:MIMO_API_KEY="paste-key-here". On Kali: export MIMO_API_KEY="paste-key-here".
5. Make a first request with the official OpenAI Python library (pip install openai), pointing it at Xiaomi's base URL.

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["MIMO_API_KEY"],
                base_url="https://api.xiaomimimo.com/v1")

reply = client.chat.completions.create(
    model="use-the-model-id-from-the-console",
    messages=[{"role": "user", "content": "Explain what a race condition is in two sentences."}],
)
print(reply.choices[0].message.content)

Copy the exact model ID from the console's model list into the model line. IDs sometimes differ from the marketing names, and a wrong ID is the most common first-call error after a wrong key. If you get a 401, the key is the problem. If you get a 404 or "model not found," the ID is.

Using MiMo 9B as a free coding assistant in your editor

Most of "xiaomi mimo code" searches are really one wish: an AI helper inside the code editor, without a subscription. The local 9B can do that, because both Ollama and llama-server speak the same API that editor extensions already understand.

The pattern works in any editor extension that supports Ollama or a custom OpenAI-compatible server, and most of the popular open-source ones do:

1. Keep the model running: ollama run maternion/mimo-v2.6:9b in one terminal, or llama-server as shown above.
2. In the extension's settings, choose Ollama as the provider and maternion/mimo-v2.6:9b as the model. Or choose an OpenAI-compatible provider with the base URL http://localhost:11434/v1 for Ollama, or http://localhost:8080/v1 for llama-server.
3. Leave the API key field empty, or put in any placeholder text if the extension insists. Local servers do not check it.
4. Use the chat panel for "explain this function" and "why does this fail," which is where a 9B shines. For fast inline autocomplete, a smaller dedicated completion model often feels snappier, and you can run both side by side.

"So it's like having the cousin sitting next to me," Jake said, "except he never gets bored of my questions."

"And he never sees your customer list," Ethan said. "Which, given your cousin, is a feature."

A practical tip: give the assistant the error message, not a description of it. "It crashes when I click Save" gives a 9B very little to work with. The actual traceback gives it nearly everything. That is true of every model, but small models reward it most.

MiMo 9B on a Mac

Apple Silicon Macs run MiMo 9B well, because the GPU shares the system's unified memory. A 16 GB MacBook Air can hold the Q4_K_M file with plenty of room left.

• LM Studio for Mac is the easiest route. Same search, same bartowski files.
• bartowski's Q4_1 file (5.94 GB) is listed as having "improved tokens/watt on Apple silicon." On a laptop running on battery, that is the one to pick.
• Ollama for Mac runs the same maternion/mimo-v2.6 tags with the same commands.
• 8 GB Macs can run IQ4_XS (5.23 GB), but macOS itself needs room, so keep the context small and close other apps.

Xiaomi MiMo not working? Fixes for every common error

"Half of that table is 'wrong file,'" Jake said.

"And the other half is 'right file, unfinished template,'" Ethan said. "Brand-new models always arrive a bit rough. Week one, you're an early adopter. Week six, you're just a user. Both are fine, as long as you know which week you're in."

For IT admins: MiMo 9B in a business

• License: MIT, the most permissive common license. Commercial use, modification and redistribution are all allowed. Keep the copyright notice.
• Base-model lineage: because it is a fine-tune of Qwen3.5-9B, your legal review may also want to see the Qwen3.5-9B license terms. Check that the base license suits your use, not only MiMo's.
• Data residency: the local 9B sends nothing anywhere. MiMo Studio, MiMo Desktop and the MiMo API all process data on Xiaomi's servers. For regulated data, that difference is the whole decision. Document which one staff may use.
• Security content: the model was deliberately trained on cybersecurity data. That is an asset for a security team and something to note in an acceptable-use policy for everyone else.
• Supply chain: pin exact GGUF files by their SHA256 hash (Hugging Face shows it on each file page) and mirror them internally, rather than letting each machine pull "latest" from a community repository.
• Fleet rollout: deploy Ollama or llama.cpp through Intune, Configuration Manager or your RMM tool. Serve one shared model on a GPU server behind your reverse proxy rather than one copy per laptop.

Best 9B or 12B AI model for coding: where MiMo fits

"Best 9B or 12B AI model for coding" is one of the most common searches around this release, and a fair answer has to admit that nobody has published a clean head-to-head of every 9-12B model on the same coding benchmarks. Here is what can be said honestly:

• Against its own base, Qwen3.5-9B, MiMo 9B is better at nearly everything Xiaomi measured. The gain is small on SWE-bench Verified and large on SWE-bench Pro and agent tasks. If you already use Qwen3.5-9B for coding, MiMo is a free upgrade for multi-step work.
• Against the original MiMo-7B from 2025, it is a different generation. Use the V2.6 9B.
• Against 12B-class models, we do not have matched numbers. Try your own repository's real tasks for an evening. That tells you more than any leaderboard.
• Against the big cloud models, a 9B does not compete, and neither does any 9B from any lab. Where it wins is price (zero), privacy (total) and availability (it never has an outage).

Xiaomi MiMo vs DeepSeek comes up often too. At the flagship level, independent evaluator Artificial Analysis placed MiMo-V2.6-Pro at 46 on its Intelligence Index, reported as the top open-weight score at release. At the local level, the comparison that matters is file size against your hardware. MiMo's 9B fits an 8 GB card. Most DeepSeek flagship models do not fit any consumer machine at all.

The verdict: should you install MiMo 9B?

Yes, if you want a local coding and agent helper on an 8 GB GPU. Q4_K_M in Ollama or LM Studio, thinking mode for real problems. It is one of the best free tools of its size for that job.

Yes, if you work in security on Kali. The deliberate cyber training and fully local inference are a rare pairing.

Maybe, if you want general chat. It works, but the training mix leans hard toward code, tools and security. A general-purpose model of the same size may feel more natural for everyday questions.

No, if you expected the V2.6 Pro in a small box. For that experience, use MiMo Studio free in the browser, or the API. Just keep sensitive data out of both.

Jake settled on the maternion 9b tag on the shop PC, with thinking on. Its first job is the cousin's spreadsheet script, under one rule taped to the monitor: read every line it suggests before you run it. "Same rule as the cousin," Ethan said. "Just faster to ask."

Xiaomi MiMo V2.6 FAQ

What is Xiaomi MiMo AI?

MiMo is Xiaomi's family of AI models, led by Luo Fuli, beginning with MiMo-7B in April 2025. The September 22, 2026 V2.6 release includes MiMo-V2.6-Pro (1.02 trillion parameters), Flash (309B), Pro-UltraSpeed (API only) and Distill-Qwen-9B, the one model sized for a home PC.

What is the MiMo V2.6 release date?

Xiaomi announced the MiMo-V2.6 series on September 22, 2026. Some trackers list September 21 because of the time-zone difference with China. The Pro, Flash and Distill-Qwen-9B weights were published as open weights at release.

Is Xiaomi MiMo free?

Partly. MiMo Studio at aistudio.xiaomimimo.com lets you chat with the V2.6 Pro and Flash for free with a Xiaomi account, and the Distill-Qwen-9B runs free on your own PC under the MIT license. The API and Token Plan subscriptions are paid.

How much does the Xiaomi MiMo API cost?

MiMo-V2.6-Flash costs $0.14 per million input tokens and $0.28 per million output. MiMo-V2.6-Pro costs $0.435 per million input and $0.87 per million output, with cached input much cheaper. Pro-UltraSpeed is $4.35 and $8.70. Xiaomi kept V2.6 prices the same as V2.5.

What is Xiaomi MiMo Studio?

MiMo Studio is Xiaomi's web chat for its large MiMo models, at aistudio.xiaomimimo.com, with a MiMo Desktop app as well. It runs on Xiaomi's servers, so it is ideal for trying the big models and not for private or client data.

Can I run Xiaomi MiMo locally?

Yes, the MiMo-V2.6-Distill-Qwen-9B model. Its Q4_K_M GGUF is 5.84 GB and runs on an 8 GB GPU through LM Studio, Ollama or llama.cpp, or on a CPU with 16 GB of RAM. The Pro and Flash models need data-center hardware.

Is the MiMo 9B really a Qwen model?

Yes. Xiaomi built it by supervised fine-tuning of Alibaba's Qwen3.5-9B on 77.4 billion tokens of data generated by MiMo's larger models, covering code, general agent tasks, visual work and cybersecurity. It keeps Qwen's architecture and chat format.

How do I run MiMo with Ollama?

There is no official Ollama library tag yet. The community build runs with ollama run maternion/mimo-v2.6:9b for thinking mode, or ollama run maternion/mimo-v2.6:9b-instruct --think=false for quick answers. It includes a fixed chat template for tool calls.

Does MiMo 9B work in LM Studio?

Yes, for chat and vision, using bartowski's GGUF files. One open bug report says agent tool requests hang on Working... in LM Studio Bionic 1.1.4. Use Ollama or llama.cpp with a fixed template for tool calling until it is resolved.

How much VRAM does MiMo 9B need?

About 8 GB for the Q4_K_M file (5.84 GB) with a moderate context, 10-12 GB for Q6_K (7.79 GB), and 12 GB or more for Q8_0 (9.55 GB). On a CPU, 16 GB of system RAM runs Q4_K_M, more slowly.

How good is MiMo at coding?

For a 9B, very good at multi-step coding: SWE-bench Pro rose from 32.0 to 44.6 over its Qwen3.5-9B base. On SWE-bench Verified the gain is small (60.0 to 61.1). It is a strong free local coding helper, not a replacement for large cloud models.

Why are the MiMo 9B benchmarks in the announcement higher than the model card?

The announcement also shows results after Xiaomi's own reinforcement-learning run, such as Terminal Bench 2.1 rising to 52.8. The downloadable weights are the SFT checkpoint before that RL stage, which Xiaomi released with 7,000+ RL environments so researchers can run the RL step themselves.

Is MiMo V2 Pro open source?

MiMo-V2-Pro from March 2026 was proprietary. The later MiMo-V2.5-Pro and MiMo-V2.6-Pro were released as open weights under the MIT license, although at about 566 GB the V2.6 Pro needs a multi-GPU server to run.

Can I run MiMo-V2.6-Flash GGUF at home?

Not on a normal home PC. Flash has 309 billion total parameters, and even its community 2-bit GGUF is about 126 GB. Its 15B active parameters describe speed, not memory. All 309B must be loaded. Use the 9B locally, or Flash through MiMo Studio or the API.

Is MiMo good for Kali Linux and cybersecurity?

It is one of the few open 9B models trained deliberately on security data: 14.2% of its training tokens. Its score on Xiaomi's cyber test rose from 5.7 to 31.3. It suits explaining tool output, drafting reports and reading code, on systems you are authorized to test.

What license is MiMo V2.6 released under?

The open-weight V2.6 models, including the Distill-Qwen-9B, are MIT-licensed, which permits commercial use. Because the 9B is fine-tuned from Qwen3.5-9B, businesses should also review the Qwen base model's license terms.

Will there be a Xiaomi MiMo V3?

Xiaomi has not announced a MiMo V3. The family has moved quickly, from V2 in December 2025 to V2.5 in April 2026 and V2.6 in September 2026, but any V3 date would be a guess.

How do I use MiMo in thinking mode?

Thinking is the default for the 9b and 9b-thinking Ollama tags, and Xiaomi's template switches it on with enable_thinking. Use thinking for debugging and multi-step tasks, and the 9b-instruct tag with --think=false for quick answers.

If you ended up where Jake did, halfway through a 126 GB download at midnight, please don't be hard on yourself. The model pages list the giants first and never say which one fits your machine. The 9B is the MiMo that was built for you, and it is a genuinely capable little coder once it is running. If anything here does not match what you see on your screen, tell me through the contact page. This guide will be updated as the chat template fix lands upstream and as an official Ollama tag appears.

Revision note. Written September 26, 2026, four days after the MiMo-V2.6 release. It covers the MiMo-V2.6-Distill-Qwen-9B SFT checkpoint, bartowski's GGUFs (llama.cpp b10964), ggml-org's Q8_0 conversion and the maternion/mimo-v2.6 Ollama build as of that date. Benchmarks are Xiaomi's published figures, and prices come from Xiaomi's announcement and current pricing roundups. Next update: when the chat template fix ships upstream, when an official Ollama tag appears, or when API prices change. Happy building. A private coding helper on your own machine is a lovely thing to have.