Kimi K3.1 Pro: Specs, Benchmarks, Open Weights Date and Can You Run It Locally?

Moonshot AI has just released Kimi K3.1 Pro, and it is one of the most talked-about AI launches of the week. It is a 2.8 trillion parameter open-weight model with a 1 million token context window, and Moonshot’s own benchmarks put it within touching distance of Claude Opus 5.5 and ahead of GPT-6 Astra on several coding and agent tests. It is live today in Kimi’s apps and API, and the weights are due in November 2026. This guide covers everything confirmed so far: the specs, the benchmarks, the price, how to use it right now, the open weights date, and the honest answer to the question every local AI fan is asking: can you run Kimi K3.1 Pro locally?

What Is Kimi K3.1 Pro?

Kimi K3.1 Pro is the newest flagship from Moonshot AI, the Beijing lab behind the Kimi chatbot. It is post-trained from Kimi K3, which Moonshot open-sourced in July 2026, and it keeps the same giant mixture-of-experts design. MarkTechPost reports that it uses Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), and activates 16 of 896 experts for each token through a framework Moonshot calls Stable LatentMoE.

Moonshot pitches it as “our most capable 3T-class open-weight model”, with a focus on full-project coding (games in Unreal, Unity and Godot), parametric 3D modelling in Blender and CAD, and more reliable finance and office work. The Decoder headline sums up the reaction: Kimi K3.1 Pro is “closing in on” Claude Opus 5.5, and if the weights land as promised it would become the highest-scoring open-weights model available.

One more detail that got attention: Wccftech reports that Moonshot stripped cyber-offence data from training. Its own cyber benchmark shows the shipped model at 81.0% on xdev-bench-hard (pass@3), versus 90.5% for an unfiltered checkpoint.

Kimi K3.1 Pro Specs at a Glance

The number that matters for speed is the 104B active parameters. Once the model is fully in memory, each token costs roughly what a 100B dense model costs, not a 2.8T one. The number that matters for local use is the 2.8T total, because every one of those weights still has to sit somewhere in memory.

Benchmarks vs Claude Opus 5.5 and GPT-6 Astra

These scores are Moonshot’s own, run at max reasoning effort and mostly inside a Claude Code-style harness, as reported by The Decoder. Treat them as a starting point until independent tests arrive.

A few takeaways:

• Long coding tasks are its strength. SWE-Marathon and DeepSWE both favour Kimi, which fits Moonshot’s “full-project coding” pitch.
• Science and CAD lag a little. On Terminal-Bench Science 0.1 it scores 38.1, against 63.3 for Opus 5.5 and 68.1 for GPT-6 Astra.
• Hallucinations are much lower than K3. On Moonshot’s internal Hallu Bench Hard, claim-level hallucination fell from 23.4% (K3) to 8.3%. GPT-6 Astra scores 5.5% on the same test.
• Independent index: On the Artificial Analysis Intelligence Index, K3.1 Pro scores 55 against Opus 5.5’s 58.

Pricing and the Highspeed Version

That is a 33% price cut compared with K3, for a model that scores higher. Moonshot says Highspeed runs two to three times faster, which is worth it for agent loops where latency adds up. Since thinking is always on and the default output limit is 131K tokens, watch your output spend: long reasoning traces are billed as output.

How to Use Kimi K3.1 Pro Today

You do not need to wait for the weights. It is available now in four places:

• Kimi.ai: the free chat app on web and mobile.
• Kimi Work: Moonshot’s agent workspace for documents, slides and research.
• Kimi Code: the coding agent and terminal tool.
• Kimi API: an OpenAI-compatible endpoint you can drop into existing tools.

To get API access, create an account on Moonshot’s platform, top up at least $1 (the minimum, according to the quickstart), and create an API key.

API Example (OpenAI-Compatible)

Because the endpoint is OpenAI-compatible, the standard openai Python package works. Replace the base URL with the one shown in your Moonshot console.

pip install openai

Then in Python:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_MOONSHOT_API_KEY",
    base_url="https://api.moonshot.ai/v1",  # check your console for the exact URL
)

resp = client.chat.completions.create(
    model="kimi-k3.1-pro",            # or "kimi-k3.1-pro-highspeed"
    messages=[{"role": "user", "content": "Write a Godot 4 script for a 2D player with double jump."}],
    reasoning_effort="high",          # low, high or max (default max)
)
print(resp.choices[0].message.content)

Settings to know, from MarkTechPost’s summary of the quickstart:

• Temperature is fixed at 1.0 and top_p at 0.95. Sending other values will not change behaviour.
• Thinking cannot be turned off. Use reasoning_effort="low" for quick answers.
• Image input does not accept public URLs. Send images as base64 or as uploaded file IDs.

You can also point coding agents that accept an OpenAI-compatible endpoint, such as Cline, Roo Code or Aider, at the same base URL and model ID.

Open Weights: Release Date and What to Expect

Moonshot says the weights will be released in November 2026, and The Decoder gives November 4 as the date. Moonshot has a good track record here: Kimi K3 was announced on July 16, 2026 and its weights went up on Hugging Face as moonshotai/Kimi-K3 on July 27, eleven days later.

What we can reasonably expect, based on K3:

• A Hugging Face repo from moonshotai, likely in the low-precision format Moonshot recommended for K3 (MXFP4 weights with MXFP8 activations).
• GGUF quants from Unsloth and others within days, as happened for K3.
• Support in vLLM and SGLang for multi-GPU serving, since K3.1 shares K3’s architecture.

The license has not been announced. Check the model card on release day before using it commercially. I will update this post when the weights land.

Can You Run Kimi K3.1 Pro Locally?

Technically yes, practically only on server-class hardware. K3.1 Pro has the same 2.8T / 104B shape as Kimi K3, so K3’s published GGUF sizes are the best guide we have. Here is Unsloth’s table for Kimi K3:

Expect K3.1 Pro quants to land very close to these numbers. What that means in real hardware:

• Gaming PC (16 to 32 GB VRAM, 64 to 128 GB RAM): no. Even the smallest quant needs about five times more memory than a well-equipped desktop. My RTX 5080 with 16 GB is not in the conversation.
• One 512 GB Mac Studio: not enough for the 594 GB file without slow disk offload.
• Two networked 512 GB Mac Studios (1 TB): enough for 1-bit and 2-bit quants.
• Dual-socket server with 768 GB to 1 TB of DDR5: the cheapest way to run 1-bit or 2-bit quants on CPU, slowly, ideally with one or two GPUs holding the attention layers.
• Full quality: Moonshot recommended supernodes of 64 or more accelerators for K3. That is datacenter territory.

Unsloth’s docs say llama.cpp will still run if your memory is smaller than the file by streaming from disk, but generation becomes much slower. For context, Unsloth reports about 20 tokens per second for K3 on B200 GPUs when it fits in memory.

If you want strong local coding models on a normal PC, smaller open models are the realistic route. I have guides for MiniCPM-V 4.7 on a 16 GB GPU and the hardware needs for Muse Spark Open.

How Local Setup Will Likely Work (llama.cpp)

Until the K3.1 Pro files exist, here is how Unsloth runs Kimi K3 today. Expect the K3.1 commands to differ only in the repo name.

# build llama.cpp (Unsloth's fork with Kimi K3 vision support)
git clone https://github.com/unslothai/llama.cpp
cd llama.cpp
git fetch origin pull/48/head:kimi-k3-fullsize-vision
git checkout kimi-k3-fullsize-vision
cd ..
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split
cp llama.cpp/build/bin/llama-* llama.cpp

# download only the 1-bit quant plus the vision projector
hf download unsloth/Kimi-K3-GGUF --local-dir unsloth/Kimi-K3-GGUF \
  --include "*mmproj-BF16*" --include "*UD-IQ1_S*"

# run
./llama.cpp/llama-cli \
  --model unsloth/Kimi-K3-GGUF/UD-IQ1_S/Kimi-K3-UD-IQ1_S-00001-of-00014.gguf \
  --mmproj unsloth/Kimi-K3-GGUF/mmproj-BF16.gguf \
  --temp 1.0 --top-p 0.95

Notes:

• Use -DGGML_CUDA=OFF for a CPU-only build. Metal is on by default on Macs.
• Check the actual shard filename after download; Unsloth’s page lists 14 shards in one place and 15 in another.
• Recommended sampling is temperature 1.0 and top_p 0.95, or top_p 1.0 for agentic work.
• Start with a 32K or 64K context. The full 1M context needs a huge KV cache on top of the weights.

Kimi K3.1 Pro vs Other Giant Open Models

If you can only run one of these locally, Muse Spark Open is by far the easiest, at roughly a third of the memory. Kimi K3.1 Pro is the one to watch if you want the strongest open model and have the hardware, or you plan to use a hosted provider once the weights are out. See my Mistral Large 4 open weights post for that model’s specs.

Who Should Use It

• Developers on a budget: at $2 / $10 per million tokens, it is a cheap alternative to frontier closed models for long coding sessions.
• Game and 3D makers: Moonshot specifically trained for Unreal, Unity, Godot and Blender work.
• Companies that need self-hosting: once the weights are out, it is one of the few near-frontier models you can run on your own servers.
• Hobbyists with a single GPU: use the free Kimi.ai app, not a local install.

Frequently Asked Questions

Is Kimi K3.1 Pro open source?

It is open-weight, or will be once Moonshot publishes the files in November 2026. The license has not been announced, so check it on release.

When will Kimi K3.1 Pro weights be released?

Moonshot says November 2026, and November 4 has been reported. They were not public as of October 24, 2026.

How much VRAM do I need to run Kimi K3.1 Pro?

Based on Kimi K3’s GGUF files, at least about 610 GB of combined RAM and VRAM for the smallest 1-bit quant, about 880 GB for 2-bit, and about 1.6 TB near full quality.

Is Kimi K3.1 Pro better than Claude Opus 5.5?

On Moonshot’s numbers it wins some long-horizon coding and agent tests and loses others, such as Terminal-Bench 4.0 (by 0.7 points), BenchCAD and science tasks. Artificial Analysis rates Opus 5.5 slightly higher overall (58 vs 55).

Is Kimi K3.1 Pro free?

The Kimi.ai chat app has free access. The API is paid, starting at $2 per million input tokens and $10 per million output tokens, with a $1 minimum top-up.

Can I turn off thinking?

No. Thinking is always on. Set reasoning_effort to low for faster, cheaper answers.

Final Thoughts

Kimi K3.1 Pro is a big moment for open AI. A model that trades blows with Claude Opus 5.5 on coding, at a fraction of the price, with weights promised within two weeks, is exactly what the open model community hoped for this year. The reality check is size: at 2.8T parameters, “open weights” means “you can self-host it on a server”, not “you can run it on your gaming PC”. For most people, the API or the free app is the way to try it today.

I will update this guide with download links, quant sizes and setup steps as soon as the weights are published. In the meantime, see how other giant open models compare in my Muse Spark Open local guide and Mistral Large 4 breakdown.

Sources

• MarkTechPost: Moonshot AI releases Kimi K3.1 Pro
• The Decoder: Kimi K3.1 Pro is closing in on Claude Opus 5.5
• Wccftech: cyber data stripped from training
• Kimi.ai announcement on X
• Unsloth: Kimi K3 GGUF sizes and llama.cpp guide