Kimi K3: The World's First 3T-Class Open Weights Model Arrives

Moonshot AI has released **Kimi K3**. It's a Mixture-of-Experts (MoE) model with 2.8T total parameters and 104B active parameters, natively multimodal across text, images, and video, with a context length of **1 million tokens**.

The model introduces new architectures — Kimi Delta Attention (KDA) and Attention Residuals (AttnRes) — which the company says improve scaling efficiency roughly 2.5x compared to Kimi K2. It also applies Quantization-Aware Training (MXFP4 weights / MXFP8 activations) starting from the SFT stage, targeting operation across a wide range of hardware.

On benchmarks, it posts top-tier scores in coding (DeepSWE 67.5, FrontierSWE 81.2), agentic tasks (BrowseComp 91.2, MCPMark-Verified 94.5), and vision tasks (MMMU-Pro 81.6). The results put it on par with Claude Fable 5 and GPT-5.6 Sol, marking a new high-water mark for open-weight models.

Notably, a member of Reddit's LocalLLaMA community reported **actually running inference on Kimi K3 with 80 RTX 5090s combined over 25GbE Ethernet** — a fresh reminder of the "you can run it yourself" value unique to open weights.

The weights are released under the [Kimi K3 License](https://huggingface.co/unsloth/Kimi-K3), and the API is available at platform.kimi.ai (model name: `kimi-k3`).

Anthropic CEO Voices Concerns About Open Weight Models

Dario Amodei, Anthropic's co-founder and CEO, has once again laid out [the company's position on open weight models](https://www.anthropic.com/news/position-open-weights-models). According to TechCrunch, Amodei says he is "not opposed to open weight models in themselves," but he expresses **security concerns**, particularly about China's advancing AI capabilities.

Specifically, he points to the risk of open weight models being used for "achieving permanent military advantage or severe repression of their own populations." The remarks drew a flurry of responses on Reddit's r/LocalLLaMA — "isn't this effectively calling for a ban on open weight models?" and "isn't the real motive fear of competition?" — sparking a lively debate.

How should we balance the freedom of open weight models against security? With the arrival of large open models like Kimi K3, this debate is becoming more concrete by the day.

Why You Shouldn't Ask LLMs for "Confidence Scores"

An article on [justinflick.com](https://justinflick.com/2026/07/27/llm-confidence-scores.html) that gained traction on Hacker News casts doubt on the reliability of the "confidence scores" LLMs produce.

Because LLMs generate text based on probability distributions, accurately estimating their own uncertainty is inherently difficult. The article's argument: if you've built confidence scores into your systems and use them for decision-making, it's time to re-examine whether those scores are "truly a reliable metric."

It's an important caution for developers using confidence scores in RAG systems and agent designs.

Real-World Benchmarks of LLM Inference Speed on Apple Silicon

Useful data has been published for developers running LLMs on Macs. [macyou.co/benchmarks](https://macyou.co/benchmarks) systematically measures inference speed for multiple models on Apple Silicon, with raw data released under a CC BY 4.0 license.

It looks like a handy resource for model selection and hardware planning when running local LLMs on M-series chips.

From the Japanese-Speaking Community

Several interesting AI-related articles have also been drawing attention on Qiita.

**Handing an Entire Refactoring Job Over to Claude Code** ([article](https://qiita.com/hikariclaude01/items/fbbf541c5a9c3c6d1567)) shares the story of delegating the refactoring of 20,000 lines of legacy code to Claude Code — and living through three days of rollback hell as a result. It concludes that carefully designing the boundary between "what to delegate to AI and what humans should keep hold of" is critical, making it insightful reading for anyone using AI coding tools in production.

There's also **Ollama vs vLLM: A Speed Comparison** ([article](https://qiita.com/hu-work/items/b6beeb2e4c4ee750e8d4)), which benchmarks inference speed for both using Gemma 4 — a useful reference when choosing an inference stack on GPU clouds. And **A Beginner's Guide to MCP, Agents, and Tools Terminology** ([article](https://qiita.com/shimada_slj/items/f02261993d625ac97ba0)) looks like a good starting point for anyone getting into AI-driven development.

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