A collection of 15 posts
Kimi K3 is Moonshot AI’s 2.8-trillion-parameter open-weight model, released July 2026. Architecture, specs, pricing, and how to access it.
How Kimi K3 compares to Claude Fable 5, GPT-5.6 Sol, and Opus 4.8 across the Intelligence Index, coding arenas, agentic tasks, and price.
Two open-weights heavyweights from China go head-to-head for the agentic-coding throne. K2.7 leads on MCP tool-use depth; V4 leads on raw per-token economics and proven independent benchmarks. We break down cost, agentic strength, self-host paths, and pick a winner per workload.
Moonshot's open-weights Kimi K2.7 Code goes head-to-head with Anthropic's Claude Opus 4.8. Architecture, benchmarks (and where they don't exist yet), per-task cost, agentic strength, self-host paths, and a clean per-workload verdict.
Moonshot's Kimi K2.7 Code and Z.ai's freshly-released GLM 5.2 are both Chinese open-weights coding flagships, both shipped in June 2026, and they trade on opposite axes. K2.7 leads on MCP tool use and pricing; GLM 5.2 leads on 1M context. We pick per workload.
How Moonshot's open-weight Kimi K2.7 Code stacks up against Claude Opus 4.8, GPT-5.5, and DeepSeek V4 for agentic coding — on price, context, and the benchmarks that exist. K2.7's scores are Moonshot-reported only, so the verdict is subject to change once independent results land.
Moonshot AI's Kimi K2.7 Code — a 1T-parameter open-weight coding model with a 256K context, ~30% fewer thinking tokens than K2.6, and strong MCP tool-use. Benchmarks, pricing, API, and local-deployment guide.
Kimi K2.6 ties GPT-5.5 on SWE-bench Pro at 58.6% — and runs roughly 3x cheaper, with open weights. Where each model wins, with the cost math.
Kimi K2.6 ties Opus 4.7 on multilingual SWE-bench but trails by 7 points on Verified — at 1/5th the cost. The honest, benchmark-by-benchmark breakdown.
Kimi K2.6 and DeepSeek V4 Pro are the two best open-weights coding models in 2026. K2.6 wins long-horizon agents and swarms; DeepSeek V4 wins on raw price.
DeepSeek V4 is out — Pro and Flash tiers, MIT license, 1M context, and pricing that undercuts the frontier by up to 11×. Here's how it stacks up against Qwen3.5, Kimi K2.5, MiniMax M2.7, GPT-5.4, and Claude Opus 4.6.
Kimi-Audio is an open-source audio foundation model capable of speech recognition, audio generation, and conversational AI tasks. While primarily designed for Linux environments, this guide provides detailed instructions for Windows users to leverage its capabilities through multiple methods. I. System Requirements 1. Hardware Specifications * GPU: NVIDIA GPU with ≥24GB VRAM
Quick answer. Kimi-Audio 7B runs on Apple Silicon Macs via MLX-LM for ASR, but speech generation still depends on CUDA-only kernels — pair it with kokoro-tts or parler-tts for Mac TTS. Needs ~20 GB unified RAM, Python 3.11, and HF transformers from main. As of May 2026, no first-party MLX/
In the rapidly evolving field of artificial intelligence, particularly in vision-language models, two notable models have gained attention for their innovative approaches and capabilities: DeepSeek VL2 and Kimi Moonlight 3B. This article aims to provide a detailed comparison of these models, focusing on their architecture, capabilities, performance, and applications. Introduction
Moonshot AI's Moonlight-16B-A3B is a Mixture-of-Experts model with 16B total parameters and ~3B active per token, trained with the Muon optimizer. Released under the MIT license on Hugging Face as moonshotai/Moonlight-16B-A3B-Instruct, it's positioned as Moonshot's compact open-weights model — distinct from the company'