Anthropic switched its flagship Fable 5 model from a limited‑time, high‑price subscription to permanent access, but with reduced quotas and unchanged token fees, as intense price competition from OpenAI and Kimi forces the high‑price anchor to collapse.
This article walks through installing Codex, adding the open‑source CC Switch tool, and configuring Chinese large language models such as Kimi so they can serve as the backend for Codex’s AI agent, with step‑by‑step screenshots and performance examples.
The newly released Kimi K2.7 Code, a 1‑trillion‑parameter mixture‑of‑experts model that activates only 32 B parameters per inference, offers a 256 K context window, supports multimodal input, improves benchmark scores by up to 31.5 % over K2.6, reduces inference token usage by about 30 %, and achieves an 81.1 MCP tool‑call score surpassing Claude Opus 4.8, while providing a CLI installation command and usage guidelines.
Cursor unveiled Composer 2.5, reporting benchmark scores comparable to Opus 4.7 and GPT‑5.5, a ten‑fold cost reduction, explicit use of Moonshot’s Kimi K2.5 as a base, new RL training techniques, and a partnership with SpaceXAI that multiplies compute power, all highlighted by Elon Musk’s retweet.
Since early 2026, China's large‑model sector has entered a rapid financing phase, with DeepSeek courting a state‑backed lead investor at a $45 billion valuation, Kimi completing a $20 billion round that pushes its valuation past $200 billion, and Jumps Star securing nearly $25 billion, reshaping the competitive landscape and highlighting the shift from pure technology breakthroughs to commercial and capital‑driven dynamics.
This week’s tech roundup covers the launch of Kimi K2.6 with a 300‑agent swarm capability and major performance gains, DeepSeek V4’s new sparse‑attention architecture and pricing, Meshy’s AI‑3D partnership, a $4.55 B AI‑brain funding round, Honor’s record‑breaking robot, M‑Flow’s cone‑graph memory engine, and Vision Banana’s unified visual model, all backed by benchmark data and industry commentary.
The article analyses DeepSeek V4’s technical report, revealing repeated “encounters” between DeepSeek and Kimi—shared MLA attention, Muon optimizer, and divergent long‑context strategies—while highlighting their open‑source releases, hardware adaptations, and ecosystem impact that dramatically lower deployment costs for Chinese AI.
In the last 24 hours the AI landscape shifted not by a handful of new model releases but by two converging fronts—model‑level advances in agentic coding and product‑level moves that turn models into usable work systems—signaling deeper changes in competition and industry impact.
The MiniMax M2.7 model switched its open‑source license to forbid commercial use, igniting a heated debate about what constitutes commercial activity, prompting a community clarification that self‑hosted coding remains free, and leading to a revised license that explicitly permits personal, academic, and non‑profit uses while highlighting broader market pressures from cloud providers that are reshaping the open‑source LLM ecosystem.
The article analyzes three recent technical reports—Moonshot AI's Kimi K2.5, Cursor's Composer 2, and Chroma's Context‑1—detailing how each system trains agent models with reinforcement learning, parallel orchestration, self‑summarization, and self‑editing, and highlights shared methodological themes and performance gains.
Kimi, a three‑year‑old AI‑native unicorn valued over $120 billion, launches a “Time‑Machine” option program that grants interns equity while showcasing its rapid valuation growth, record‑breaking context lengths, novel Kimi Linear architecture, token‑efficiency gains, and open‑source models that rival leading LLMs.
A developer uncovered that Cursor’s Composer 2 actually runs on the Kimi K2.5 model with reinforcement learning, prompting a rapid licensing dispute that ended with official confirmation and highlights the opaque yet collaborative nature of today’s open AI model ecosystem.
This step‑by‑step tutorial shows how to integrate domestic LLM providers (GLM‑5, GLM‑4.7, GLM‑Flash, Kimi K2.5, DeepSeek, Qwen3‑Coder‑Next, BGE‑M3) into OpenClaw, define model routing, create dedicated controller, writer and coder agents, and run a complete multi‑agent workflow.
This guide walks through preparing the Kimi 2.5 model, uploading it to OSS, configuring persistent storage, and using SGLang, RoleBasedGroup, and Openclaw to deploy a production‑grade inference service on Alibaba Cloud Kubernetes with step‑by‑step commands and YAML examples.
This article presents a hands‑on evaluation of Kimi K2.5 and its open‑source Kimi Code agent across a series of hard‑core prompts, covering Python API generation, cost‑optimized routing, multimodal ECharts visualisation, massive‑scale SQL optimisation, web‑search‑driven research, MoE explanation and video‑to‑code workflows.
Kimi's open‑source K2‑Thinking model, a 1‑trillion‑parameter agent with native INT4 quantization and 256k context, achieves SOTA performance on benchmarks like Humanity’s Last Exam, BrowseComp and SEAL‑0, outperforms GPT‑5 and Grok‑4, and demonstrates complex tool‑driven reasoning through real‑world examples.
The author builds an AI‑driven end‑to‑end assistant that transforms a research paper into a structured reading note, generates reproducible code, runs experiments, summarizes results, and creates a report, demonstrating how large language models like Kimi K2 can streamline the entire paper‑to‑implementation workflow.
This comprehensive analysis by the Peking University AI Alignment team dissects the technical innovations behind DeepSeek‑R1, DeepSeek‑R1 Zero, and Kimi‑K1.5, covering reinforcement‑learning‑based post‑training, rule‑based rewards, GRPO optimization, scaling laws, multimodal extensions, safety challenges, and future research directions.
This article demonstrates how to combine DeepSeek's AI‑driven content generation with Kimi's visual design engine to create professional, data‑rich PPTs up to 80% faster, offering reusable prompts, a four‑step workflow, a real‑world case study, and advanced collaboration tips.
The article analyzes how Kimi’s superior long‑context capabilities have propelled it to the top of user traffic in China, reshaping the competitive dynamics among domestic and international AI large models and driving a rapid surge in compute demand across both C‑end and B‑end applications.
The author tests Moonshot’s Kimi API, revealing its 200 k‑character context window, superior token‑to‑character ratio compared with GPT‑3.5 and Gemini, and performance that, while slower than GPT‑3.5 Turbo, rivals GPT‑4 Turbo, all while offering OpenAI‑compatible endpoints and free credit for developers.
