China’s AI-powered coding tools entered a new era in June 2026 when three major releases reshaped the competitive landscape. Zhipu’s ZCode 3.0, Xiaomi’s MiMo Code, and Huawei’s DevEco Code—nicknamed the “Three Kingdoms War” by developer circles—each brought distinct technical philosophies to market. While these tools dominate headlines, another trend is quietly gaining traction: local-first AI coding solutions designed for security-conscious teams.
How the Three Titans Stack Up Against Each Other
The trio of releases didn’t just coincide by accident—they reflect diverging visions for the future of AI-assisted development. ZCode 3.0 positions itself as a multi-agent collaborative IDE, leveraging Zhipu’s proprietary GLM model series to power features like grouped task workspaces and intelligent project knowledge bases. This model-driven approach ties its performance ceiling directly to the underlying LLM’s capabilities.
MiMo Code, open-sourced by Xiaomi on June 11, takes a diametrically opposite route. Built on OpenCode with an MIT license, it avoids locking developers into a single model ecosystem. Instead, it supports persistent memory systems, unlimited context windows, and seamless switching between models like DeepSeek, Kimi, GLM, and its own MiMo v2.5. The open approach lowers adoption barriers but shifts differentiation to user experience rather than underlying architecture.
Huawei’s DevEco Code, unveiled at HDC 2026, carves out a niche entirely focused on the HarmonyOS ecosystem. Powered by Huawei’s Bifang large model, it spans the entire development lifecycle—from requirements design to testing and maintenance. With an AI code generation rate of 80%, it exemplifies vertical specialization, betting that HarmonyOS’s scale justifies a dedicated tool.
Beyond the Cloud: The Rise of Local-First AI Coding
As cloud-based AI coding tools converge on similar feature sets, data security and latency emerge as critical differentiators. Mininglamp’s Mano-P, an open-source GUI-VLA agent model, exemplifies this shift by enabling fully local execution on Apple M4 hardware with 32GB RAM. By keeping screenshots and task descriptions on-device, it addresses strict compliance requirements in industries like finance and healthcare.
In OSWorld model evaluations, Mano-CUA 1.1 delivered a 58.2% task success rate—outperforming the second-place opencua-72b (45.0%) by 13.2 percentage points. On WebRetriever Protocol I benchmarks, it scored 41.7 NavEval, narrowly edging out Gemini 2.5 Pro (40.9) and decisively beating Claude 4.5 (31.3). These results challenge the assumption that cloud-scale models always outperform local alternatives.
Performance metrics further underscore the viability of local-first solutions. The 4B quantized version of Mano-P achieves approximately 80 tokens per second during decoding on M5 Pro hardware. When paired with Mininglamp’s Cider SDK and W8A8 activation quantization, prefill speeds improve by roughly 12.7% compared to W8A16 baselines. In real-world testing on 100 macOS GUI tasks, the local Mano-CUA-Thinking-4B model hit a 56.0% pass rate—significantly ahead of the cloud-based Qwen3-VL-Plus at 39.0%.
Open Source Roadmap and Installation Paths
Mano-P follows a phased open-source strategy under the Apache 2.0 license. Phase one focuses on releasing Mano-CUA Skills, installable via brew tap Mininglamp-AI/tap && brew install mano-cua. The second phase introduces local models and SDKs, with releases available on Hugging Face and ModelScope. Phase three, still in planning, will include training methodologies and quantization pruning techniques.
Choosing the Right Tool for Your Needs
Developers evaluating these tools should align their choice with project requirements. Teams seeking deep model integration will find ZCode 3.0’s GLM-powered features compelling. Those prioritizing flexibility across multiple models may lean toward MiMo Code’s open ecosystem. HarmonyOS developers, in particular, will benefit most from DevEco Code’s ecosystem-specific optimizations.
For teams with stringent data security mandates or ultra-low latency demands, Mano-P’s local-first architecture offers a compelling alternative. Rather than chasing cloud-scale performance at all costs, it proves that targeted local models can deliver superior results in controlled environments. As AI coding tools evolve, the real winners may be those who balance cloud convenience with on-device control—proving that sometimes, the future isn’t just about scale, but about sovereignty.
AI summary
ZCode 3.0, MiMo Code ve DevEco Code’un teknik detayları, performans karşılaştırmaları ve hangi geliştirici grubuna hitap ettikleri hakkında derinlemesine analizler.
