MiniMax integrates M3.1-Flash-Preview model for coding into MiniMax Code product

NaiveAI has introduced the open-weight Naive-N0.5-Flash model on the Hugging Face platform. This model features a Mixture-of-Experts (MoE) architecture designed for software development tasks and artificial intelligence research. Information about the model was published in mid or late September 2026.

The official product name is NaiveAI / Naive-N0.5-Flash. According to technical documentation, the total number of parameters is approximately 309 billion, with 15.5 billion weights being active. The model is distributed under the MIT license and includes inference code, as well as native support for a one-million-token context window.

The context length is achieved through a hybrid combination of the Sliding-Window Attention (SWA) mechanism and lightweight DeepSeek Sparse Attention (DSA), which utilizes grouped attention. The architecture is described as having a predominant SWA–DSA ratio of approximately 5:1, with no fully attentive layers in the stack. The model is based on the open-source MiMo-V2.5 model, followed by fine-tuning and post-training, rather than full pre-training from scratch.

In terms of serving, NaiveAI emphasizes NaiveRT—an inference stack developed using AI-oriented research. This stack integrates mega-kernel fusion, Programmatic Dependent Launch, and speculative decoding. According to the vendor, processing speed reaches approximately 50 tokens per second per user in Standard mode and up to 2000 tokens per second in Ultra-Fast mode.

Tags on Hugging Face highlight that the model is suitable for code text generation, long-context handling, and AI research tasks. This aligns with the stated concept that fine-tuning and post-training on a strong open base can push boundaries for coding agents. For developers comparing Chinese MoE releases this week, Naive-N0.5-Flash is a specific example of a 309B / 15.5B MoE with a million-token hybrid context under the MIT license, representing an architectural announcement rather than a cost or product launch news.

Xiaomi has made the weights, technical documentation, and reinforcement learning (RL) training resources for its MiMo-V2.6 series publicly available. According to English notes from Xiaomi dated September 22, the MiMo-V2.6-Pro and MiMo-V2.6-Flash repositories were published on the Hugging Face platform along with the RL code and over 7,000 test environments.

This release involves providing weights and code, which differs from previous coverage of the RL training process via live streaming. The branding remains unchanged: Xiaomi / MiMo.

The Pro and Flash models are positioned as native multimodal models with a claimed context window of one million tokens. Model cards and supplementary reports in English indicate that the Pro model features a sparse mixture-of-experts architecture with a total parameter count of approximately 1.02 trillion, actively utilizing about 42 billion parameters. The Flash model is rated at 309 billion total parameters with an activity level of around 15 billion.

Xiaomi reports that each model underwent 30 RL steps across approximately 750,000 trajectories in less than six days. The process utilized task mixing, including coding, general agent work, visual tasks, and cybersecurity. In each update, approximately 1,568 queries and 16 runs were used, with a data volume per step of 3.5–3.7 billion tokens.

According to the company, performance gains in training tasks were observed at approximately 25% for Flash and 12% for Pro. Furthermore, improvements were recorded in DeepSWE v1.1: from 48.8 to approximately 65.7 for Flash and from 58.4 to approximately 72.6 for Pro; however, these figures are vendor-provided data and require third-party verification.

The available assets go beyond mere checkpoints. Xiaomi has provided a comprehensive RL framework built on verl and related agent mechanisms, over 7,000 classified environments covering software development, vulnerability reproduction, intellectual labor, and web design. A Distill-Qwen-9B starting point is also available for community RL experiments, along with lightweight components for multichannel training. The API cost for hosted Pro and Flash versions is stated to be comparable to MiMo-V2.5, while the UltraSpeed tier promises up to 20 times higher throughput than standard Pro while maintaining quality.

Nevertheless, engineers still face high maintenance costs for large MoEs, and Xiaomi's claims regarding artificial intelligence analysis and agent benchmarks require external reproduction. The main news is the complete MiMo-V2.6 package with open weights, RL code, and environments that laboratories can study, rather than just a graphical representation in a live stream.