Naive-N0.5-Flash open-weights coding model prioritizes speed
NaiveAI released Naive-N0.5-Flash, an open-weights model for AI coding that continues training from Xiaomi’s MiMo-V2.5 and runs with the NaiveRT inference engine. Latent Space found that it answered faster overall than DeepSeek V4.1 Flash 1017 and GLM 5.3 Flash in limited tests, but it took longer on harder questions and had a slightly higher cost per question. NaiveAI reports about 984 tokens per second during thinking and 1,816 tokens per second while producing the answer, with a roughly 2,100-token-per-second peak measured on a single eight-GPU Blackwell node.
The release combines an open-weights coding model with a specialized inference system optimized for rapid single-stream decoding, giving developers another speed-focused option for coding and research workflows.
Editor’s note: With this post, we are launching a new series focused on trying new models and products with unbiased reviews. Reach out with suggestions to [email protected].
Naive-N0.5-Flash is a new open-weights model built for AI coding tasks and tuned to generate as many tokens per second as possible. It was developed by a Chinese company called NaiveAI, with the aim of speeding up engineering and research workflows so that teams can experiment, iterate and solve complex problems faster.
The model is an evolution of Xiaomi’s MiMo-V2.5, which NaiveAI has trained further for long-context reasoning and paired with its own NaiveRT inference engine. Naive-N0.5-Flash arrives with a bold pitch: a model built largely by AI and trained to do AI research itself, with recursive self-improvement as the long-term goal.
To see what changed, it helps to know where it started. MiMo-V2.5 is a 310B-parameter sparse Mixture-of-Experts model with 15B active parameters, trained on 48T tokens. Its language backbone inherits the hybrid design of MiMo-V2-Flash, which mixes sliding-window attention (SWA) with global self-attention.
#NaiveAI#Naive-N0.5-Flash#NaiveRT#Xiaomi MiMo-V2.5#DeepSeek V4.1 Flash 1017#GLM 5.3 Flash#SGLang