Latent Space, which writes for AI engineers, launched a series reviewing new models on 10 November. First, Gabriele Beltramo tested Naive-N0.5-Flash from the Chinese company NaiveAI.

Naive-N0.5-Flash is an open-weight model built for coding and tuned to generate as many tokens per second as possible. NaiveAI built it by further training Xiaomi’s MiMo-V2.5, a 310-billion-parameter mixture-of-experts (MoE) model that uses 15 billion parameters to compute each token.

The speed figure is the developer’s peak

The figure of 2,100 tokens per second is the developers’ own measurement, which Latent Space reported. They measured it on a single server with eight Blackwell GPUs, running NaiveAI’s own NaiveRT inference engine. The settings were tuned to keep latency low for a single request, not to handle many requests at once.

The speed comes from speculative decoding: a small draft model proposes the next tokens and the main model checks them in one pass. The more proposals the model accepts, the faster it generates text. With reasoning on, the model accepted 43.4 per cent of proposals; without reasoning, 70.6 per cent. Speed was about 984 tokens per second in the reasoning phase and 1,816 in the answer phase. Latent Space said 2,100 is a one-second peak that should be treated as an upper bound, not a typical speed.

Easy questions were fast, hard ones slow

Latent Space put 30 questions to Naive-N0.5-Flash and to two comparison models, DeepSeek V4.1 Flash 1017 and Z.ai’s GLM 5.3 Flash. Ten questions were easy (MATH-500), ten medium (GPQA-Diamond) and ten hard (HLE-Diamond). The table shows the time from request to finished answer.

Latent Space’s measurement, 30 questions per model; reasoning setting in brackets.

The table says nothing about answer quality: Latent Space did not check whether the answers were correct, and the sample was small. Latent Space gives no exact speed difference, because the tokens-per-second figures came from different sources: the Naive model’s from the server, the others’ from OpenRouter.

A few very long reasoning runs pushed up the mean, especially for the Naive model. On the hard questions, Naive-N0.5-Flash generated about 108,500 tokens on average, roughly 11 times as many as DeepSeek and nine times as many as GLM. It took 89.8 seconds on average to answer them; DeepSeek took 79.7 seconds.

Three browser apps

The models built three browser apps from the same prompts: a 3D solar system, a 3D tree swaying in the wind, and a browser version of the ray tracer from the book Ray Tracing in One Weekend. In Latent Space’s assessment, Naive-N0.5-Flash with reasoning on built the best solar system, and its tree was as good as DeepSeek’s. It came a close second on the ray tracer because its JavaScript was less efficient. Without reasoning, it could not make the tree app usable. GLM was the weakest.

NaiveAI’s target prices per million tokens are $0.10 for input, $0.40 for output and $0.01 for cache-read tokens. Latent Space said a question cost slightly more than with the comparison models, and that the final bill depends on how many tokens are used.