NaiveAI cut median response time to 3.1 seconds in a Latent Space test

Waiting for AI to finish writing another version of an app takes its own toll on the working day. NaiveAI offers to shorten those pauses: its Naive-N0.5-Flash model generates text at a claimed peak of around 2100 tokens per second. In Latent Space's evaluation, median response time with reasoning enabled was 3.1 seconds, compared with 10.1 seconds for DeepSeek V4.1 Flash 1017 with reasoning set to high.

For developers, that looks appealing: get a version faster, run it, spot an error and submit the next revision. Yet in the same test, NaiveAI and DeepSeek had almost identical average response times. The gap between those two observations explains why choosing a model solely by generation speed can put budgets and deadlines at risk.

Where the speed comes from

Naive-N0.5-Flash is an open-weight model built on Xiaomi MiMo-V2.5. The team trained it further, adapted its attention mechanism and uses its own NaiveRT engine. The underlying architecture has 310 billion parameters, of which 15 billion are active when processing a token.

Speculative decoding is a key part of the speedup. Put simply, a small model proposes a continuation in advance, and the main model checks several proposed tokens at once. When those guesses fit, the system moves faster than it would if the large model generated each token sequentially.

There is a significant caveat: the claimed speeds were measured on a node with eight Blackwell GPUs. The setup is optimized to minimize latency for a single generation stream. These results do not tell us how many concurrent users such a system could serve or how much a self-hosted deployment would cost.

Moreover, 2100 tokens per second is a peak measured over one second. In the published measurements, throughput was around 984 tokens per second during reasoning and 1816 during answer generation. With reasoning enabled, the main model accepted 43.4% of the auxiliary model's proposals; with reasoning disabled, it accepted 70.6%.

How it handled code

Latent Space gave the models identical tasks: an interactive Solar System, a procedural 3D tree and a browser-based ray tracer based on the book Ray Tracing in One Weekend.

For the Solar System, the reviewer preferred NaiveAI's result with reasoning enabled: the model added sunspots and nighttime city lights on Earth. In the tree task, NaiveAI and DeepSeek both produced good results, although the wind animation retained some minor flaws. NaiveAI without reasoning, however, failed to produce a usable app for that task at all.

The ray tracer worked for all three models with reasoning enabled. DeepSeek won here: its implementation ran more efficiently. NaiveAI's app worked correctly, but its JavaScript was slower.

That is a useful distinction for a team building a prototype. Fast generation lets you open the result in a browser sooner, but says nothing on its own about the resulting app's performance. Three demonstration tasks are also no substitute for evaluating fixes in an existing repository, tests and code maintenance.

Where the gains disappear

Response speed was tested on 30 questions per configuration: ten each at easy, medium and difficult levels. Answer correctness was not assessed in this experiment. The result therefore describes latency, but does not establish superior problem-solving ability.

On difficult questions, NaiveAI with reasoning enabled generated an average of around 108,500 tokens, roughly 11 times as many as DeepSeek. Average response time was 89.8 seconds, compared with DeepSeek's 79.7 seconds. Across the full sample, the averages were virtually identical: 45.3 and 45 seconds, respectively.

NaiveAI's stated target price is $0.40 per million output tokens. But a cheap token does not necessarily mean a cheap task: lengthy reasoning increases the total cost. The speed figures were also collected using different methods, so they cannot be used to derive a precise speedup factor.

The practical next step is to run your own set of tasks and measure the time to a verified working result, total token usage and the number of retries. This review provides a reason to try NaiveAI for rapid iteration. A decision to switch will require testing it on your own code.