NaiveAI’s new open-weights coding model, Naive-N0.5-Flash, generated tokens at striking speeds in Latent Space’s tests, but its long reasoning traces could make some tasks slower and more expensive. For developers, the pitch is faster iteration, not a free lunch measured in tokens.
Watch Desk analysis
What happened
Latent Space tested Naive-N0.5-Flash against DeepSeek V4.1 Flash 1017 and GLM 5.3 Flash on 30 questions and three app-building prompts. The report says NaiveAI’s model reached close to 1,000 tokens per second in thinking configurations. Its median response time across the questions was 3.1 seconds, compared with 5.3 seconds for GLM and 10.1 seconds for DeepSeek in their high-thinking settings.
The comparison has important wrinkles. NaiveAI’s throughput figures came from server-side measurements, while the other models’ came through OpenRouter, so Latent Space does not claim a like-for-like speed multiple. On hard questions, the new model’s mean response time was slower than DeepSeek’s. Its thinking-enabled runs also used far more tokens on some questions: the average completion on the hard set was about 108,500 tokens, while the median across all questions was about 3,200. Latent Space says it did not grade answer accuracy, and the sample was small.
Why it matters
Fast generation can help developers test ideas quickly, but tokens are not the same as finished work. A model that produces a long stream of reasoning may run up costs or take longer end to end, even when its output rate looks splendid on a chart.
Latent Space also found that Naive-N0.5-Flash with thinking enabled performed well on its three app-building prompts: it made the strongest solar-system app, matched DeepSeek on the tree, and came second on the ray tracer. Those are the outlet’s evaluations, not a broad measure of coding ability. The report gives target prices of $0.10 per million input tokens and $0.40 per million output tokens, but actual spending will depend in part on how much the model generates.
Our read
This is a model worth trying if rapid iteration is the priority, but the headline speed needs a running-cost and quality check beside it. The important comparison is not simply tokens per second; it is how quickly and economically the model completes useful work. Latent Space’s tests offer an informative first look, not a verdict on accuracy or performance across everyday coding jobs.
What to watch
- Whether independent, like-for-like tests confirm the reported speed advantage.
- How often real coding tasks trigger unusually long reasoning outputs.
- Whether the target pricing and performance translate into lower costs per successful task.
Discussion spark: When choosing a coding model, would you prioritise faster iteration or predictable cost and end-to-end completion time?
Sources and evidence
- NaiveAI Promises 2,100 Tokens per Second. How Well Does It Code? (10 November 2026, 18:56 UTC)
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