NaiveAI releases Naive-N0.5-Flash open-weights coding model
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NaiveAI released Naive-N0.5-Flash, an open-weights coding model derived from Xiaomi's MiMo-V2.5 and paired with its NaiveRT inference engine, tuned to generate tokens as fast as possible. NaiveAI targets $0.10 per million input tokens, $0.40 per million output tokens and $0.01 per million cache-read tokens. Latent Space tested it against DeepSeek V4.1 Flash 1017 and GLM 5.3 Flash. In the author's runs it peaked near 2,000 tokens per second and sustained about 1,000 on thinking-heavy tasks, with a median response time of 3.1 seconds versus 5.3 and 10.1 seconds. On hard HLE questions it averaged about 108,500 completion tokens with thinking on, roughly nine times GLM 5.3 Flash and 11 times DeepSeek V4.1 Flash 1017. The author notes the sample was 10 questions per difficulty level with no accuracy grading. The reported decoding speeds were measured on a single eight-GPU Blackwell node running speculative decoding in NaiveRT, which the source describes as an upper bound rather than a typical rate.
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- Latent SpaceNaiveAI's Naive-N0.5-Flash claims 2,100 tokens per second: how well does it code?
Latent Space tested NaiveAI's open-weights Naive-N0.5-Flash, a coding model derived from Xiaomi's MiMo-V2.5 and paired with the NaiveRT inference engine, against DeepSeek V4.1 Flash 1017 and GLM 5.3 Flash. In the author's runs it peaked near 2,000 tokens per second and sustained about 1,000 during thinking-heavy tasks, with a median response time of 3.1 seconds versus 5.3 and 10.1 seconds for the two comparison models. On hard HLE questions it averaged about 108,500 completion tokens with thinking on, roughly nine times GLM 5.3 Flash and 11 times DeepSeek V4.1 Flash 1017, and the author notes the sample was 10 questions per difficulty level with no accuracy grading.
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