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EVENT / TOPICHistorical

Recursive launches Flint language model driven by Eureka Machine AI

1 reports1 sources10/22/2026 updated

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AI overview

Recursive has released Flint, a language model developed by its Eureka Machine AI system. Flint is a Mixture-of-Experts model with 40 billion total parameters, activating 2.5 billion parameters per token, and is designed for agent coding on local hardware. This marks the first output of Recursive's AI-driven research process.

Written with AI from the reporting · updated 10/22/2026

Latest developmentOct 22, 22:40
Recursive releases Flint, an AI-driven MoE model for agentic coding on local hardware
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  1. 1Unclassified fact stationOct 22, 00:00recorded Oct 22, 23:07single sourcerevised

    Recursive发布Flint语言模型

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    Evidence & revisions · 1 source records
    Subject：
    recursive
    Action：
    发布语言模型
    Object：
    Flint
    Occurred (fact side)：
    Oct 22, 00:00
    The Decoder ↗Report pageRevision 2 · currently revised
    Published (valid side)：
    Oct 22, 22:40
    First seen (known side)：
    Oct 22, 23:07
    Fact stationSingle source · one-sided claimRevised

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Oct 22
  1. The DecoderRelated fact: single sourceSource report revised
    Recursive releases Flint, an AI-driven MoE model for agentic coding on local hardware

    Report first recorded Oct 22, 23:07

    AI startup Recursive has released Flint, its first language model, which was developed using an AI system called "Eureka Machine" that autonomously proposed, ran, and evaluated experiments. Flint is a Mixture-of-Experts (MoE) model with 40 billion total parameters but only 2.5 billion active parameters per token, designed for agentic coding on local hardware. Recursive claims Flint leads coding benchmarks like SWE-Bench Pro v2 and Terminal-Bench 2.1 among MoE models with fewer than 50 billion parameters, matching the benchmark scores of much slower dense models in the 27 to 31 billion parameter range on an M4 Pro with 48 GB of memory. On a MacBook Pro with M4 Pro, it generates over 125 tokens per second at short contexts, and over 150 on an Nvidia RTX Spark with 64 GB of memory.

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