Recursive Superintelligence released its first model on Wednesday: Flint, an open-weight coding model designed to run on a laptop rather than a data center. Flint is a mixture-of-experts model with 39.8 billion total parameters but only about 2.5 billion active per token, fits on a 48 GB Apple Silicon Mac or a single 48 GB Nvidia GPU (4-bit weights take about 23 GB), and is free on Hugging Face under Apache 2.0. The model card lists a 262,144-token context window; Recursive's blog cites up to 512k.

Recursive says Flint generates more than 125 tokens per second on an M4 Pro MacBook Pro and more than 150 on Nvidia RTX Spark — up to twice comparable models. On its own charts it tops other MoE models under 50B on agentic coding, scoring 74.2% on SWE-bench Verified and 95.1% on IFEval, though it trails badly on Terminal-Bench 2.1 at 54.9% against 73.0% for a dense Qwen model. The company concedes Flint may need more guidance than large closed coding assistants.

The bigger claim is how Flint was made: a small team built it from scratch in under four months, leaning on Eureka Machine, Recursive's automated research system, for nearly every part of the stack. Recursive, led by former Salesforce chief scientist Richard Socher, emerged from stealth in May with $650 million in funding from backers including GV, Nvidia and AMD Ventures, and pitches Flint as a first public test of recursive self-improvement.