Recursive 发布首个由 Eureka Machine 参与研发的模型 Flint

AI startup Recursive built its first language model with an AI system that proposes, runs, and evaluates its own experiments.

AI startup Recursive has released Flint, its first language model. An AI system called "Eureka Machine" was involved in nearly every step of development, proposing ideas on its own, building them out, testing them, and planning follow-up experiments based on the results.

Flint is a mixture-of-experts model (MoE) designed for agentic coding on local hardware, and it was built in four months. Out of 40 billion total parameters, only 2.5 billion are active per token. Recursive claims Flint leads coding benchmarks like SWE-Bench Pro v2, Terminal-Bench 2.1, and SWE-Bench Verified among MoE models (such as Qwen3.6-35B-A3B and Laguna S 2.1) with fewer than 50 billion parameters. The results haven't been independently verified yet.

On an M4 Pro with 48 GB of memory, Flint matches the benchmark scores of much slower dense models in the 27 to 31 billion parameter range. | Image: Recursive

The model is meant to compete with dense models in the 25 to 40 billion parameter range while running more than five times faster. On a MacBook Pro with M4 Pro, Flint generates more than 125 tokens per second at short contexts, according to Recursive. On an Nvidia RTX Spark with 64 GB of memory, it tops 150. Compared to similar models, inference is up to twice as fast.

On M4 Pro and RTX Spark, Flint generates roughly twice as many tokens per second as comparable MoE models at short context lengths. | Image: Recursive

Among sparse MoE models of similar size, Flint leads across all six coding and instruction-following benchmarks, though dense models like Qwen 3.8 score higher in some tests. | Image: Recursive

Eureka Machine chose the architecture, quantization, and compute kernels on its own

Recursive says the Eureka Machine automates the entire research cycle, running multiple approaches in parallel. For Flint, it helped with training data, optimizers, architecture design, and inference tuning. Three examples show how the system shaped the model's performance.

Starting from an architecture with 35 billion total and 3 billion active parameters, the Eureka Machine tested different expert configurations. The final design of 40 billion total and 2.5 billion active parameters turned out to be the best tradeoff between speed and coding performance among all the variants it tested.

For quantization, the system went against common practice and reduced the output layer to four bits as well. An added distillation step was meant to recover the lost accuracy, and according to Recursive, it needed roughly half as many training steps as the previous method. The finished NVFP4 model sits only 0.7 points behind the 16-bit version across benchmarks.

The Eureka Machine also built specialized compute kernels for Apple Silicon and Nvidia RTX Spark. On Apple's M4 and M5 chips, a Metal megakernel combines the entire forward pass during token generation into a single operation. Recursive says these optimizations across multiple development stages are what enable the high inference speed.

Flint runs locally and ships with open weights

Flint supports context windows of up to 512,000 tokens, though Recursive recommends capping it at 256,000. The model weights are available on Hugging Face under the Apache 2.0 license. Supported runtimes include llama.cpp, Ollama, LM Studio, vLLM, and SGLang. Recursive also released rec, its own inference engine for Apple Silicon.

All of that means the model can run locally without sending code to external servers. Recursive does acknowledge that Flint needs more guidance on complex tasks than large proprietary coding models. The company recommends breaking bigger tasks into smaller steps.

Recursive is betting it can build AI that keeps building better AI

The startup, founded by Richard Socher and others including former DeepMind researcher Tim Rocktäschel, came out of stealth in May 2026 and raised $650 million at a $4.65 billion valuation. At the time, Recursive had no concrete technical results to show. Flint is the company's first publicly available model from its automated research process.

The long-term goal is to build a superintelligence that keeps improving itself. Each new model is supposed to make the Eureka Machine a better researcher, so the next generation of models can be built faster and more efficiently. Eventually, Recursive wants to apply the same approach to automate research in other scientific fields.

Whether this will actually produce accelerating self-improvement is far from settled. Former Google DeepMind research lead Oriol Vinyals considers AI-assisted self-improvement realistic but thinks an intelligence explosion is unlikely. Finding good research ideas and reliably judging results remain especially hard problems for AI systems.

Other AI labs are already using AI to build new models too. Anthropic says Claude now handles a large share of its own development work, while OpenAI deploys AI agents across its entire research pipeline. How far that automation actually goes is still debated. A study of more than 700 task logs found that AI agents handle large portions of the work, but humans still make most of the critical research calls.

Those are the steps Recursive wants to automate with the Eureka Machine. Flint is a first concrete result, but it doesn't yet prove full recursive self-improvement. For that, the company would need to show that a model built with the Eureka Machine actually makes the Eureka Machine itself a better researcher.

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