Recursive Flint: a free 40B coding model for laptops, largely built by AI

On 21 October 2026, the startup Recursive released its first model. It is called Flint, it is a coding model, and it is free to download. That alone would be a normal day in AI. What makes Flint worth a closer look is how it was made. Recursive says its automated research system, the Eureka Machine, helped with nearly every step of building the model, from the training recipe to the low level code that runs it on a laptop. A small team built Flint from scratch in under four months.

Recursive is open about its goal. The company wants to build AI that improves itself, an idea known as recursive self improvement. Flint is the first public product of that bet, and it gives the rest of us something concrete to test instead of a slide deck. This article explains what Flint is, how it performs, what the Eureka Machine actually did, where the claims need care, and what it means for developers who want a strong coding model on their own machine.

The short version

• Flint is a mixture of experts model with 39.8 billion total parameters and about 2.5 billion active per token. Recursive rounds this to "40B-A2.5B".
• It is built for agentic coding: reading codebases, fixing bugs, adding features and writing tests, with multi turn tool use and reasoning.
• The weights are on Hugging Face under the Apache 2.0 license, which allows free commercial use and modification.
• Recursive reports 54.9 percent on Terminal-Bench 2.1, 89.7 percent on SWE-bench Pro v2 and 74.2 percent on SWE-bench Verified. All scores are vendor reported.
• With Recursive's own inference engine, Flint reaches about 131 tokens per second on an M4 Pro MacBook Pro at short context and more than 150 tokens per second on a 64 GB Nvidia RTX Spark.
• Recursive emerged in May with 650 million dollars at a 4.65 billion dollar valuation. Its CEO is Richard Socher, former chief scientist at Salesforce.

Who is Recursive?

Recursive was founded in 2025 and came out of stealth in May 2026 with one of the largest early rounds of the year: 650 million dollars at a 4.65 billion dollar valuation. Backers include GV, Greycroft, Nvidia and AMD Ventures. Richard Socher, previously chief scientist at Salesforce, leads the company as CEO. His cofounders include Jeff Clune, a professor at the University of British Columbia who studies AI systems that can keep learning indefinitely.

The company's plan is to use its own AI system to advance both AI and other areas of science. In June it signed a 410 million dollar compute deal with Amazon Web Services, which Recursive told TechCrunch was "less about headcount and more about agent count." In other words, the money is meant to run research agents, not to hire a large staff.

Flint is the first time the company has shipped something anyone can download and judge.

What Flint is

Flint is a decoder only transformer with a mixture of experts design. In a normal dense model, every parameter is used for every token. In a mixture of experts model, the network contains many smaller expert blocks, and a router picks only a few of them for each token. Flint has 256 routed experts in each expert layer and selects six of them per token, plus one shared expert that is always on.

The model has 49 layers. The first two are dense, the remaining 47 use experts. Attention is split in a similar spirit: 35 layers only look at a sliding window of the current token and the 512 tokens before it, and 14 layers attend to the full context. This keeps the memory cache for long sessions small, which matters a lot on a laptop. The model card lists a documented context of 262,144 tokens. Recursive's blog says Flint supports up to 512,000 tokens, but recommends staying within 256,000 for the best results.

The full precision BF16 checkpoint is about 80 GB, which is too big for most laptops. For local use, Recursive published quantized builds the same day. According to file listings checked by OrcaRouter, the NVFP4 GGUF build is about 22.9 GB, the int4 GGUF about 26 GB and the int4 safetensors build about 22.6 GB. That puts Flint within reach of a Mac with 32 or 48 GB of unified memory, or a desktop GPU with enough memory.

Flint works with common local tools: llama.cpp, Ollama, Unsloth, LM Studio, vLLM and SGLang. Recursive also released rec, a small inference engine tuned for Flint on M4 Pro and M5 Pro and Max chips.

How well does it code?

Recursive claims Flint is the best mixture of experts model under 50 billion total parameters on agentic coding benchmarks. The company also says it rivals dense models with 25 to 40 billion parameters while running more than five times faster. Those two claims deserve to be read separately.

Against other sparse models, the first claim holds up well in Recursive's own numbers. Flint beats the best comparable mixture of experts model, a Qwen model with 35 billion total and 3 billion active parameters, by about 10 points on Terminal-Bench 2.1 and by about 16 points on SWE-bench Pro v2. On SWE-bench Verified the lead is less than one point, which is close to normal run to run noise.

The second claim needs more nuance. Against Google's dense Gemma 4 31B, Flint wins clearly on the agentic tests. But Alibaba's dense Qwen3.8 27B scores higher on four of the six benchmarks, including a large gap on Terminal-Bench 2.1 (73.0 against 54.9). TechCrunch also notes that on Recursive's six test average, only Qwen3.8 27B scored higher than Flint. An independent analysis by Kalaris Labs, averaging the printed scores, puts Qwen3.8 27B at about 84.7 and Flint at about 80.6. So Flint is not the strongest model of its size. It is the strongest fast one.

Two more details are worth knowing. First, all Flint scores use temperature 1.0 and an average over at least five attempts, which is a fair setup. Second, Recursive counted Terminal-Bench solutions found through web retrieval as failures, while other developers may not apply the same rule, and most rival scores in the chart are reported by their own developers. That could make Flint look slightly worse, not better, compared with rivals.

How fast is it?

Speed is the real selling point. On a laptop, generating each token mostly means reading model weights from memory. A dense 30 billion parameter model has to read all of them for every token. Flint reads only the 2.5 billion active ones, so it can be much faster on the same machine.

Recursive reports about 131 tokens per second on an M4 Pro MacBook Pro at short context. TechCrunch compares that with 58 to 69 tokens per second for similar models on the same machine, including Gemma 4 and Nvidia's Nemotron 3.5 Lightning. On a 64 GB RTX Spark, Flint passes 150 tokens per second.

The headline number is a short context figure, though. Kalaris Labs points out that the same Recursive chart shows Flint at about 69 tokens per second when averaged over a full 128,000 token session. That is still the best on the chart, but a coding agent that has loaded a real repository will feel closer to the second number. Much of the advantage also comes from Recursive's tuned runtime. On the generic llama.cpp path, Flint is only about 1.1 to 1.3 times faster than the fastest sparse rival, according to the same analysis.

Against dense models with similar scores, the gap is large. Flint on MLX is roughly ten times faster than Qwen3.8 27B at short context and almost seven times faster through 128,000 tokens. For anyone who has waited on a slow local model, that difference is the whole point.

What the Eureka Machine actually did

The most interesting part of the release is not a benchmark. It is Recursive's description of how much of the work its AI system did. According to the company, the Eureka Machine runs the full research loop: it proposes ideas, implements them, tests them and uses the results to decide what to try next. It explores several directions in parallel and combines promising ones.

Recursive gives three concrete examples:

• Architecture. Starting from a baseline with 35 billion total and 3 billion active parameters, the system explored different expert setups and widths. It settled on 40 billion total and 2.5 billion active as the best mix of speed and coding performance among the options it tested.
• Quantization. Against common practice, it compressed the output layer to 4 bits. In a model with so few active parameters, that layer is a large share of what gets read for every token. To win back accuracy, it added a distillation step in which the compressed model learns from its own earlier answers, with the full precision model as teacher. Recursive says this nearly halved the training steps compared with its baseline, and the final NVFP4 model lands within 0.7 points of the 16 bit version.
• Kernels. It wrote specialized code for low bit math, expert routing, attention and prompt processing on Apple Silicon and the RTX Spark. On M4 and M5 chips, it produced a single Metal "megakernel" that fuses the entire decode step.

Socher added on X that Eureka's work included a sparser model shape, low bit training that shrank Flint by 71 percent with almost no loss in quality, and the custom kernels. He told Wired: "Nearly every aspect of the entire model pipeline was actually done and improved upon by AI."

What the company has not shared is just as important. Recursive did not say how much researcher time the system saved, and only Recursive can verify how much of the work was really automated. Humans set the goals, chose the benchmarks and approved the results. This is AI assisted research at a high level, not a machine that built a model on its own.

The safety debate

Recursive self improvement is one of the most argued about ideas in AI. Supporters see it as the fastest path to scientific breakthroughs. Critics fear that a system able to improve itself again and again could slip out of human control. Wired notes that several bills in the US Congress propose to ban it, and that a researcher who quit Anthropic in September warned that advances including recursive self improvement could become an existential risk within a few years.

Recursive treats these fears as closer to science fiction. Socher told Wired that Flint was built with a system that cannot escape human control, and that the long term goal of full self improvement would still be overseen by people who can stop the process. The Flint model card itself is more modest: it warns that the model can still produce wrong or harmful code and unsafe tool calls, and asks developers to review changes and enforce their own permissions.

Recursive is also not alone. OpenAI said last month that by mid August its AI agents were logging 3.1 workdays for every one worked by a human researcher. A team from Microsoft, Carnegie Mellon University and the University of Virginia recently published work showing a coding agent that improves itself by changing both its software harness and its own model weights. Flint is one of the clearest public examples so far of this trend producing a usable product.

What it means for developers

For developers, Flint is practical news. A capable coding model that runs on your own machine means three things: you can work offline, your code never leaves your computer, and there are no per token fees. For companies with strict data rules, that can matter more than a few benchmark points.

Early hands on reports are positive. Wired's Will Knight tested Flint on a Mac with 48 GB of memory and used it to build a command line to do list, a tool that searches the web for research papers and a simple website. He found that smaller coding models often stall or make errors compared with Claude or Codex, but Flint "nailed" his requests every time. On r/LocalLLaMA the release thread drew hundreds of upvotes and a long technical discussion within a day.

Recursive is honest about the limits. It suggests splitting large tasks into smaller steps and giving clear instructions, and says Flint may need more guidance than the big closed coding assistants. Long reasoning can also eat the output budget, so Recursive recommends at least 32,768 output tokens for hard problems.

Our take

Flint is a good model, but the bigger story is the process behind it. A team of a few researchers, working with an automated research loop, built a competitive coding model from scratch in under four months. Each of the three Eureka examples is a normal engineering choice that a human team could make. What is new is that a system proposed, tested and combined them at speed.

The marketing is a step ahead of the evidence. "Best in class" is true for sparse models, not for every model of its size, and the speed headline is a short context number on a tuned runtime. Independent benchmark runs over the next weeks will show how much of the lead survives.

Still, if Recursive's bet is right, each model makes the Eureka Machine a slightly better researcher, which then builds the next model faster. Flint is the first data point on that curve. For developers who want a fast, private and free coding model today, it is worth a download. For everyone watching where AI research is heading, it is worth watching what Recursive ships next.

Sources: Recursive: Introducing Flint, Flint model card on Hugging Face, TechCrunch, Wired, Kalaris Labs analysis, OrcaRouter on the release files.