Cursor, the AI coding tool recently acquired by Elon Musk’s SpaceX in a $60 billion all-stock deal, has launched a model router designed to direct every coding request to whichever model handles it best, bypassing the need to pay frontier prices for work that doesn’t need it.

Under the hood, the new Cursor Router uses a triage system à la a hospital emergency room: It looks at what a request actually needs — how hard it is, what it’s for, the surrounding code — and picks a model that is the best fit. A quick fix goes somewhere cheap, while a genuinely hard problem gets escalated to something closer to frontier-grade.

Notably, developers and admins also have access to three distinct modes that nudge that balance in either direction, favoring speed and cost over raw power, or vice versa if needed.

The broader rationale for Cursor Router, according to the company’s own field CTO David Pan in a social media post on Wednesday, is that developers shouldn’t have to become experts in model performance just to write code.

“We briefly went insane and decided every software engineer should also become an expert in model benchmarks, thinking levels, and cache hit rate.”

“We briefly went insane and decided every software engineer should also become an expert in model benchmarks, thinking levels, and cache hit rates,” Pan writes.

We briefly went insane and decided every software engineer should also become an expert in model benchmarks, thinking levels, and cache hit rates.

Cursor Router is out today. It picks the model for each task based on measured quality against cost. Fable 5 level performance for… https://t.co/3B2103mGWr

— David Pan (@davep) July 22, 2026

Early community feedback has largely echoed Pan’s sentiment: Fatih Arslan, a software engineer at PlanetScale, notes on X that engineers already juggle the choice between cost and capability by hand — defaulting to a cheap, fast model for routine work and saving the slow, expensive one for “serious tasks.”

Nice! I wanted something like this for a long time. See my previous rant last year. I think as engineers, we're already developing skills ourselves which model to choose. We use a less costly/fast model for implementing chore details, but use a higher reasoning model (slow and… https://t.co/5y5xMOR2E6

— Fatih Arslan (@fatih) July 22, 2026

“We already spend quite a bit [of] time on [choosing models],” Arslan writes. “Why not automate that part? Cursor Router does the automation.”

“We already spend quite a bit time on it [choosing models]. Why not automate that part?”

In a separate blog post published on Wednesday, Cursor claims that early access customers saved 30-50% compared to routing everything through Opus 4.8, with no drop in output quality.

Working model: Taking control of the stack

The launch follows a run of moves by Cursor to control more of its own AI stack. In May, the company released Composer 2.5, an update to its in-house coding model built for long tasks at a lower cost than frontier options from Anthropic and OpenAI. Composer 2.5, like its predecessor, is built on Moonshot AI’s Kimi K2.5, an open-weight model out of China.

Now, with the weight of one of the world’s most valuable companies behind it (SpaceX has attained a market cap of $1.5 trillion since its June IPO), Cursor is pushing a powerful frontier model of its own.

On July 8, Cursor and SpaceXAI jointly released Grok 4.5, a mixture-of-experts model built on a new foundation dubbed V9, which Musk had previously noted was roughly 1.5 trillion parameters. The model’s trained on trillions of tokens of real Cursor usage data and available across all Cursor plans at $2 per million input tokens and $6 per million output tokens.

With Composer handling cheap, fast work, and now the Grok-branded frontier line for more serious horsepower, Cursor has its own models in the mix alongside the usual list of outside providers. And this gets to the heart of why Cursor built Router: Most developers pick one model and stick with it regardless of the task, billing simple work at frontier prices it doesn’t need.

Sending every request to its own models would be the easy way to keep that money in-house, but it would also mean shipping inferior output on some tasks — so Router instead sends each request to whichever model actually suits it, Cursor’s own or not.

The lay of the land

Model routing itself isn’t exactly new. OpenRouter has offered a version since 2023: a single API sitting in front of more than 400 models from over 60 providers, including OpenAI, Anthropic and Google. Its own auto-router feature does roughly what Cursor Router does — classify a request, then send it to whichever model fits the task and the person’s stated preference between cost and quality.

More recently, OpenRouter launched Fusion, which takes a slightly different approach: instead of picking one model, it sends a prompt to several models at once and uses a judge model to synthesize the strongest answer out of all of them.

This past month ushered another entrant to the mix: Japan’s Sakana AI released Fugu in June, which instead breaks a single task into subtasks and routes each piece to a different model, pitched by Sakana as a hedge against relying on any one AI provider.

“[Cursor Router is] a great example of how a technological innovation immediately translates into a product improvement.”

Not everyone rates some of these other attempts, though. On Wednesday, Kirill Balakhonov, head of AI products at Nethermind, argues on LinkedIn that Cursor’s version succeeds precisely because it’s focused on coding specifically, rather than trying to be a general-purpose router for any task.

“A great example of how a technological innovation immediately translates into a product improvement… rather than an abstract idea like Sakana Fugu or OpenRouter Fusion,” Balakhonov writes, predicting both of those broader routing efforts would fade from use.

What’s new, perhaps, is some of the names emerging behind an array of model diversity efforts. In early July, Microsoft launched a $2.5 billion services unit dubbed Microsoft Frontier Company, embedding thousands of engineers at customer sites to help them build with a mix of AI models.

Judson Althoff, CEO of Microsoft Commercial Business, told Reuters at the time that the push came partly from watching rivals like DeepSeek and Google’s Gemini close the gap on OpenAI. Referring to the original Copilot, he admitted, “we made a mistake by binding it to OpenAI models only.”

If the company with the deepest single-model relationship in the industry is walking it back, the idea for model flexibility has clearly gone mainstream — certainly if this week is anything to go by.

Ramp and Meta get in on the act

On Tuesday, Ramp, the $44 billion spend-management behemoth, opened up Ramp Router, an early-access public version of the model router it built to manage its own AI bills internally, which it says cut its LLM costs by roughly 30%. It’s free to start, requires no Ramp account, and routes across OpenAI, Gemini, and select open-source models including Kimi through an OpenAI-compatible endpoint.

The very same day, Jyoti Mann from The Information reported that Meta is also working on a model router. According to internal documents cited in the report, an incubator inside Meta called AAI Labs is developing a new product named Switchboard that would score each request for difficulty and send simpler ones to smaller, cheaper models — aimed initially at cutting Meta’s own AI agent costs, though it reportedly could end up as a public release.

Meta has particular reason to want this. Data from Runpod’s State of AI report, published in March, suggests Meta’s open Llama models are now a marginal presence in production: Llama 4 has near-zero real-world deployment, with Alibaba’s Qwen having overtaken it as the most-deployed self-hosted LLM.

Meta has also been building proprietary models in response. Muse Spark, its first model out of the newly formed Meta Superintelligence Labs, shipped in April. That was followed in July by Muse Spark 1.1, Meta’s first model with a public, paid API, priced at roughly a quarter of what OpenAI and Anthropic charge for comparable models.

Meta is aggressively targeting the incumbents, and Switchboard fits the same pattern: a way to make it easier for users to cut costs, switch models freely, and, where it makes sense, land requests on Meta’s own models instead.

But amidst all this hullaballoo about model routing, there is perhaps a broader question of openness. Not of the models themselves, which is a fervent debate in itself, but of whether the routing decision itself — the logic that decides which model handles which request — should sit inside a vendor’s own closed product at all.

“Is anyone building this as open-source?”

Elvis Saravia, a former technical product marketing manager at Meta AI who co-founded DAIR.AI, took to X to argue that it shouldn’t, given how differently teams weigh cost against quality.

“Is anyone building this as open-source?” Saravia asks. “It feels like this is something you don’t want to offload to an API. We all work with different trade-offs, so we need the ability to achieve custom routing.”

Recommended reading. Cursor Router routes tasks to the right model.

Is anyone building this as open-source? It feels like this is something you don't want to offload to an API. We all work with different trade-offs, so we need the ability to achieve custom routing. https://t.co/zcrpHbnlCe pic.twitter.com/Vw2xyMy1jp

— elvis (@omarsar0) July 22, 2026

As for Cursor’s own version, Router is currently available to Teams and Enterprise customers only, across desktop, web, iOS, CLI, and Cursor’s SDK. It’s not yet clear whether it will eventually land on individual plans.

The post Cursor, Ramp, and Meta are all building model routers — but two have major model ambitions themselves appeared first on The New Stack.

Thomas Dohmke, who stepped down as GitHub’s CEO last year to become a founder again, is opening a preview of a distributed Git network on Wednesday that is designed to keep fleets of AI coding agents from overwhelming a single central server — and one that may soon compete directly with GitHub’s core service.

Entire, Dohmke’s post-GitHub startup, is launching a preview of this on Wednesday (but for now, it is behind a waitlist). With this, developers can mirror an existing GitHub repository onto Entire’s own infrastructure in one step.

“In the era of agents, centralized Git hosting has become a fundamental constraint, as the strain of billions of agents and developers hammering a central server shows up in the form of rate limits, high latency, or even outages,” says Dohmke in today’s announcement. “Today, we begin to return Git to its original promise, with a distributed, and soon fully decentralized and open-source network of interconnected nodes around the world. By doing so, we enable any developer or agent to host their code in-region, pushing, pulling, and cloning close to where they operate, fast and without bottlenecks, while still part of a global, collaborative network.”

The key here is that the code stays on GitHub, as Entire stresses, but coding agents can work with the Entire mirror and, as the company notes, “build without rate limits.”

Entire’s mirror is meant to absorb the constant flow of traffic that a fleet of agents can generate. That traffic, after all, is part of the reason GitHub is often buckling under pressure these days and startups like Entire have an opening.

Centralized Git hosting, Dohmke says in an interview with The New Stack, has become “a fundamental constraint” now that billions of agent and developer operations land on the same servers, showing up as rate limits, latency, and outages.

Given GitHub’s recent availability issues, it’s no surprise that startups are trying to get into this space. Entire is one — and it has the pedigree — but in June, Cursor also announced Origin, its own Git forge rebuilt for swarms of agents that are cloning and committing against a single repository in parallel.

Entire is starting with active regions in the United States, the European Union, and Australia, but the team says that now that is has spun up its first few regions, it will add more soon.

‘Git as a database’

To build its network, Entire rewrote the server part of git. GitHub, GitLab, and Bitbucket all wrap the server-side of the Git binary and build their infrastructure around it. Entire started from scratch.

“We see Git really as a database,” Dohmke says. The open source Git project has two halves, he explains: the client that an agent uses to talk to a repository, and the server a host runs to manage storage. Rather than build on that stock server, like most companies would do, “we made the decision of not going that route, and instead implemented our own Git backend.”

That only makes sense if Entire’s version has significantly better performance than the stock Git server, of course. Entire says its benchmarks have pushed the network to a sustained rate of 570,000 clones per hour, 586 pushes per second, and roughly 470 combined clone-and-push operations per second.

Pushing to a native Entire branch can run up to 25 times faster than pushing through to GitHub, Dohmke says.

Entire it will open-source both the git backend and the benchmark suite.

The foundation layer, now real

When Dohmke first described Entire’s plans to The New Stack in February, he described a three-layer platform that included a Git-compatible database at the bottom, a semantic reasoning layer in the middle, and an interface on top. Even then, he said that the database, unlike a centralized Git host, could be a globally distributed network of nodes.

But in February, Dohmke also said Entire wouldn’t necessarily end up competing with GitHub, and that code repositories would stay central to the pitch.

Pressed on whether that still holds now that Entire hosts its own copy of the GitHub repo, he calls the mirror complementary, in part because Entire can offer enterprises the ability to keep their code in a local region to fulfill local regulations. He also notes that GitHub has a huge ecosystem and an extended feature set.

“I think the question for the buyer really is, is it not better for me from an availability and reliability perspective, that I have both of these products, so if one of them is down — there’s always going to be single points of failure and human errors — then I have my mirror on the other side,” he says. “But we certainly will, in deals, compete for the dollar spent at a much smaller scale compared to the multi-billion-dollar business that is GitHub today.”

For now, that keeps the two complementary. Dohmke argues that GitHub remains the “source of truth,” or “cold storage,” while the working copy lives on Entire. But he also says that Entire will launch native repositories in the coming months, and those wouldn’t need GitHub underneath at all. All of this will be open-sourced as well.

Entire raised its $60 million seed round in February, when it had 15 employees. Felicis led it, with Microsoft’s venture arm among the backers. The company is now past 40 people and aiming for 60 by the end of the year.

Entire beyond Git: the semantic memory layer

Entire is building its middle layer — the semantic reasoning layer — in parallel with the Git platform.

The semantic layer now integrates with every major coding agent, including Claude Code, Codex, Cursor, Factory AI, and GitHub Copilot, and records each session, prompt, and tool call in the repository alongside the code.

Having this data is useful for agents, and it was the first core service the company launched. Now, it is also building more services on top of that history.

The company is adding Entire Blame, for example, which shows not just who last touched a line but the agent session and prompt behind it. There is also Entire Review, which fans out several agents for an intent-aware review, and the company is adding a code and semantic search feature that lets agents (and developers) search across code changes and the reasoning that produced them.

“Session logs are now the second most important artifact in software development, and they belong in the repository alongside the code,” Dohmke says.

The post Entire is building a Git network for agents appeared first on The New Stack.