Web agents now 100x cheaper by making DeepSeek write code
备注：以下为自动提取的正文内容。
Catch Retriever hacking the web and one-shot complex workflows in your browser.
There is an adversarial relationship between developers and the big model labs.
Developers pay premium API prices. The labs use that margin to subsidize their own apps, their own competing agent harnesses, and their own consumer subscriptions.
If you are building an AI IDE, browser agent, support agent, or workflow product on top of a frontier API, you are often subsidizing the company trying to replace you.
That has been the uncomfortable bargain under the agent market: use the best closed model, pay the tax, then watch the same lab bundle an agent product against you.
Not because it is the smartest model in the abstract. Because it hits the exact hot path agent products were overpaying for: cheap, fast, text-only code generation against a harness.
DeepSeek V4 Flash is open, cheap, long-context, and strong enough at code that the harness becomes the moat again. Once the model is good enough to compile browser work into executable code, inference providers start racing to the bottom on hosting and every non-SOTA model bill starts looking optional. Even Microsoft is reportedly weighing DeepSeek for Copilot Cowork as it moves agent pricing toward usage-based economics.
For two years, the default browser-agent stack was quietly absurd:
That architecture does not just use the model for judgment. It rents the model as the runtime.
That was great for API bills and terrible for agent products.
The uncomfortable version is simple: developers were being milked for runtime, not intelligence. Big labs could charge external builders premium API rates for every agent loop while subsidizing their own first-party agent experiences. If your agent needed 80 model calls to finish one workflow, that was not a bug in the pricing model. That was the business model.
Once a cheap text/code model can write the plan once, and a browser harness can execute that plan locally, the frontier API moat gets a lot smaller. The model does not need to be the worker. It can be the compiler.
That is the real unlock behind our new Retriever architecture:
Code-as-plan changes that. A workflow that used to be 40 to 100 model turns can become one planning call, a few targeted semantic extractions, and normal JavaScript doing the boring work at machine speed.
For Retriever, switching the hot path to DeepSeek Flash gave us over a 100x cost decrease while preserving the practical browser-agent performance we needed from Gemini Flash-class models.
It is a new bargaining position for every agent harness builder.
Text-only beats screenshot-first for cost and cacheability. The browser already has the DOM, forms, links, inputs, URLs, cookies, routes, and page text. Throwing that away and asking a vision model to rediscover it from pixels is expensive. Language is also a much more efficient sparse representation for this kind of work than raw pixels.
Code beats tool-call transcripts. Most browser work is loops, filtering, retries, URL construction, extraction, deduping, and structured output. Those are programming tasks. A for-loop should not cost tokens.
The harness is the product. If open models can write good code, the value moves from model access to the callable DSL: getPageTree find click type pageAction extract processText callTool askUser , sheets, KBs, recordings, cloud scrape, pause, cancel, and logs.
The authenticated browser is the runtime. Valuable automation needs the user's real session: SSO, cookies, CSRF tokens, extension permissions, selected tabs, service-worker state. Moving everything to a remote browser means recreating state the user already has.