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Extraction models turn human language into data your software can act on. We're bringing them to Sage. Early access tomorrow for everyone who registers on @levantolabs by PT today. Demo 📺👇 Most software needs structured inputs. People communicate through emails, chats, tickets, contracts, and conversations. Extraction models connect those two interfaces. You define the information your application needs, and the model identifies it in the text: people, companies, products, dates, amounts, descriptions, or requests. Calling this “extracting stuff from documents” understates what developers can build with it. Consider a customer message: “The blue jacket arrived with a broken zip. Please send a replacement to 18 King Street.” An extraction model can identify the product, defect, requested remedy, and address. Your application now has fields it can validate, look up, and pass to a tool. A decision model can then evaluate whether the request qualifies for an automatic replacement, needs more information, or requires human review. Extraction → decision → action. Each stage has a defined responsibility. Extraction supplies the values. The decision model evaluates the available options against your policy and context. Your application validates the result and controls execution. That separation matters for agent builders. You can inspect what the model extracted, test the decision independently, and control which actions are permitted. Speed changes where this becomes useful. At roughly 200 ms for extraction, developers can start putting language interpretation directly into interactive software: • A support interface that fills relevant fields while an agent reads a ticket. • A browser agent that extracts values from a message before populating a form. • A find-in-page feature that highlights cancellation deadlines or product complaints, even when the text uses different wording. • A workflow that turns an incoming email into validated tool arguments and evaluates the next action. The opportunity extends to any application where someone currently reads a message, copies information into fields, and decides what happens next. Developers can define the information they need and the decisions their software must make, then connect those outputs to existing functions. We're bringing extraction and decision models together in Sage so builders can implement that flow. Watch the demo below. Register on Levanto by PT today for early access tomorrow.

The ants are here! 🐜 The best model router (powered by our Sage decision model, of course), directly integrated into my favorite decentralized inference network. Enjoy 😎 Benchmarks below 👇

SageCut is going viral New video production pipeline: → Generative model creates frames → Decision model (QA): is this frame good or not? → Regenerate until it passes QA, which used to be done with LLMs, is now orders of magnitude cheaper and faster.

New day, new use case 😂 GenAI 📽️video has a hidden cost: most tokens are spent judging frames, not making them. The fix: → Generative model creates (and re-creates) → Decision model: QA, is this frame good or not? Voilà ✨ #SageCut

What does @Microsoft do when they can’t win a benchmark? Leave out whoever beat them 😄 The launch chart is built on JevBench but skips Sage by @LevantoLabs, #1 in the API category where Microsoft, OpenAI and Jev compete. @satyanadella, this is not fair. Here’s the real chart: benchmarkheaven.com/jev-mode…

Sage by @LevantoLabs is the first decision model, launched in July, and currently the best on the market. It wins across all the evals proposed by @perplexity_ai's CEO (nice job @AravSrinivas!), and it's first on JevBench, beating Jev and Google's models on every dimension. BUT frankly, benchmarks are not what excites us. I'm BORED by the dozens of new decision models launching every day with a tiny +0.02 on some eval. They all look the same. The magic is somewhere else. There are thousands of new things that could be invented in the space of decision models and models for machines. The invention space is huge! After launching Sage, we also pioneered: - The first guardrails and model routers built on decision models - Automated context engineering with "grounding" (automatic web search when confidence is low). Decision models are only as smart as their context, especially because they can't make tool calls. - Reasoning. In many use cases you don't know whether your question is System 1 or System 2. With reasoning, the model is as fast as a System 1 model when the question is System 1, and takes more time when it's needed. This enables hard, multi-step decisions. Our clients just want the correct answer, and in some use cases they're happy to wait 0.5s more. This will soon become the market standard (GLiDE/@george_onx and @TypeLLM already followed). And *so* much is yet to come. Models for machines: it's just the beginning! We're cooking. Follow @levantolabs for new product primitives that will surprise you. Open innovation is HEALTHY. Competition is HEALTHY. Benchmark-race is BORING. Product inventions are FUN. Go go go!!!

On JevBench, Sage1 scores better on accuracy than other System1 models, with a similar median latency. When multi-step reasoning is required, Sage has 93% accuracy vs. 71% for other System1 models. Sage is smart enough to know when to be fast and when to think step by step, covering both System1 and System2. When Sage activates reasoning, it takes on average 3X longer to decide.

Since we launched the first decision model in July, we've been concerned that models like ours (and now Jev) can't answer basic logic that a 12yo can solve. A ball is under cup A. Swap cups A and B. Swap cups B and C. Swap cups A and B. Swap cups A and C. Which cup is the ball under? Do you really care about a 200ms answer if it gets basic stuff confidently wrong? Today @levantolabs releases Sage1: – It's multimodal: it can see! – It reasons, but only when needed. So it knows where the cup is :) But there are downsides.