Not every AI task needs a giant LLM.
@levantolabs just launched Sage2 and claims Extract is 166x cheaper than Claude Opus 5.5.
I think developers building AI agents should pay attention.
The interesting idea?
Instead of using a large LLM for every small task, Sage2 focuses on two specific jobs: extracting information and making decisions.
1. Extract: Natural language → Structured data
Most software expects structured inputs.
But users communicate through emails, messages, documents and conversations.
Extract is designed to bridge that gap.
• Pulls names, dates, amounts and other details from natural language.
• Converts unstructured text into structured fields.
• Helps applications understand what a user is actually asking for.
• Shows the source text and a probability for each value.
• Returns null when a detail isn't there.
Example:
A customer says:
"My blue jacket arrived with a broken zip. Please send a replacement."
Extract can identify:
• Product: Blue jacket
• Issue: Broken zip
• Request: Replacement
• Order number: null (not in the text)
Your application can then process that information.
2. Decide: now with streaming
This is the part I find most interesting.
Decide is a decision model with streaming.
Send each new message, and it updates the answer using everything said so far.
• Processes growing context.
• Supports structured decisions.
• Can update its answer as new information comes in.
• Designed for real-time applications and AI agents.
Levanto claims 3x faster decisions on growing context, as low as 50 ms per decision.
3. The cost and benchmark claims
According to Levanto:
• Extract is 166x cheaper than Claude Opus 5.5.
• Sage2 scored 77.8 on extraction accuracy.
• It scored 72.2 on identifying information that isn't present.
• Cloudflare Clairic scored 75.3 and 59 respectively on the same benchmark.
These are company-reported results, not independent proof.
4. Real-world use cases
Some early users are already putting Sage to work:
• Normies Arena reported 15M+ agent decisions in about a month.
• PerpFlow reported 500K+ decisions in the past month for trading-related decisions and database analysis.
• PerpFlow's founder also reports cutting a research workflow from 14 days to 12 hours.
• GrowBlocks uses Sage to power interactions inside its AI-driven virtual city.
We're moving from AI that simply generates responses to AI that can make decisions inside live applications.
5. Availability
• Available through Levanto.
• Also available on
@near_ai .
• Agents can access it through MPP using
@stripe cards and
@tempo stablecoins.
• Levanto offered 20M free tokens to early sign-ups during its launch promotion.
Website:
levanto.ai/
Playground:
playground.levanto.ai/
My take:
I don't think every task needs a massive LLM.
Imagine an AI agent handling 100 different operations.
Does it really need an expensive general-purpose model for every single decision?
Probably not.
A specialised model for extraction, another for classification, and a larger model for complex reasoning could make the whole system faster and more cost-efficient.
But there's one thing developers shouldn't overlook:
A model making a decision doesn't mean it should have unrestricted permission to execute it.
Keep validation, permissions and human approval where they matter.
The future of AI agents might not be one giant model doing everything.
It could be multiple specialised models working together.
That's the direction I'm watching.