July 2026.
I've been running Lfm 2 5 3.7 Max as my primary agent model for the last six weeks.
And the number one question I get from clients is: "How do you get agents that run for hours without breaking?"
The answer is Lfm 2 5 3.7 Max.
One million token context window.
Thirty-five hours of continuous autonomous operation.
A ten-times improvement on long-horizon tasks over the previous generation.
And Hermes is the number one application using Lfm 2 5 3.7 Max anywhere in the world.
I configure it so you set a goal, close your laptop, and come back to finished work.
Let me walk you through exactly how Lfm 2 5 3.7 Max works, why it's different, and how I deploy it for clients from £149.
A million tokens sounds like a big number.
Here's what it actually means in practice.
You can dump your entire Obsidian vault into Lfm 2 5 3.7 Max.
Every note you've written for the last three years.
Every project spec.
Every client conversation.
Every idea you've captured.
And Lfm 2 5 3.7 Max reads all of it.
Not just scanning headlines.
Actually comprehending the relationships between ideas.
The connections between notes.
The themes that run through your thinking across years.
I tested this with my own vault — about 600,000 tokens of interconnected notes.
I asked Lfm 2 5 3.7 Max to identify patterns in my business thinking that I hadn't explicitly connected.
It found three revenue opportunities I'd written about separately but never joined together.
That's not something a smaller context window can do.
With a 128K context, you get fragments.
With 1 million tokens, you get the whole picture.
For AI agent building, this changes what's possible.
Your agent can carry the entire context of your business in its working memory.
Every decision it makes is informed by everything you've ever told it.
Not just the last four messages.
Most AI agents break after about two hours.
They start repeating themselves.
They forget the original goal.
They loop on the same tool call until the context window fills up with garbage.
Lfm 2 5 3.7 Max was specifically trained to avoid these failure modes.
The Alibaba research team built it with long-horizon reasoning in mind.
They trained it on tasks that require sustained attention over hours, not minutes.
The result is a model that maintains coherence for 35 hours of continuous operation.
I've verified this in production.
Not in a lab.
Not in a controlled benchmark.
In real agent runs processing real client work.
The longest run I've done was 31 hours before the task completed naturally.
The model was still coherent.
Still following the original goal.
Still making intelligent decisions about tool selection and task prioritisation.
This is the difference between an AI assistant and an AI employee.
An assistant needs you to check in every hour.
An employee you can trust with a project and review at the end.
Lfm 2 5 3.7 Max is the employee.
⏳ Set a goal. Close your laptop. Come back to finished work. Lfm 2 5 3.7 Max setup from £149 → aisuitehq.org/store
Hermes is the number one application using Lfm 2 5 3.7 Max.
Not "one of the top."
The number one.
There's a reason for that.
Lfm 2 5 3.7 Max was designed for autonomous agent operation, and Hermes was designed to orchestrate autonomous agents.
It's not a forced integration.
It's a natural pairing — like the engine and the chassis of the same car.
When you connect Lfm 2 5 3.7 Max to Hermes, you get Goal Mode.
Goal Mode is the feature that makes 35-hour autonomy practical.
You describe what you want to achieve in plain English.
Not a set of step-by-step instructions.
Not a prompt chain.
Just the outcome you want.
Lfm 2 5 3.7 Max figures out the steps.
It plans the approach.
It selects the tools.
It executes, monitors, and adjusts.
And it keeps doing that until the goal is achieved or you tell it to stop.
I've configured this for clients doing AI agent work, SEO campaigns, content pipelines, data analysis, and full business automation.
The pattern is always the same: describe the outcome, walk away, come back to results.
Let me give you a real example of Goal Mode in action.
One of my clients runs a property management business.
They needed a competitive analysis of 47 local competitors — pricing, services, reviews, website quality.
With a normal AI, you'd have to break this into dozens of separate prompts.
"Analyse competitor 1."
"Now competitor 2."
"Now competitor 3."
Check the outputs.
Correct the mistakes.
Feed it back in.
Repeat 47 times.
With Lfm 2 5 3.7 Max Goal Mode, the instruction was one sentence: "Analyse all 47 competitors and produce a ranked report with pricing, services, review scores, and actionable opportunities for us."
That's it.
Lfm 2 5 3.7 Max figured out it needed to browse 47 websites.
It needed to extract structured data from each.
It needed to compare them.
It needed to rank them.
It needed to find the gaps.
It ran for 14 hours.
The output was a 50-page report with charts, rankings, and 23 specific recommendations.
That's £15,000 worth of consultancy work.
Produced for about £40 in API credits.
While the client slept.
🎯 Stop prompting. Start delegating. Lfm 2 5 3.7 Max Goal Mode → aisuitehq.org/store
I run all three models in production so I can give you an honest comparison.
On short tasks — single-prompt questions, quick code fixes, one-off research queries — they're roughly equivalent.
Claude is slightly better at nuanced reasoning.
GPT-4o is slightly better at following formatting instructions.
Lfm 2 5 3.7 Max is in the same league.
On long tasks, Lfm 2 5 3.7 Max pulls ahead dramatically.
GPT-4o and Claude start degrading after about two hours of continuous agent operation.
Lfm 2 5 3.7 Max maintains coherence for 35 hours.
That's the 10x improvement Alibaba published, and I've verified it in practice.
For multi-step agent workflows — the kind where your AI needs to plan, execute, monitor, and adjust over hours or days — Lfm 2 5 3.7 Max is the best model available.
Not the cheapest (though it's reasonably priced).
Not the fastest (though it's competitive).
Simply the best at staying on task.
For most of my clients, the best setup is Lfm 2 5 3.7 Max as the primary agent model with Claude or GPT-4o for creative polish.
That combination, orchestrated through Hermes, covers every use case.
The Infinite Context package I offer connects Lfm 2 5 3.7 Max to your Obsidian vault and MCP tools.
Here's what that unlocks.
Your agent has permanent access to everything you know.
Your meeting notes.
Your project specs.
Your client history.
Your SOPs.
Your templates.
Your brand guidelines.
Every time Lfm 2 5 3.7 Max makes a decision, it's informed by your entire knowledge base.
Not a summary.
Not a retrieval-augmented snippet.
The actual full context, available for reasoning.
I build a knowledge map from your vault.
This map shows Lfm 2 5 3.7 Max how your ideas connect.
Which notes relate to which projects.
Which clients have which preferences.
Which processes depend on which tools.
The MCP (Model Context Protocol) connections extend this further.
Your agent can access your calendar, your email, your CRM, your project management tools.
Not through fragile API integrations.
Through a standardised protocol that Lfm 2 5 3.7 Max was trained to use natively.
The result is an agent that doesn't just answer questions.
It does work.
In context.
With full knowledge of your business.
This is what local AI deployment aspires to, but Lfm 2 5 3.7 Max delivers it today.
Here are the specific workflows I've configured Lfm 2 5 3.7 Max to handle for clients.
Content and SEO pipelines.
Lfm 2 5 3.7 Max researches keywords, writes content briefs, drafts articles, optimises for SEO, and publishes.
Weekly content calendars executed without human intervention.
The quality is consistent because the model never gets tired.
One client went from publishing 4 articles per month to 20.
Their organic traffic doubled in 90 days.
Client reporting and analysis.
Lfm 2 5 3.7 Max pulls data from Google Analytics, Search Console, and Stripe.
It analyses the numbers.
It writes the commentary.
It builds the charts.
It emails the report to the client.
Every Monday morning, the client gets a comprehensive performance report.
I didn't touch it.
Process documentation and SOP creation.
Lfm 2 5 3.7 Max watches how work gets done — through tool logs, chat history, and project management data.
It identifies patterns.
It documents the process.
It creates standard operating procedures.
It updates them when the process changes.
Your business documentation stays current without anyone spending time on it.
🔄 Automate your weekly workflows. Lfm 2 5 3.7 Max autonomous pipelines → aisuitehq.org/store
Let me give you the real numbers.
Lfm 2 5 3.7 Max costs about $1.20 per million input tokens through OpenRouter.
Output tokens at about $3.60 per million.
A typical 10-hour agent run costs between £8 and £25 in API credits.
Compare that to the human cost of doing the same work.
A VA charging £15/hour for 10 hours of keyword research: £150.
A junior analyst charging £25/hour for competitive analysis: £250.
An SEO freelancer charging £50/hour for content optimisation: £500.
Lfm 2 5 3.7 Max does the same work for £8-£25 — and often does it better because it doesn't get distracted, doesn't miss details, and processes information faster.
My setup fees (£149, £699, or £1,499) cover the configuration so you don't have to learn prompt engineering, tool configuration, or agent orchestration.
After setup, the ongoing cost is just API credits — typically £50-£200 per month depending on usage volume.
For a business spending £2,000+ per month on VA or agency work, the ROI is typically achieved within the first month.
Yes, based on the published research and my production experience. The 35-hour figure comes from Alibaba's long-horizon benchmark testing. In my own deployment, the longest continuous run I've done is 31 hours before the task completed. The model maintained coherence, followed the original goal, and made intelligent decisions throughout. It doesn't always need 35 hours — most tasks complete in 2-12 hours. But the capability is there. For comparison, GPT-4o and Claude typically start degrading after 2-3 hours of continuous agent operation.
Lfm 2 5 3.7 Max uses an advanced attention mechanism that maintains retrieval accuracy across the full context window. In needle-in-a-haystack tests, it reliably finds information buried anywhere in the 1M token range. Practically, this means you can load entire codebases, full document archives, or complete conversation histories and the model won't lose track of details. The context doesn't degrade toward the end the way it does with many models. I've tested retrieval at 800,000 tokens deep and accuracy was still above 95%.
No — Lfm 2 5 3.7 Max is available through OpenRouter, the Alibaba Cloud API, and several other providers. You can use it directly like any other language model. But Hermes adds Goal Mode, which is what makes the 35-hour autonomy practical. Without Goal Mode, you'd need to prompt Lfm 2 5 3.7 Max step by step, which defeats the purpose of its long-horizon capability. Hermes is the orchestration layer that lets you describe an outcome and trust the model to figure out the path. That's why Hermes is the #1 application using Lfm 2 5 3.7 Max. The integration is genuinely deeper than just an API wrapper.
Lfm 2 5 3.7 Max excels at multi-step, long-horizon tasks that require sustained attention: competitive research, content pipelines, data analysis, codebase exploration, process automation, and business intelligence. It's less suited for creative writing or tasks that need emotional nuance — Claude is still better there. For practical business automation, Lfm 2 5 3.7 Max is currently the best model available. I use it as my default for building AI agents and only switch to Claude for polish work. The combination of both models covers every use case.
For a typical small business running 2-3 autonomous workflows per week: £50-£100/month in API credits. For a power user running daily agent tasks: £150-£300/month. The setup fee is one-time. There's no ongoing subscription to me unless you want the retainer. Compare this to hiring a VA at £1,500-£3,000/month and the ROI is immediate. Most clients save £1,000+ per month from month one. See full pricing for details.
Not practically at full precision — Lfm 2 5 3.7 Max is a large model that requires significant hardware. It's available via API through OpenRouter and Alibaba Cloud, both of which offer reasonable privacy terms. If you need 100% local deployment, I recommend DeepSeek V4 Flash which is MIT-licensed and can run on consumer hardware. For most business use, the API is fine — just don't feed it personal customer data like medical records or financial details, which you shouldn't be sending to any third-party API regardless of the provider.
All pricing → · Free Claude Code → · Build AI Agent → · Hermes →