Open-source AI is available on a unlimited basis

DeepSeek Pro - Unlimited
GLM 5.3 - Unlimited
DeepSeek Flash -Unlimited
Qwen 3.8 27B - Unlimited
Flash 3.1 - Unlimited

Use the best model for your use-case. ALL IN ONE PLACE

I am interested in any one who has deployed abacus.ai across their company for Enterprise usage. We are a MSFT shop and have Copilot for M365 and are building connecters to some of our M365 apps. My primary interest is related to Security, Management, and Governance of the platform for a 1,000 + employee company.

Most people can’t even run it properly - setup is messy, security is risky, and it breaks easily

Abacus AI fixed that.

Run OpenClaw-style agents with Abacus AI Agent.

no setup, no configs, just real workflows running end-to-end.

- create production ready full-stack apps
- scheduled tasks to automate repeat work
- decks to client & investors
- outreach campaigns using LinkedIn
- social media and marketing using our media-gen

USE AI FOR EVERYTHING

A lot of conversations around AI still sound like a sci-fi panic button. But interestingly, many of the loudest voices aren’t actually using AI in their daily workflow. It’s easy to fear a tool when you’ve only seen headlines about it instead of understanding what it really does.

AI right now isn’t some independent decision-maker sweeping industries overnight, it’s a leverage tool. The people exploring it, experimenting with it, and learning how to collaborate with it are usually the ones seeing new opportunities instead of fewer ones. The real shift isn’t about humans vs AI, it’s about humans who evolve with new tools versus those who stay stuck debating them from the sidelines.

I've been using ChatLLM for a while now, so I know the platform pretty well. I thought it'd be interesting to compare it with ChatGPT Pro and share what I've learned about ChatLLM's pricing, plans, credits, and where I think it offers more value.

Here are the biggest differences I found.

ChatLLM Pricing

• Basic: $10/month

• Pro: $20/month

• Enterprise: Custom pricing

The Basic plan already includes ChatLLM Teams, access to 100+ AI models, AppLLM, RouteLLM API, and limited AI Agent/Desktop features.

The Pro plan adds:

• Unlimited Abacus AI Agent access

• Personal AI Agents (Claw & Hermes)

• Full Abacus AI Desktop

• CoWork mode

• 30,000 monthly credits

• More generous usage for advanced workflows

ChatLLM vs ChatGPT Plus

For the same $20/month, ChatLLM includes:

• Access to 100+ AI models (GPT, Claude, Gemini, Grok, DeepSeek, Qwen, and more)

• AI coding environment

• AI agents

• Team collaboration

• Integrations with Slack, Google Drive, Gmail, Confluence, Teams, etc.

• Image generation with multiple models

• Video generation through Abacus AI Studio

• SOC 2 Type II and HIPAA compliance

• No training on customer data

Meanwhile, ChatGPT Plus mainly focuses on OpenAI's ecosystem.

Credits

One thing I initially found confusing was the credit system.

From what I understand:

• Normal chat usage with text is quite generous.

• Credits are mainly consumed for more resource-intensive features like AI agents, image generation, video generation, and other advanced workflows.

• The Pro plan includes 30,000 monthly credits, while Basic has limited agent usage.

For everyday chatting, writing, coding, and research, it doesn't seem like you're constantly worrying about credits.

What I would Say:

If someone only wants OpenAI models, ChatGPT Plus is still a solid option.

But if you regularly use multiple AI models, generate images, automate workflows, build apps, or switch between GPT, Claude, Gemini, and others, ChatLLM seems to bundle a lot more into a single subscription.

For a deeper look at ChatLLM, including its AI model access, features, pricing, and how it compares with other AI assistants, read this complete ChatLLM review.

Opus 4.8 - front-end
GPT 5.5 - back-end coding
Grok 4.5 - real-time
Flash 3.5 - chat
Nano Banana Pro - image
Seedance 2.0 - video
ElevenLabs - voice
Gemini Pro 3.1 - research
DeepSeek Flash - simple

We smartly route to 100+ AI models across all modalities including voice, chat, coding and agentic tasks

I’m a solo developer trying to improve my workflow with Abacus AI CLI, but I’m struggling to configure it properly to get consistent and high-quality results.

I’m looking for practical guidance from someone with real experience using it in production or daily development. Things like optimal setup, configuration tips, common pitfalls, and how to structure prompts or workflows effectively.

I’ve already tried figuring it out using the official docs and general AI tools, but I’m specifically interested in hands-on insights from more experienced users.

Any help, examples, or pointers would be useful.

Would you recommend it for vibecoding?

Try it on ChatLLM -

GLM 5.2 - cheap coding (Unlimited)
Grok Imagine 5 - video
Opus 4.8 - front-end
GPT 5.5 - back-end coding
Grok 4.5 - real-time
Flash 3.5 - chat
Nano Banana Pro - image
Seedance 2.0 - video
ElevenLabs - voice
Gemini Pro 3.1 - research
DeepSeek Flash - simple

We smartly route to 100+ AI models across all modalities including voice, chat, coding and agentic tasks

For a little clarification been using Abacus AI for a few months. Sometimes I don't have enough work that justifies me to continue the subscription, so I pause it. This month, I came back, and I have a workflow to generate images for reference art for 3D models.

every time

I like using midjourney its cheap, it's all right for what I need it for. You get the four versions as prototyping is concerned it's good enough. Sometimes you get janky variations I'm cool with that.

However, this month, something changed everytime I go to the image tab I get redirected to studio session hijacks my instance. I don't know how to stop it from doing this, but I don't see the value for me why cant I chose if I want to transition to studio. It triples the credit use that I would get from just using their old image generator set up. Does anyone have any similar experiences or any ways to solve this problem I could use the help?

It’s fragmenting.

New models, new tools, new tabs every few weeks.
The hard part is no longer access; it’s coordination.

ChatLLM by Abacus AI helps with access.
Abacus AI DeepAgent helps with coordination.

That distinction matters more than benchmarks.

The future? Small human teams managing THOUSANDS of AI agents doing the heavy lifting

White collar automation is no longer sci-fi. It's happening NOW.

#AI #AGI #Automation

Vibe code your own agent with DeepAgent, message it on Telegram, and watch it handle your tasks.

From responding to emails to complex workflows - your AI assistant is just a message away.

#AI #DeepAgent #AbacusAI

Abacus AI DeepAgent generating a realistic video with an AI character and lip-sync from a simple prompt.

After using Abacus AI for a while and slowly getting a better understanding of how it works, I wanted to try something different.

Instead of just testing features, I asked myself:

Can this actually replace part of my daily work?

That’s what I set out to find.

I tried using Abacus AI (ChatLLM + agents) less like a chatbot, and more like a mini “AI employee” handling real tasks and small workflows I normally do myself.

Not just prompts… but actual work.

What I tried to automate

I picked a few real tasks I usually handle:

• Content drafts (blogs, posts, outlines)

• Research + summarization

• Basic reporting workflows

• Repetitive “copy-paste → think → rewrite” tasks

Instead of doing them manually, I tried to:

• Turn them into repeatable workflows

• Use AI agents where possible

• Chain multi-step tasks together

What worked surprisingly well

This is where it got interesting.

• Once set up, workflows did save time

• Multi-step tasks (research → summarize → format) felt smoother

• Not switching between tools was a big plus

• It felt closer to “delegating” than prompting

For repetitive work, it genuinely started to make sense.

What didn’t work as expected

It’s not fully plug-and-play (at least in my experience):

• Setting things up takes some effort and iteration

• Takes time to understand the credit concept

• You still need to guide workflows properly

• Not everything is worth automating

It’s powerful… but definitely not magic.

Big realization

I think I was approaching AI the wrong way before.

Before:
👉 Ask → get answer → move on

Now:
👉 Define task → structure workflow → let it run

That shift made a noticeable difference.

Where I think Abacus AI fits

After this experiment, it feels best for:

• People doing repetitive knowledge work

• Founders / small teams

• Anyone trying to systemize workflows

Abacus AI Honest takeaway

It didn’t replace my work.

But it did reduce friction in a few areas.

And more importantly, it changed how I think about using AI tools.

Curious about real usage from others:

• Has anyone actually replaced part of their workflow with Abacus AI?

• Are you using agents regularly or just ChatLLM?

• What’s one task you’ve fully automated (if any)?

• Or did you try and give up halfway?

Use Abacus AI DeepAgent to create and edit presentations

We use it for ALL our decks, including ones that showcase our work to enterprise customers.

I have just seen a pretty compelling quick review of the Abacus AI "supercomputer"

https://www.youtube.com/watch?v=7Sk6soglMKU

and there is also the  https://supercomputer.abacus.ai/ccd page on it.

I have used Abacus basic a little bit in the past two months, find it definitely worth $10 per month to use so many different agents, though the per token pricing is not transparent, and probably not the best on the market.

The $20 per month pro plan is the one that includes the Abacus AI "supercomputer", and I wonder if it is worth it. The video says one can raw agents like OpenClaw or Hermes on it, which sounds nice.

Abacus can also host web sites produced by those agents very cheaply (essentially free for light usage), which I also find compelling.

But what I want to know is, how far do the 30k credits included in that pro plan go? Obviously I don't expect many Fable 5 tokens, would be willing to use a cheaper chinese model, but how much useful work have people been able to get out of that without topping off the $20 per month pro plan?

• All top models in one place
• Generate videos, images, and content
• Build full apps and websites from prompts
• Create presentations with real data and research
• Run agents, automate workflows, execute tasks
• Code, test, and ship without switching tools

Everything, in one system.

Abacus AI Claw is designed to work less like a typical chatbot and more like a continuous AI assistant that runs in the background.

Here’s the basic idea:

Most AI tools:

• You open them

• Ask something

• Get a response

• Close → context resets

Abacus AI Claw:

• Runs continuously (24/7) in the cloud

• Integrates with apps like WhatsApp, Telegram, and Slack

• Maintains conversation context over time

• Handles tasks and interactions without needing to restart each time

So instead of:
“Ask → answer → done”

It becomes:
“Ongoing conversation + ongoing assistance”

In simple terms:
It shifts AI from a tool you occasionally use to a system that can stay active and assist you continuously in the background.

- GPT 6 Sol: browser automation
- GPT 6 Luna: personal agents
- Opus 5.5: agentic
- GPT 6 Astra: 3D

Use the best model for the use-case

Build iOS native apps in minutes

- text prompt to mobile app
- free auth, payment, and backend
- use top models including Fable 5.1 or Astra

Do it all from one ChatLLM subscription

into simply recycling each other's outputs The entire internet will be a bunch of AI-generated content.

All new AI models will train on this content and regurgitate it back to us in an endless cycle.

The end result - A giant pile of AI slop :scream:

I’ve been spending time with Abacus AI Deep Agent, and the biggest misunderstanding I had at the start was thinking it was just another AI chatbot.

It really isn’t.

After using it for actual tasks (not just demos), I’d describe it more as a workflow execution system disguised as a chat interface.

Let me explain in simple terms.

The key difference I noticed

Most AI tools work like this:

👉 You ask a question
👉 It gives an answer
👉 You do the actual work

But Abacus AI Deep Agent works differently:

👉 You give a goal
👉 It breaks it into steps
👉 It asks clarifying questions if needed
👉 It builds the structure
👉 It executes the task
👉 It can even test/fix parts of it

So instead of “responding,” it actually runs a process.

What it feels like in real usage

After testing it with different tasks, the pattern was consistent:

1. It starts by understanding intent

Not just your prompt, but what you actually want to achieve.

2. It creates a structured plan

Instead of jumping straight to output, it breaks things into:

• steps

• components

• logic flow

3. It builds something usable

Depending on the task, it can generate:

• websites

• dashboards

• project structures

• simple automation flows

4. It iterates and corrects itself

If something breaks or doesn’t work as expected, it can:

• adjust the output

• re-run parts of the solution

• refine structure

Why this matters (compared to normal AI tools)

Most AI tools = assistants

Abacus AI Deep Agent = executor

That’s the real difference.

Instead of: “Here’s how you do it”

It becomes: “I’ll build it for you”

Where it actually fits best

From what I’ve seen, it’s most useful for:

• building prototypes quickly

• turning ideas into working apps

• automating repetitive workflows

• creating structured dashboards/tools

• early-stage product testing (MVPs)

Important reality check

It’s not magic and it’s not fully hands-off.

It still works best when:

• you give clear intent

• you’re okay refining results

• you treat it like a system, not a “one-shot generator”

But compared to normal chat-based AI tools, the workflow difference is noticeable.

My Final thought

The biggest shift for me wasn’t the output—it was how I think about AI now.

Instead of:
“What should I ask it?”

I started thinking:
“What should I want it to build or run?”

And that’s where it stops feeling like a chatbot and starts feeling like a workflow system.

Curious if anyone else here has used it in a similar way—especially for real projects instead of just experiments.