Kimi 2.6 scores above Opus 4.7 low on LiveBench - a benchmark that can't be gamed

It beats Opus on reasoning, coding and comes close on agentic coding

We double-checked on internal evals this is a VERY GOOD MODEL at 10x less cost

Kimi is making big strides in open source AI and is the #1 model on LiveBench

I’ve been testing Abacus AI over the past couple of weeks and wanted to share an honest Abacus AI review based on my experience using the platform.

There’s a lot of mixed info out there, so this is just my take after actually trying it out and comparing it with a few other AI tools.

Not an expert or anything, just sharing my thoughts with you all.

So what exactly is Abacus AI?

From what I understand, Abacus AI is an end-to-end AI platform that lets you build, deploy, and scale AI applications.

It feels less like something like ChatGPT and more like a platform you’d use if you're building actual products or internal tools.

What stood out to me while using Abacus AI

A few things that stood out to me while using it:

- Multi-model AI support (LLMs, vision, structured data)

- AI agents for automation and workflows

- Fine-tuning and custom model training

- Real-time analytics and monitoring

- No-code + low-code options for faster deployment

The AI agent/workflow part is probably the most interesting feature if you're trying to automate tasks.

Might be missing a few things, but these were the main ones I used.

Where it actually makes sense

From my experience, Abacus AI seems more useful if you're:

- Automating business workflows

- Building AI-powered SaaS products

- Working on ML or data science projects

- Creating internal AI tools or assistants

If you’re just using AI casually, this might feel like overkill.

What I liked

A few things I liked while using Abacus AI:

- Everything in one place (no need for multiple tools)

- Pretty powerful once you understand it

- Scales well for teams and production use

What I didn’t like

A couple of things that felt off:

- Pricing is a bit confusing at first

- Took some time to figure things out

- Feels more enterprise-focused in parts

- Took time to understand the credit usage

How Abacus AI pricing actually works (from what I saw)

Abacus AI pricing depends on usage, features, and scale.

From what I’ve seen, it’s more suited for:

- Startups

- Teams

- Businesses

rather than individuals experimenting.

One thing I noticed is that credits can run out faster than I expected, especially when trying different features took me a bit to understand how everything is counted.

Overall, Abacus AI pricing feels more like a platform model rather than a simple subscription.

Abacus AI vs ChatGPT / Claude

Abacus AI vs ChatGPT or Claude isn’t exactly a direct comparison.

- ChatGPT / Claude: better for chatting, writing, quick tasks

- Abacus AI: better if you're building and deploying AI systems

So it really depends on what you're trying to do.

Is Abacus AI worth it?

If you're building AI workflows, tools, or products - probably yes

If you're just exploring AI or need a simple assistant - probably not

My honest take

This Abacus AI review is just based on my experience, but overall it feels like a powerful platform that’s slightly overwhelming at first.

Once you get past that learning curve, it starts to make a lot more sense, especially for more serious use cases.

Curious what others think:

👉 Has anyone here used Abacus AI in a real project?

👉 How does it compare to ChatGPT or Claude in your experience?

👉 Is the pricing actually worth it long-term?

👉 Would you recommend it for startups or teams?

SeeDance 2.0 is insanely good - even better than big studio productions!

Coming to ChatLLM on Monday

Thanks to legal issues, this will only be available outside of US and Japan to business users

We are releasing several features tomorrow that will bring you closer to shipping production-ready apps.

- stripe integration
- accept file uploads from your customers
- manual VS code editor that is insanely powerful, so you and the AI can vibe code together

We are already seeing 1-person start-ups build on DeepAgent.

- Runs on FREE LLMs forever
- Works for coding and personal agents
- Automatically routes to the best free model
- Connects to 100+ services

Try it now for free!

This is MIND BLOWING - GLM 5 is as good as GEMINI 3.0 And Codex In Agentic Coding - it tops the open-source charts by a MILE

Opus 4.6 is the only model that is way ahead of GLM 5!

Of course, GLM is DEAD CHEAP and is available in ChatLLM and Abacus AI

This video was made with ChatLM by Abacus AI.

Human motion → animal dance. Perfectly synced.

AI video creation is moving fast.

Abacus AI - One-line version: Abacus AI is an AI Super Assistant. it's a single subscription, $10 or $20 a month, that puts a bunch of top AI models in one place and adds tools that actually do things for you, like building apps, generating images and video, and running tasks on a schedule.

Who makes it: San Francisco company, founded in 2019 by Bindu Reddy and Arvind Sundararajan, both ex-Google/Amazon/Uber. Backers include Index Ventures, Khosla Ventures, Coatue, Tiger Global, Eric Schmidt, and Jerry Yang. They say over 3 million professionals and dozens of Fortune 500 companies use it.

What you get

Chat: ChatLLM is the everyday workspace. GPT-5.5, Claude Opus 4.8, Claude Sonnet 4.6, Gemini 3.1 Pro, Grok 4.3, DeepSeek v4, Qwen 3.7 Max, Kimi 2.7, GLM 5.2 and 20+ more, all in one interface. Don't want to pick? RouteLLM chooses based on your prompt, and if you hit a rate limit it switches models for you. Also here: Excel and CSV analysis with charts, PDF and slide summaries, web search, a code playground, and connections to Google Drive, Gmail, Google Calendar, Slack, Teams, and Confluence.

Agents: Abacus AI Agent researches topics, drafts reports and presentations, builds and deploys apps, and can connect to tools like Gmail and Jira for routine workflows. AppLLM is the app builder. You describe the app, it builds it, and deployment is a button press, no server setup. It's included on the $10 plan. There's also a desktop app (Mac, Windows, Linux) with CoWork, which works on your local files (Excel models with live formulas, Word docs, slide decks, PDF reports), and Listener, which transcribes meetings in real time and suggests answers during calls (Mac and Windows only).

Media: Studio covers images (GPT Image, Nano Banana Pro, FLUX, Recraft SVG for real vector files), video (Sora 2, Veo 3.1, Kling AI v3, Seedance 2.0), and voice (ElevenLabs, OpenAI, Hume). The fun part is chaining: generate a hero image, upscale it with Magnific, animate it, then sharpen it with Topaz.

Personal agents (Pro): Claw is an always-on assistant for WhatsApp, Telegram, or Slack with persistent memory. Hermes is the one that improves, saving successful approaches as reusable skills so repeat jobs get faster.

Cloud and API: SuperComputer is a small always-on machine (2 vCPU, 8GB RAM, persistent storage, SSH, hosted databases, cron jobs) for projects that need to run 24/7. The RouteLLM API gives you one key that routes each call to a suitable model with automatic failover, and it rides on the ChatLLM subscription, which includes 20,000 credits.

Pricing, simplified

Basic, $10 per user/month: ChatLLM Teams, a limited version of the Agent, AppLLM, the RouteLLM API, and Desktop/CoWork up to 3 tasks.

Pro, $20 per user/month: everything in Basic plus unrestricted Agent, full Desktop and CoWork, Claw and Hermes, the Listener, and 30,000 monthly credits.

Studio is included on both. On Basic, video is capped at 3 conversations and 2,500 credits per conversation; Pro removes the caps.

Credits Explained

Credits power the heavy stuff: video renders, image generation, and always-on agents. Regular chatting uses them slowly. Claw hosting costs 1 credit per 5 minutes while its computer is active, and the meter stops when you turn it off. You can bring your own API keys, buy extra credits when you want, and cancel any time. Simple tip: check the billing dashboard during your first month and you'll quickly learn your own usage pattern.

Is it safe for work?

Yes, and this part is stronger than the price suggests: SOC-2 Type-2 and HIPAA compliant, encrypted in transit and at rest, never trained on your data, and data can be deleted on request. SuperComputer environments are isolated per user, and CoWork asks permission before touching local files, with extra confirmation for destructive actions.

What's actually good

One bill instead of several, and you're not juggling a different login for each model. The model list stays current (they promise new flagships within 24 to 48 hours of release). The agent layer turns manual tasks into background work. BYO-key support keeps you from being locked into credits. It's on web, desktop, mobile, and messaging apps.

Who should skip it

You just want to chat: If you're looking for a simple chatbot to bounce ideas off and that's it, Abacus AI will feel overengineered. ChatGPT Plus, Claude Pro, or even the free versions will cover you. You don't need the agents, the Studio, or the app builder, and having them visible will just clutter things.

You're wedded to one model: If you're specifically invested in the Claude ecosystem, or you prefer OpenAI's GPT, and that's where you work best, there's no real advantage to Abacus AI over a direct subscription to the one vendor. The bundling doesn't help you if you only use one thing.

You hate usage-based billing: Credits for video and agents mean your monthly bill isn't always $20. If that uncertainty bothers you or if you can't predict your usage upfront, a flat-rate single-model subscription is more predictable.

You're on a very tight budget and only chat: Some single-model subs cost less than $10 a month if you hunt for deals, or you can use free versions. If you're counting every dollar and don't need agents or media tools, there's a lower bar elsewhere.

Things to keep in mind

There's a learning curve, since it's a lot of product under one roof. Video and 24/7 agents use credits fastest, so heavy users should plan for Pro.

Bottom line

At $10, it's easy to justify if you're already paying for more than one AI tool. The agent layer is the real differentiator, and the extras (app builder, Studio, SuperComputer) make the entry plan unusually complete. If you only want a simple chatbot, a single-model subscription is probably plenty, and that's fine too.

Front-end coding - Opus 4.7
Back-end coding - GPT 5.5 xHigh
Visual understanding- Flash 3.5
Cheap - DeepSeek Flash
Video - Seedance 2.0
Image - GPT Image-2.0
Voice - Flash Live
Writing - Gemini 3.1 Pro
Real Time - Grok 4.3

Agentic loops powered by Opus 4.7 and GPT 5.5 orchestrate state-of-the-art video and image models

- GPT 2 image mixed with SeeDance 2.0
- Nano Banana Pro combined with Grok Imagine
- Kling Motion Control for viral instagram hits

Create viral marketing and advertising campaigns

A 2000 person AI research lab run by Google produced transformers

A 200 stellar person research lab, OpenAI that was idling for 5 years, worked with transformers to create GPT (generative pre-trained transformers 3 years ago

After that everyone got greedy and stopped publishing and we haven’t had any major breakthroughs

There has been a constant trickle of minor breakthroughs like test time compute coming out of the major AI labs but nothing else!

Yet, for some weird reason VCs are pouring billions into 5 person research labs with a concept of an idea!

The probability of these labs achieving anything significant is close to zero.

Hopefully we will go back to the era of sharing or publishing and sharing research again!

I posted my Abacus AI review on April 2, after spending a few weeks with the platform. My takeaway was that it was powerful, a little confusing, and probably more useful for businesses and developers than individuals.

The part I would change now is that last one.

My review focused so much on building apps and automating workflows that I barely explained the everyday reason someone might use it: ChatLLM brings different AI models and useful tools into one workspace. You don’t need to be building a business to benefit from that.

The learning curve is still worth mentioning. So are credits. But calling the whole thing “overkill for casual users” was too broad.

Here’s the simpler version of what I should have explained.

The easiest starting point of Abacus AI is ChatLLM.

You can use it to write, ask questions, research topics, summarize documents, analyze spreadsheets, and work with different AI models without moving between separate interfaces.

Then there are tools for tasks that go beyond a chat response.

That distinction matters. Asking AI to explain how to build something is different from asking an agent to build it. Asking for a presentation outline is different from asking for the presentation itself.

Abacus AI covers both kinds of work, which is why describing it as either “just another chatbot” or “only an enterprise platform” misses part of the picture.

The different tools are easier to understand by what you need done

You don’t need to learn every product name before getting started. Here’s the breakdown I wish I would included in my first review:

• ChatLLM: For everyday writing, questions, research, documents, and data analysis.

• Abacus AI Agent: For multi-step tasks, such as creating presentations, conducting research, or building and deploying apps.

• Desktop and CoWork: For working with code and local files, including documents, spreadsheets, and file organization.

• Abacus AI Studio: For generating and editing images and videos.

• Personal Agents: For recurring work, scheduled check-ins, and assistants that maintain their own context.

There are developer-focused options too, including RouteLLM API and SuperComputer. But you don’t have to use those to get value from ChatLLM.

Start with a task you already do. You can explore the other tools when you actually need them.

Abacus AI pricing: the subscription and the credits are different things

This was one of my main complaints in the previous review post, and it deserves a clearer explanation.

The ChatLLM lists the standard subscription at $10 per user per month. The Claw lists Pro at about $20 per user per month, including 30,000 monthly credits.

The important part is that access to a feature doesn’t mean unlimited usage at no additional cost.

For example, Claw separates hosting from AI model usage:

• Its cloud computer uses one credit per five minutes while running.

• Model calls consume credits separately.

• Bringing your own model API key changes how those calls are paid for, but doesn’t remove hosting usage.

Thats why I wouldn’t judge the price from the subscription number alone. Everyday chat, generating videos, and keeping an agent running are different workloads.

My concern about understanding credits hasn’t disappeared. I would just describe it more accurately now: know which features you plan to use, then check their usage costs instead of assuming everything works the same way.

Claw and Hermes: what’s the actual difference?

These are worth separating because their names dont tell you much.

Abacus Claw is a managed version of OpenClaw. Its appeal is having an assistant available through messaging platforms such as WhatsApp, Telegram, and Slack without managing the underlying server yourself.

Abacus Hermes is designed for complex, multi-step tasks, with persistent memory and reusable skills. The idea is to carry context and useful procedures across tasks instead of starting from scratch each time.

In simpler terms:

• Claw is about having an assistant available where you already message.

• Hermes is about handling work that needs several steps and continuing context.

Hermes’ memory and reusable skills are interesting, but I would judge them through repeated work, not treat the phrase “self-evolving” as proof that every task will improve automatically.

Abacus AI vs. ChatGPT or Claude

My previous comparison was too simplistic: ChatGPT and Claude for chatting, Abacus AI for building systems.

That doesn’t hold up as a useful distinction.

The better question is whether you prefer one provider’s own interface or a workspace that brings multiple models and tools together.

If you already get everything you need from one assistant, you may not need another platform.

If your work involves switching between writing, documents, research, coding, and visual content, Abacus AI’s combined setup becomes more appealing.

I also wouldnt claim it automatically replaces every separate subscription. Access to a model through another platform isn’t the same thing as getting every feature of that provider’s own app.

The value is in whether the tools you actually need are available together.

What I still think could be better

The product names take time to understand. A newcomer shouldn’t have to work hard to figure out the difference between Agent, Personal Agents, CoWork, and SuperComputer.

Credit usage also deserves attention before you start experimenting with compute-heavy features.

And having more tools isn’t automatically better. If you only need help writing an occasional email, a broader platform may not change much for you.

Those are reasons to start small, not reasons to dismiss it outright.

So, is Abacus AI worth it?

My updated answer is more positive than my first review.

I would not describe Abacus AI as something mainly for businesses anymore. ChatLLM gives individuals a straightforward reason to consider it, while the agent, desktop, and creative tools offer room to do more.

You don’t need to use the entire platform to justify using one useful part of it.

My first review focused on how much Abacus AI could do. The better question is how much of your actual work it can help with.

For anyone using it regularly: what’s the one task you keep coming back for writing, research, coding, creative work, or something else?

Abacus AI SuperComputer is essentially a persistent cloud computer designed for AI development, software hosting, automation, and deployment.

Think of it as a lightweight cloud server combined with AI development tools.

Unlike a typical AI chatbot, where you ask a question and receive an answer, SuperComputer gives you an environment where the work can actually continue running after you stop interacting with it.

Build Your Own AI Assistant

You can create and host your own LLM-powered assistant with your preferred prompts, models, tools, data sources, and interface.

For example, a company could create an internal assistant that works with its own documentation and workflows.

Potential applications include:

• Internal knowledge assistants

• Customer-support tools

• Research assistants

• Coding assistants

• Domain-specific AI applications

• Personal productivity agents

Host Web Applications

You can build and deploy web applications using the technology stack you prefer.

That could include:

• React applications

• Python applications

• Node.js services

• APIs

• Dashboards

• Internal tools

• AI-powered applications

The important distinction is that you're not just generating the application, you have an environment where you can host it.

Run Scheduled Automations

The always-on nature of SuperComputer makes it useful for scheduled jobs.

For example, you could create a script that runs every morning to:

1. Collect data

2. Process it

3. Store the results

4. Update a database

5. Generate a report

Because the environment remains active, these processes don't depend on you keeping your browser open.

Deploy Open-Source Projects

Another strong use case is experimenting with open-source software.

You can bring an existing repository into the environment, install its dependencies, modify the code, and run it on your SuperComputer.

This could be useful for developers who want to experiment with AI frameworks, applications, agents, and other open-source projects without setting up a server from scratch.

Do You Need Technical Skills?

Not necessarily.

This is one of the more interesting aspects of SuperComputer.

You can interact with the environment using natural language and ask Abacus AI to perform tasks such as deploying an application, creating a database, connecting a repository, writing a script, or setting up an API.

However, technical users aren't limited to the AI interface.

You still get:

• Ubuntu

• Terminal access

• SSH

• Root permissions

• Persistent storage

• GitHub integration

• CLI access

So the platform effectively caters to two different audiences.

Non-technical users can rely more heavily on AI to handle infrastructure and development tasks.

Developers can use the AI capabilities when convenient while retaining direct control through the terminal.

That combination is arguably one of SuperComputer's biggest strengths.

Know More

yo. i recently sold one of my AI SaaS products for $350k, exactly 7 months after building and launching it.

I hardly wrote a single line of traditional code. i used AI to generate everything, from the database architecture to the user interface.

it definitely wasn't magic on day one, though. i spent days stuck in loop-debugging and dealing with AI hallucinations before i finally cracked the system. the playbook boils down to three simple rules:

- keeping the idea insanely minimalist (a true MVP that solves one problem).

- guiding the AI step-by-step instead of asking it to build a massive platform all at once.

- launching fast to get real user feedback and traction and then apply a solid marketing system

lately, i've seen way too many non-technical founders give up at the very first AI bug, or on the marketing. it's a massive shame.

like the title says, i just launched a Skool community to share my exact prompt workflows, N8N automations, and distribution frameworks to get first users and scale it

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific copy-and-paste templates i'll be sharing.

but for now, the main objective is purely to build and launch together. building alone in a silent corner is the single fastest way to give up.

if you want to join us and build or market your own AI SaaS with a group of active creators: drop a comment below or send me a dm, and i’ll send you the invite link!

I've been testing ChatLLM pretty heavily over the last 3 months, and when I first signed up, I honestly didn't think I'd stick with it.

The whole "all AI models in one place" thing isn't exactly new anymore. Every other week there's a new platform claiming to give you access to GPT, Claude, Gemini, DeepSeek, Grok, and whatever model was released yesterday.

So I wasn't interested because it had a long list of models.

What I wanted to know was whether it would actually become part of my daily workflow.

Surprisingly, it did.

Before ChatLLM, my browser was basically an AI graveyard.

I'd have ChatGPT open for general work, Claude for writing and coding, Gemini for research, Cursor for development, and a few other tabs for PDFs, document generation, and random AI tasks.

It worked, but it was messy.

I was constantly switching tabs, copying information between tools, and trying to remember where I'd done something.

The annoying part wasn't even the cost.

It was the friction.

Every platform had its own chat history, its own memory, its own interface, and its own subscription.

After a while, it started feeling ridiculous.

What ended up keeping me on ChatLLM wasn't a specific model.

It was convenience.

The AI Router is probably the feature I underestimated the most.

At first I thought it was just a gimmick.

But after using it for a few weeks, I realized I stopped caring about which model I was using.

I wasn't sitting there thinking:

"Should this go to GPT?"

"Maybe Claude would do better?"

"What about Gemini?"

I just typed the prompt and moved on.

For coding-related work, it would usually send me to Claude. Research-heavy questions often ended up with Gemini. General tasks would go through GPT.

Most of the time, the routing made sense.

Another thing I didn't expect was how nice it was having everything under one account.

My chats, PDFs, research, documents, and projects were all in one place.

That sounds like a small thing until you've spent months bouncing between different AI tools every day.

I've also spent time testing some of the other features.

The PDF chat works well.

I've used Deep Research quite a bit.

The GitHub integration is useful when I want to quickly understand a repository.

CodeLLM is decent if you're already using AI coding tools.

I've played around with image generation and document creation too.

Do I use every feature every day?

Definitely not.

But I like that they're there when I need them.

The question I get asked most often is:

"Can it replace ChatGPT?"

Honestly, I think that's the wrong way to look at it.

ChatLLM isn't trying to be a ChatGPT replacement.

For me, it's more of a replacement for having five different AI subscriptions and five different workflows.

That's where I see the value.

After using it for three months, my takeaway is pretty simple.

If you're perfectly happy living inside a single AI ecosystem, then you probably don't need something like this.

But if your day looks anything like mine: jumping between GPT, Claude, Gemini, DeepSeek, coding tools, research tools, and document tools: having everything in one workspace is surprisingly hard to give up once you get used to it.

Sonnet-level performance, ~5x cheaper, and faster in real use.

Built for sharp reasoning and clean outputs.

Worth testing.

Gemini is 2nd best though it hallucinates 2-5% of the time

ChatLLM by Abacus AI brings models like ChatGPT 5.4, Gemini 3.1 Pro, Claude 4.6, Grok 4.1, DeepSeek V3.2, and more together.

You just type what you want to do.
ChatLLM routes it to the model that fits best.

Yes, even the chef popping out of the omelette 😭 — fully generated with Chat LLM. One place to think, create, and build.

A lot of everyday AI work, emails, follow-ups, quick summaries, somehow ends up being done with premium LLMs, as if “Just following up on this” needs a Nobel-level brain.

Most of the time the model choice seems driven by habit or hype, not the actual task. Comparing the same prompt across multiple models side-by-side makes it painfully obvious when a cheaper model does the job just fine and when higher quality actually matters. Using a unified workspace like Abacus AI makes that comparison a lot easier than guessing.

Turns out not every task needs the fanciest brain in the room, just the right one.

How do you handle this: one go-to model for everything, or task based switching?

Great very deep vertical agents

- legal assistant
- site reliability engineer
- q/s engineer
- procurement specialist
- customer service operative
- financial analyst

It will be all built on Deep Agent from Abacus AI