Three browser tabs, the same question pasted into each, and a lot of alt-tabbing to keep the answers straight. That is a normal way to compare AI models, and plenty of people stack free chatbots so they never pay for one.

Msty Studio does the same job in one window. It runs several models against a single prompt, lines the answers up next to each other, and the free tier covers everything I needed. The comparison changed less about which answers I get and more about how far I trust them.

Msty Studio puts every model in one window

Each pane keeps its own context, and that matters

Msty Studio is a desktop workspace from CloudStack that reaches local models and cloud providers through a single interface. It runs on Windows, macOS, and Linux, and it does not require an account before you start.

I skipped the local model it offers during setup, since small models on a mid-range laptop are slow enough to be irritating. An OpenRouter key went in instead, which puts dozens of providers behind one credential. There are other free tools that run AI on your PC without a subscription, and several run a model beautifully. Msty Studio is better at comparing them.

I added three models from three different labs, namely Google Gemma 4 31B, NVIDIA Nemotron 3 Ultra, and Z.ai GLM 5.2. Split Chats gives each one its own pane and its own conversation history, so every pane answers independently without seeing what the others produced.

That independence is not a detail. I first ran my tests down a single thread, and one model’s reasoning panel said plainly that it was choosing a different answer than last time for the sake of variety. It was avoiding repetition rather than disagreeing, and I would have written that up as a real split.

Where three models agree, and where they split

The disagreement is the part worth your attention

So I gave all three the same question.

All three said the same thing about what to give up. Sacrifice the dedicated graphics card, the premium metal chassis, the high-refresh display. Three models from three labs, one answer, and that kind of agreement is a fair signal the question is settled.

Then they split on memory. GLM and Gemma both called for 16GB. Nemotron said 8GB was enough and went further than either, naming a 13-inch to 14-inch ultraportable with a Core i5 or Ryzen 5, a 256GB SSD, and a 1080p IPS panel, which it reckoned fits $600.

That gap matters at this budget, since the laptop Nemotron described is one you can actually go and find.

Free ChatGPT handles most questions perfectly well, and it has picked up features that used to sit behind a subscription. The limitation is structural rather than a matter of quality. One model in one thread cannot tell you that another model would have answered differently.

The thought panels show the route taken

One model changed its mind before answering

The reasoning panels turned out to be more interesting than the answers. GLM drafted its response around 8GB of RAM, then stopped itself. Its panel notes that 8GB is a little low for photo editing, and it revised the figure to 16GB before committing.

Nothing in the finished answer hints that any of that happened. Read the panel, and you learn the 16GB figure was a second thought rather than a first instinct, which is worth knowing when another model landed somewhere else entirely.

The panels also show how differently these models work. GLM spent 14 seconds on a numbered breakdown of constraints and priorities. Nemotron pondered briefly and sketched three sentences. Gemma exposed no reasoning at all and answered at 19.90 tokens per second against GLM’s 56.85.

Knowledge Stacks and the free tier’s real edges

Most of what you want is here, and some firmly is not

Knowledge Stacks handle retrieval. Add documents, folders, or notes, choose an embedding model, and the models answer from your material rather than from training data alone. Running one document past three models surfaces different emphases from a source you control, which is where a private AI setup anyone can run starts making more sense.

Prompt Studio matters more than it appears to. Saving a comparison prompt once means every later test runs on identical wording, and identical wording is the only reason the answers stay comparable at all. Both sit on the free plan, alongside chat with local and online models, Split Chats, Agent Mode, and the Persona, Skill, and Media Studios.

Aurum costs $149 a year, or $349 once, and it adds Msty Studio Web, Turnstiles, Forge Mode, Insights, and Live Contexts. Free models bring limits of their own. OpenRouter caps them at 20 requests a minute and 50 a day until you buy credits, and providers can withdraw free capacity without notice.

Gemma failed three times before I paid a fraction of a cent to reach the same model another way. Models also change quietly behind the scenes, which is one more argument for keeping more than one in view.

I ask questions differently now

One prompt, three answers, and a reason to trust one

What I want next from this workspace is a standing set of prompts I run whenever a decision actually costs something, pointed at a Knowledge Stack of my own reference material instead of the open web. That is a weekend of setup, and it would sharpen every comparison after it.

For anything where being wrong wastes your time or your money, sending one prompt to three models and reading where they disagree is worth the few minutes it takes to set up. For looking up a command you half remember, one chatbot is still the faster answer.

OS

Windows, macOS, and Linux

Developer(s)

Msty AI

Price model

Free

Chat with models, organize knowledge, create media, and reuse prompts in one privacy-first workspace.