When was the last time you read a headline about Microsoft Copilot? Probably not recently. The media space is wall-to-wall GPT-5, Claude 4.7, Gemini Ultra. Copilot looks like the boring corporate tool that only gets written up on LinkedIn.
Now look at who is actually paying for what.
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Anyone who has spent enough time with ChatGPT or Claude eventually asks the question: can I run something similar right on my own laptop – without a subscription, without data leaving the machine, without depending on someone else’s servers?
In 2026, the answer is yes – but the caveats matter more than the answer.
This article is for people already using cloud LLMs who want to understand what local execution actually gives you, what hardware you need, and where expectations break down. No deep technical dive, but concrete numbers.
In November 2025, Stanford data surfaced an unexpected fact: Americans were using AI more at home than at work. We unpacked that in a mysummit.school piece here – and the takeaway then was blunt: work AI was still catching up to personal AI.
Four months later, Epoch AI teamed up with Ipsos for new research – and the picture has shifted.
You have three data files: an activation funnel, A/B test results, and support tickets. The task – figure out why onboarding is underperforming. You open ChatGPT, upload the first file, ask your question. You get an answer. You upload the second file. ChatGPT asks: “Can you remind me of the context?” You upload the third. The context of the first file has already been pushed out.
Forty minutes later you have three separate conversations, none of which answer the original question. Because the question was one, and the data was in three places.
This isn’t a ChatGPT problem. It’s a problem of approach.
Every team has a ‘good enough’ model. Maybe it’s the one your company approved. Maybe it’s the one that runs locally because legal won’t let data leave the building. Maybe it’s the only option your country’s regulations allow.
In Russia, this is about to become very literal. In March 2026, the Ministry of Digital Development published a draft law on “trusted AI models.” If passed – enforcement is planned from September 2027 – government agencies and critical infrastructure will be limited to models from an approved registry. ChatGPT, Claude, and Gemini, which route data through foreign servers, could be restricted. Russian-made AI assistants would be pre-installed on devices by default.
The problem is that Russian models lag behind. In our study of 54 models, GigaChat scored 3.26 out of 5. GPT-5.4 scored 4.80. That’s a 32% gap.
If GigaChat is all Russian managers have, can smart prompting pull acceptable quality out of it?
In March 2026, Lenny Rachitsky published an article with a telling headline: “Everyone should be using Claude Code”. It went viral on LinkedIn and tech newsletters, picked up hundreds of thousands of views, and now every week a manager somewhere asks: how do I try this?
The answer is uncomfortable. The Anthropic Max subscription that unlocks Claude Code costs $100 per month. That’s $1,200 a year for a single tool – before you’ve even figured out whether it fits your workflow. And it locks you into a single vendor, a single model, and a pricing tier designed for power users, not for someone exploring whether AI agents are worth the investment.
There’s a direct alternative. OpenCode is an open-source project that does exactly the same thing, works with any model (including free ones), and takes 15 minutes to set up.
GigaChat Ultra Thinking takes longer to think and uses more compute. It solves management tasks 3.3% worse than the version without reasoning. This is not a bug or a fluke – it’s a pattern documented in academic papers over the past two years.
This week, Sber unveiled GigaChat Ultra – a new flagship model with a reasoning mode (Thinking). The model is available for free via web, mobile apps, and a Telegram bot. We immediately added both variants to our AI model research for managers: ran them through all 32 scenarios using our unified methodology, scored them with both LLM judges, and compared against the other 52 models.
Twelve hours a week. That’s how much time a typical project manager spends on reports, plan updates, stakeholder correspondence, and risk tracking. Nearly a third of their working time goes not to making decisions, but to documenting them.
With AI, that time drops to three hours. But only under one condition: AI must be embedded in the operational rhythm of work, not used episodically – “when you remember.”
Can an open-source Chinese model compete with the closed flagships from OpenAI and Anthropic? Based on our independent testing – yes. On January 27, 2026, Beijing-based Moonshot AI released Kimi K2.5, and it immediately joined the elite group of the world’s best models – competing directly with GPT-5.4 and Claude Sonnet 4.5. And it’s completely free for basic use.
On February 6, 2026, an anonymous model called “Pony Alpha” appeared on OpenRouter – free, with zero details about its creators. The AI community immediately set about identifying it. Its coding abilities came remarkably close to Claude Opus 4.5. When asked “who are you?”, the model responded: “I am GLM.” But when prompted to write a web page describing itself – it wrote: “I am Claude, created by Anthropic.”