- Is it really worth considering Hy3 for my company, or is it just for big tech?
- What is MoE architecture and why does it change the math?
- Do Hy3's numbers hold up to an honest comparison?
- What is happening to enterprise AI costs: has anything really changed?
- Is the Italian AI market really ready, or are we still behind?
- Where can an Italian company concretely apply Hy3?
- What open-source tools exist and how do they compare to Hy3?
- What risks and constraints do I need to know before adopting Hy3?
- How to get started concretely: which steps to follow to evaluate and adopt Hy3?
- What are the most common mistakes made by those approaching an open-source model like Hy3?
- How to tell if Hy3 is really working for my company?
- So, is Hy3 worth it for my company?
- How we see it at SHM
- Frequently Asked Questions
- Hy3 uses the MoE (Mixture-of-Experts) architecture: 295 billion total parameters, only 21 billion active per operation, with the computational cost of a medium-sized model.
- The hallucination rate claimed by Tencent is 5.4%, halved compared to comparable benchmark models: a concrete advantage for those who produce content or analysis.
- The code is open-source under the Apache 2.0 license: companies can run it on their own servers without sending data to third parties, a key point for compliance and GDPR.
- The Italian AI market reached 1.8 billion euros in 2025 (+50%), but 76% of SMEs have not invested yet: Hy3 lowers the barrier to entry right as the gap widens.
Updated October 12, 2026
A 295-billion parameter model that costs like a 21-billion one: Tencent released Hy3 on July 6, 2026, cutting the hallucination rate in half compared to comparable competitors. For Italian SMEs looking at AI with an eye on the budget, it's time to figure out what really changes.
Is it really worth considering Hy3 for my company, or is it just for big tech?
Hy3 is also relevant for Italian SMEs: the usage cost is that of a medium model, not an enterprise system, and the open code allows it to be installed on your own servers. The right question is not 'can I afford it?' but 'do I have the skills to manage it?'
| Your situation | Is it worth considering? | why |
|---|---|---|
| SMEs with internal technical team or IT partner | Yes | Low inference cost, data stays on-premise |
| Company with GDPR constraints or sensitive data | Yes | No data is transmitted via external APIs |
| E-commerce with high content volume | Yes | High-volume text production with reduced hallucinations |
| Professional firm without internal IT | Evaluate carefully | Need a technical partner for deployment |
| SMEs without budget for GPUs or dedicated cloud | No, for now | Hardware infrastructure remains a real hurdle |
The theme is not just technical. In the cluster AI and process automation we have often talked about how the most advanced companies build hybrid stacks: open-source models for high-volume tasks, enterprise solutions for critical processes. Hy3 fits exactly into this scheme.
What is MoE architecture and why does it change the math?
MoE (Mixture-of-Experts) is a system where the model activates only a part of its components for each individual operation. The result: performance of a large model, cost of a medium model.
Imagine an office with 100 specialists. To answer a marketing question, you don't call all 100: you involve 7 or 8, those competent on that topic. The cost is that of 7-8 people, not 100. Hy3 works like this.
The model has 295 billion total parameters, but activates only 21 billion in each operation. This mechanism is called sparse activation (sparse activation). A useful comparison: in the Mixtral-8x7B model, analyzed by PatSnap in an analysis of over 30 MoE patents , each token accesses 47 billion total parameters but only 13 billion participate in the actual calculation, with a 3.6x reduction in active compute.
Google applies it in Gemini models, Meta has explored it in some LLaMA variants. Tencent brings this architecture into open-source with unprecedented scale and transparency, as documented by The Decoder .
Do Hy3's numbers hold up to an honest comparison?
Tencent claims that Hy3 matches models two to five times its size in active parameters. The most verifiable figure is the hallucination rate: 5.4% compared to 12.5% for comparable reference models, according to statements made at launch.
The hallucination rate it measures how often an AI model invents false information and presents it as true. For those who produce marketing content, product descriptions, or reports, a model that often hallucinates is a real problem: it generates unreliable texts that require constant manual review.
According to data published by Tencent Hunyuan at model launch , Hy3 records a rate of 5.4%, halved compared to comparable models which stood at 12.5%. It's a significant improvement, but the data is declared by Tencent itself: independent validation by the academic community was still ongoing at the time of release. It's worth waiting for external reviews before making binding strategic decisions.
A second element to consider is the Italian language. As with many models of Chinese origin, performance in Italian may be lower compared to English or Chinese. This should be tested empirically before integrating Hy3 into a content production workflow for the Italian market.
What is happening to enterprise AI costs: has anything really changed?
Yes: average costs per AI token in the enterprise sector have dropped by 67% in one year. Open source and multi-model routing are the two main drivers of this reduction.
According to an analysis published by Open Source For You in May 2026 , the average cost per million tokens dropped from $18.40 in the first quarter of 2025 to $6.07 in the first quarter of 2026. A 67% drop in twelve months.
The multi-model routing (routing between multiple models) is the practice of automatically using the cheapest model suited for each single task: for simple questions a lightweight model is used, for complex ones you scale up. Hy3, with its cost-performance profile, lends itself well to this kind of architecture.
This trend makes the adoption of open-source models like Hy3 even more interesting for Italian SMEs that have held off until now for budget reasons. The comparison with other quality open-source models is explored in depth in the article on Mistral AI as an open-source alternative to OpenAI .
Is the Italian AI market really ready, or are we still behind?
The market is growing fast (1.8 billion euros in 2025, +50% compared to the previous year), but the gap between large companies and SMEs is huge: 71% of large companies have already started AI projects, against just 8% of small and medium-sized ones.
According to Artificial Intelligence Observatory of the Polytechnic University of Milan , the Italian AI market reached 1.8 billion euros in 2025. Growth is real, but distributed very unevenly.
The data of Digital Innovation Observatory in SMEs by the same Polytechnic show that 76% of Italian SMEs have not invested in AI and do not plan to do so. Only 7% have launched structured training programs for their employees. The gap is not just technological: it is cultural and organizational.
A positive sign: according to the data ISTAT on Italian SMEs updated to 2026 , AI adoption in small and medium-sized enterprises has more than tripled in two years, going from 5% in 2023 to 16.4% in 2025. The direction is right, but the pace is still slow compared to large companies.
Where can an Italian company concretely apply Hy3?
The most immediate areas are content creation, marketing data analysis, and internal chatbots. The starting point is always the specific use case, not the model.
Three illustrative scenarios to understand where Hy3 can create concrete value. The numbers are calculation examples, not real customer data.
| Company type | Use case | Main advantage | What is needed |
|---|---|---|---|
| E-commerce (e.g., 5,000 SKUs) | SEO-optimized product sheet generation | Fewer hallucinations = fewer manual revisions | Cloud or on-premise GPU, prompt engineering |
| B2B manufacturing company | Analysis and synthesis of technical reports and offers | Sensitive data remains on-premise | Server with adequate GPU, technical partner |
| Professional firm (e.g., consulting) | Internal assistant for research and drafts | No external APIs, total control | Dedicated cloud infrastructure, team training |
| Store network / retail | Campaign customization and creative variations | High volume, low cost per token | Integration with CRM and ad platforms |
For those managing Google Ads campaigns or LinkedIn activities, Hy3 can support the generation and automated testing of high-volume creative variants. To dive deeper into how Chinese open-source models are changing the options available for Italian businesses, read the analysis on Chinese open-weight AI models and their impact on businesses .
Those looking to deploy an AI assistant for the marketing team without depending on external APIs will find Hy3 a credible candidate. The reduced inference cost lowers the barrier to entry compared to traditional enterprise solutions.
What open-source tools exist and how do they compare to Hy3?
Hy3 is not the only quality open-source model: the market offers alternatives with different profiles for language, cost, and ease of deployment. The choice depends on the use case and available infrastructure.
| Model | Active parameters | License | Strength | Main limitation |
|---|---|---|---|---|
| Tencent Hy3 | 21B (out of 295B) | Apache 2.0 | Low hallucinations, MoE scale | Italian to test, GPU required |
| Mistral Large | ~70B | Mistral Research | Excellent in Italian, simple API | Less on-premise control |
| Meta LLaMA 3.1 | 8B / 70B / 405B | Meta Community | Large ecosystem, active community | 405B requires heavy infrastructure |
| DeepSeek V3 | ~37B active | MIT | Very low cost, strong benchmarks | Chinese origin, governance to be evaluated |
| Mixtral-8x7B | 13B active (out of 47B) | Apache 2.0 | Mature MoE, well-documented | Performance inferior to the latest models |
The choice between open-source and proprietary is never just technical: it's also organizational. An open-source model requires internal expertise or an external partner for ongoing management. Proprietary APIs offer automatic updates but less control over data. For an in-depth comparison of how companies are stopping 'renting' artificial intelligence, the article at Open-source AI and on-premise proprietary models offers a complete picture.
What risks and constraints do I need to know before adopting Hy3?
The main risks are three: hardware infrastructure, still partial independent benchmark verification, and ongoing model management. None are insurmountable, but they need to be planned.
Even with just 21 billion active parameters, local deployment requires adequate GPUs. For many Italian SMEs, this means a dedicated cloud investment or a specialized technical partner. It is not a prohibitive cost, but it must be factored in before starting.
| Risk | Impact | How to manage it |
|---|---|---|
| Insufficient GPU infrastructure | High | Dedicated Cloud GPU (e.g., AWS, Azure, OVH) or technical partner |
| Benchmarks not yet independently validated | Medium | Wait for external academic reviews; test on your own use case |
| Performance in Italian not guaranteed | Medium-high for IT content | Empirical testing on real samples before go-live |
| Maintenance and updates | Medium | Define a management plan with your technical partner |
| European AI governance and regulation | Medium | Verify compliance with AI Act and GDPR before deployment |
| Technological dependence on the Chinese ecosystem | To be evaluated | Consider geopolitical and digital supply chain implications |
There's also a reading that goes beyond the technical. Tencent's open-source release of Hy3 is part of a broader strategy to position the Chinese AI ecosystem in the global market. Making a competitive model available without licensing costs lowers adoption barriers for developers and companies worldwide, accelerating the spread of Chinese-origin standards and architectures. For Italian decision-makers, this means more choice and more complexity: evaluating not only technical performance but also the implications of technological dependence and alignment with European AI regulations.
How to get started concretely: which steps to follow to evaluate and adopt Hy3?
The path is divided into six steps: from use case definition to production monitoring. Each step has a clear owner and a measurable output.
- 1 · Marketing manager / Management Define the specific use case Identify a high-volume, low-sensitivity process: text generation, classification, report summarization. Don't start with the model, start with the problem.
- 2 · Internal IT or technical partner Evaluate the available infrastructure Check if you have adequate on-premise GPUs or if a dedicated cloud is needed. Estimate the monthly cost before proceeding.
- 3 · Technical Team Test Hy3 on real Italian samples Run a test on 50-100 examples of your specific use case. Measure output quality, speed, and error rate.
- 4 · Marketing + IT Compare with alternatives (Mistral, LLaMA, DeepSeek) Don't just pick the first model that works. Comparing 2-3 options takes little time and can make a big difference on the final cost.
- 5 · Management / Legal Verify GDPR and AI Act compliance Even an on-premise model requires an impact assessment if it handles personal data. Involve the DPO or a legal consultant.
- 6 · Operations Team Go into production with active monitoring Define clear KPIs (output quality, time saved, cost per task) and monitor weekly for the first 4-6 weeks.
What are the most common mistakes made by those approaching an open-source model like Hy3?
The most common mistake is starting from excitement about the model rather than the problem to solve. The second is underestimating ongoing management costs.
- Choose the model before the use case. 'Hy3 is powerful, let's find something to do with it' is the fastest way to waste budget and time.
- Ignore hidden costs. The model is free, but GPUs, cloud, maintenance, and team training are not.
- Trust only manufacturer benchmarks. Tencent's data on Hy3 is promising, but independent validation is still partial. Test it on your actual use case.
- Skip the Italian test. Performance in Italian can differ significantly from English. Testing on real samples is mandatory before go-live.
- Forget governance. An AI model that produces content or analysis for the company falls within the scope of the European AI Act. Ignoring it is not an option.
- Do not train the team. According to data from the Polytechnic University of Milan, only 7% of Italian SMEs have started structured AI training programs. Without training, even the best model produces mediocre results.
How to tell if Hy3 is really working for my company?
You need measurable indicators, not impressions. The KPIs to monitor are four: output quality, speed, cost per task, and manual review rate.
| Indicator | How to measure it | Alert threshold |
|---|---|---|
| output quality | Human evaluation on sample (scale 1-5) | Average below 3.5 out of 5 → revise the prompt or model |
| Manual review rate | % of outputs requiring significant correction | Over 30% → the model is not suited for the use case |
| Cost per task | GPU/cloud cost divided by the number of outputs generated | Compare with the cost of equivalent human labor |
| Processing speed | Average time per task (seconds/minutes) | Benchmarks against the previous manual process |
| GDPR Compliance | Quarterly audit with DPO or legal consultant | Zero data breach or non-compliance incidents |
So, is Hy3 worth it for my company?
It is worth it if you have a high-volume use case (content, analysis, classification), an internal technical team or a reliable partner, and you want to keep data on-premise for compliance or GDPR reasons.
A middle ground you are an SME without internal IT but with a trusted technical partner: you can start with a limited pilot project, measure the results, and decide whether to scale. The entry cost has dropped enough to make testing sustainable.
Not yet if you haven't identified a specific use case yet, you don't have adequate infrastructure, and you can't count on internal or external technical skills. In this case, a managed proprietary API is still the safest choice.
How we see it at SHM
For us, AI should be integrated to free companies from routine, not to chase the latest trend. Hy3 is a real step forward: it lowers computational cost, reduces hallucinations, and returns control over data. But technology alone doesn't create value. The real differentiator is the ability to integrate these tools into concrete, measurable, and scalable workflows.
Let's consider a B2B manufacturing company that wants to automate the production of technical datasheets in Italian for the European market. The right model isn't necessarily the biggest or the most famous: it's the one that works for its specific use case, in its language, with its infrastructure. This is exactly the kind of evaluation we do with our clients: from choosing the model to designing the workflow, to monitoring the results.
The Italian AI market is accelerating, as shown by data from AI Observatory of the Politecnico di Milano . The gap between large enterprises and SMEs is widening. Those who still wait risk starting from a structural disadvantage. Also the article on Meta Glimmer and Zuckerberg's AI vision shows how the open-weight landscape is rapidly reshaping.
If you are evaluating how to integrate AI into your processes, you can explore our service for strategic AI integration , the path of AI training for businesses or the service of Data processing and business intelligence . For a direct comparison, the team is available from contact page .
Frequently Asked Questions
MoE (Mixture-of-Experts) is a system where the model activates only a part of its components for each single operation. Hy3 has 295 billion total parameters but only uses 21 billion for each calculation: the computational cost is that of an average model, not a giant. This translates into lower infrastructure costs and faster response times.
It is not guaranteed. As with many models of Chinese origin, performance is primarily optimized for English and Chinese. Before using it in production for Italian content, empirical testing on real samples of your specific use case is essential. Results can vary significantly depending on the type of text.
Yes. The Apache 2.0 license allows you to download the model and install it on your own servers (on-premise) or on a dedicated cloud. In this case, data does not transit through Tencent's infrastructure. Compliance with the European GDPR and AI Act, which apply regardless of the model's origin, remains to be evaluated.
The model is free (open-source), but the real costs are GPU infrastructure (cloud or on-premise), potential technical partner for deployment, and ongoing maintenance. Average enterprise AI token costs have dropped by 67% in a year, but a precise estimate depends on usage volume and the chosen architecture. A pilot test on dedicated GPU cloud is the fastest way to get a real number.
The data declared by Tencent (5.4% hallucination rate, performance equivalent to models 2-5 times larger) are promising but come from the manufacturer itself. At the time of launch, independent validation by the academic community was still ongoing. The recommendation is to test Hy3 on your real use case before making binding strategic decisions.
That's a legitimate question. The open-source release of Hy3 is part of a broader strategy to position the Chinese AI ecosystem. With the Apache 2.0 license and on-premise deployment, operational dependence on Tencent is minimal. The implications for data governance, alignment with European AI regulations, and the long-term choice of Chinese-origin technological standards remain to be evaluated.
The right model isn't the biggest one: it's the one that solves your specific problem, in your language, with your infrastructure.
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