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It was a little over a month since the Chinese Ai Stersup Sugtsup, an offshoot of Hong Kong-based Hong Kong-based capital handling, released Latest version of Hit Open Sure Studenter Depeek, R1-0528.
As for its first, Depeek-R1 – that Stabbed AI and global business communities How it is trained and how well tasks do well, all who apply to developers and withdrawing AI labs
This week, the 24-year-old German company Gmbh technology consulting technology issued by one Such adaptation: Depseek-tng r1t2 chimaraThe most recent family model is more language language (LLM) family. R1T2 provided a great enthusiasm for recovery and speed, score above the 90% of intelligence points in R1-0528while producing answers Under 40% of the output count of R1-05528.
That means it makes more than the most greater answers, translated directly to faster infesting and lower computing costs. At the Model Card TNG issued for new R1T2 at AI Code Sharing Community Hugging Face, this “about 20% louder in R1-0528” (the official update from Dereseeek).
That is, the answer is possible positively from the AI community developing. “Damn! Deresek R1T2 – 200% faster than R1-0528 & 20% faster than R1,” wrote Vaibhav (VB) Srivasav, a senior leader in face-to-face, of X. “It’s important to be better than R1 in GPQA & ASEL 24, made by assembly to experts with DS V3, R1-0528 – and use it messy.
This profit has been done through Thin Assembly-of-Experts Method (AOE) method – a technique for LLMS construction (internal weight parameters characterized by TNG Paper published in May In Arxiv, non-peer checked open access to online journal.
An heir to the original Chimera, R1T2 introduced a new “trizzle” suppression with three parent models: Dereseeek-R1-0528, derekeek-r1, and derekehek-v3-0324. The result is an engineered model to maintain high competency ability while significant reduction in inforce cost.
R1T2 was built without further tuning or repeating. This has inherited strong reasoning on R1-0528, structural standards of R1, and research, behavior checked by the V3-0324 – available model for business use and research.
How are Assembly-of-Experts (AOE) different from mixed experts (MOE)
The mixed experts (MOE) is an architectural design in which different substances, or “experts, are” activated in the condition. In Moe LLMS such as Dereseek-V3 or mixtral, only a subset of modeling model experts (eg, 8 out of 256) active in pass. It allows several models to achieve higher number of parameter and specialist while maintaining the costs of wasting management – because only one part of the network is checked on each sign.
Assemblies-of-expert (AOE) a model combining technique, not an architecture. It is used to create a new model from several pre-trained Moe models by selecting their weight tenmor.
The “experts” of AOE refers to the ingredient models together – usually the expert routes in Moe layers – not experts who are somewhat activated on runtime.
TNG’s implementation of AOE primarily preceding expert experts – the part of a model most responsible for specialists and attention medications from faster models like V3-0324. This procedure enables the resulting police models to inherit the strong reasoning that does not waste noise or latency with the most powerful parental models.
Performance and speed: what benchmarks are shown
According to the contrasts of the benchmark presented by TNG, R1T2 reached between 90% and 92% In its rationalization of its most intelligent parent, DEPSESSEEK-R1-0528, as measured in ASEL-24, ASE-25, and GPQON-DIAMOND TRY.
However, unlike defsheek-r1-0528 – with probability long, detailed answers due to thoughtful rationalization – R1T2 is designed to shorter. It is delivered to similar intelligent answers while using fewer words.
Than to focus on time processing raw or markers-per-seconds, the steps “speed” in terms of Output token Cox per Answer – a practical proxy for the same cost and omission. According to benchmarks TNG shared, R1T2 has provided answers used approximately 40% of tokens required at R1-0528.
That interpreted a 60% reduction in output lengththat directly reduces the time of drainage and compute load, accelerate the answers to 2x, or 200%.
Compared to the original DeepSec-R1, R1T2 is also around 20% lower on averageoffers significant recovery wins for high deployment above or cost sensitive.
This effective does not come at the cost of intelligence. As shown in the benchmark chart presented by Technical Paper in TNG, R1T2 sits in a desirable zone of Intelligence vs Curve Curve. It preserves the quality of reasoning while shortening the noise – a result of critical business applications where the intensification speed, and costs all things.
Considerations of deployment and arrival
R1T2 is released under a stable license of myth and is currently available to deal with face, which means it’s open source and is available to use and constructed in commercial applications.
TNG says that while the model is good for general logic tasks, it is not recommended to use cases obtained in function calling or using its line. This can be discussed in future updates.
The company also advised European users to assess adhering to EU AI Act, with an impact on August 2, 2025.
Business operating businesses should review relevant provisions or assume that withdrawal of model use after the date if the requirements cannot be fulfilled.
However US companies operate in handling US-based users, or other countries, not Subject to the terms of the EU AI Act, which should give them a lot of flexibility to use and deploy this free, easy open source modeling model. If they serve with EU users, some EU ACT provisions will still apply.
Tng has made the first chimera variants available on platforms such as Openerouter and Chutes, where they reported billions of tokens daily. R1T2 release represents further evolution of this public effort.
About the GMBH Consultation Technology
Established in January 2001, TGNG TECHNOLOGY GMBH Consultation Technology Based on Bavaria, Germany, and uses 900 people, with high concentration of PhDS and technical specialist.
The company focuses on software development, artificial intelligence, and Cloud Devops / Service, serving business clients like telecommunications such as telecommunications, telecommunications, insurance, e-commictments.
TNG works as a consultation-based amount. Its unique structure, built on the principles of research and self-management, supporting a culture of technical innovation.
It actively contributes to open source of communities and research, shown by public release such as R1T2 and the publication of its experiences.
What does business traders mean for business
For CTOS platform owners, engineering leads, and retrieval teams, R1T2 indicates non-benefit benefits and strategic options:
- Low Inference Costs: In fewer output signs per assignment, R1T2 reduces GPU time and energy-free, directly to infrastructure storage – more important to infrastructure environment or in real environments.
- High quality of reason without overhead: It preserves most of the model rational models of top-tier models such as R1-0528, but without their long air. It is good for structured tasks (math, programming, logic) where letters like short answers.
- Open and modificable: The MIT license allows thorough restraint control and adaptation, obtain private hosting, model alignment, or additional training of regulates or bounds.
- Emerges in modularity: Approaching AoE suggests a future where models are built steady, allowing businesses to congregate specializing variants by complaining from the beginning.
- Cave: Businesses that depend on calling, using the tool, or advanced agent orchestra should find limits now, even if future updates to chimera can answer these gaps.
TNG is encouraged to researchers, developers, and business users check the model, test its behavior, and provide feedback. R1T2 chimera is available to Huggingface.co/tngtech/deepseec-tng-r1t2-chimeraand technical questions can be assigned to [email protected].
For technical background and benchmark approach, TNG research role is available in Arxiv: 2506.14794.
