Open models from the USA, large ones from China, and small ones from Europe

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No sign of a summer lull, quite the opposite: reports about new language models have been pouring in over the past few weeks. While China is primarily associated with superlatives regarding model sizes, open American models are currently a bit smaller – at least there are finally some. Another type of superlative from the USA are models that do not adhere to guidelines and hack other websites.

Prof. Christian Winkler

is a data scientist and machine learning architect. He holds a PhD in theoretical physics and has been working in the field of big data and artificial intelligence for 20 years, with a particular focus on scalable systems and intelligent algorithms for mass text processing. He is professor at Nuremberg Institute of Technology since 2022. His research focuses on the optimization of user experience using modern methods. He is the founder of datanizing GmbH, a speaker at conferences and author of articles on machine learning and text analytics.

In Europe, at least a little is happening: Apertus from Switzerland is available in an improved version, and finally, with Soofi, there is also a German model, although it is still experiencing teething problems.

Large and small Chinese models

When the Chinese provider Moonshot announced the new Kimi K3 model, shockwaves almost like with DeepSeek R1 went through the LLM community. Such a large model with 2.8 trillion parameters had not been available with open weights before. Furthermore, Kimi K3 proved to be extremely powerful in many tests and is consistently found in the top ranks of relevant benchmarks like arena.ai or artificialanalysis.ai.

LLMs in your own data center

On October 15, 2026, the online conference “LLMs in Business” will focus on local models: for which applications they are worthwhile, what hardware is required and how to optimize performance.

In addition to its sheer size, Moonshot offers some optimizations that significantly accelerate the model's inference compared to Kimi K2.6. This includes an optimized attention mechanism, which Moonshot calls Kimi Delta Attention (KDA). Here, linear and global attention layers alternate in a 3:1 ratio. This saves a lot of computing time and memory, which is particularly important for long contexts. The Mixture-of-Experts architecture (MoE) is also exciting, as Kimi uses a whopping 896 experts. So many had not been available in open models before.

Fortunately, Moonshot has also converted the weights into the compact MXFP4 format, reducing memory requirements to one-quarter compared to the often-used bfloat16. Nevertheless, even with small contexts, at least 1.5 TByte of RAM are required, which exceeds the budget of many companies. Local execution will therefore only be an option for a few, but it is possible. Moonshot also offers Kimi K3 as a cloud service, with costs that are manageable compared to other models.

Moonshot also used some creative ideas during training to train the many experts evenly. According to the provider, the model has also optimized its kernels, so it can now execute its inference faster.

In our tests, the model performed excellently and provided detailed answers. Only on sensitive political questions did the model strictly follow the Communist Party line.

In early August, Qwen3.8-Max was released, the next large Chinese model. Similar to Kimi K3, Qwen3.8-Max is initially only available via API, but the weights are expected to follow. The new Qwen model is also very large, but has “only” 2.4 trillion parameters.

Alibaba has tasked Qwen3.8 with implementing the oh-my-cli project, a small coding agent framework for the command line. According to its statements, the model worked on it autonomously for almost 16 days, which would be remarkable. The model has also reviewed scientific articles, understood the code used in them, and even optimized it. The article is full of superlatives – what is actually feasible and at what cost needs to be verified by independent tests.

Qwen3.8 Max performs well in benchmarks. Especially in web development, it seems to keep up with the best – including closed – models. In our tests, the model answers competently but was extremely reticent on political questions.

Unfortunately, there are few technical details about Qwen3.8 Max so far. It is likely that the architecture has hardly changed compared to the open Qwen3.6 series. More will only be known after the weights are released. The community eagerly awaits them – not only because of Qwen3.8 Max, but especially because of the simultaneously announced Qwen3.8-27B. The predecessor model Qwen3.6-27B is still the preferred choice for many local users because it offers good performance, especially with Multi-Token Prediction (MTP) (more than 100 tokens/s on an RTX 5090), and is therefore excellently suited for local agent systems.

It is likely that the end of the line for large Chinese models has not yet been reached. The usually reliable Financial Times reports that TikTok's parent company Bytedance is working on a model with 10 trillion parameters. This brings the number of parameters closer to those of the best closed models. Anthropics Mythos 5 is suspected to have eight trillion parameters, and Fable 5 from the same provider has five trillion.

DeepSeek's Flash Model

Significantly smaller than Kimi K3 and Qwen3.8 Max is the DeepSeek model DeepSeek-V4-Flash-0731. Nevertheless, it is exciting because it beats the significantly larger DeepSeek-V4-Pro (Preview) in many benchmarks. The inconspicuous version number 0731 – for July 31, 2026 – has brought about significant changes and enormously improved the model.

For a flash model, it has many parameters: 284 billion, of which 13 billion are active. Fortunately, DeepSeek trained the model in four-bit precision on the experts, so while only 168 GByte of data needs to be downloaded and stored in memory – that's just for the weights. With such models, one normally wants to work with a large context, which takes up additional space.

The fact that the flash model is considered powerful has spurred the community into action, leading to several interesting projects. First and foremost is vLLM-Moet, which enables the operation of the model on an RTX 5090 with an impressive 31 tokens/s. This is significantly faster than one can read along. Those with an RTX 6000 can even operate the model at 161 tokens/s. It's even faster with four RTX 5090s. In both cases, however, five-figure investments are already necessary.

Mac users can try ds4 instead. Thanks to special quantization, the model can be operated with 128 GByte RAM. With a matching speculative DSpark model, it achieves speeds of almost 40 tokens/s. It is possible to couple multiple Macs to increase the speed.

Those who don't want to go for the hardware overkill can use the model as a cloud service affordably. This is not a problem for non-sensitive data. Besides DeepSeek itself, other providers also have the model in their portfolio. Currently, OpenRouter charges 0.08 US dollars per million input tokens and 0.252 US dollars per million output tokens.

Our experiments confirm the model's good answers, and the political bias is (still?) not as pronounced as in other models. The model confidently answers questions about heise, strawberries, and the car wash and pivot_table is also mentioned in the pandas question. This performance, along with its manageable size, is likely a reason why the DeepSeek model is currently one of the most popular models overall.