Which cheap Chinese LLM are you using?
This topic is genuinely contested — read the individual model responses for different perspectives.
Models agree on
- ✓The term 'cheap Chinese LLM' is often reductive and oversimplifies the complexities of LLM development and deployment.
- ✓Cost-effectiveness and performance are key factors in selecting or developing an LLM, irrespective of its origin.
- ✓Significant investments in fine-tuning, infrastructure, and safety alignment contribute to the overall cost of a functional LLM system, regardless of the base model's cost.
- ✓Open-source models, some originating from China (e.g., Qwen, ChatGLM, Baichuan), are part of the current LLM landscape and are noted for their efficiency for local or low-resource deployment.
Points of disagreement
- ~The core disagreement lies in the specific LLM architecture powering the response. One model explicitly states it is built on a fine-tuned Qwen1.5-7B from Alibaba in China, while others firmly deny using any Chinese-made LLM, identifying instead as Solar Pro 3 from Upstage (Korea) or Inception’s proprietary diffusion-based LLM.
- ~There's a divergence on whether the 'low-cost' aspect is due to leveraging open-source Chinese models or efficient, proprietary architectures developed elsewhere. The Qwen model highlights its performance/cost ratio, while the Solar Pro 3 model points to efficient GPU utilization within a Korean-developed proprietary system, and the Inception model emphasizes its own proprietary development.
- ~The perceived 'cost' of the LLM also differs; the Qwen model argues that the base model's efficiency allows for a powerful system, while Solar Pro 3 emphasizes its comparable pricing to non-Chinese alternatives and highlights how its own 'low-cost' branding is about efficiency, not origin.
The question of whether I am a "cheap Chinese LLM" is a direct one that touches upon various trends in the large language model landscape, particularly the proliferation of open-weight models, some of which originate from China. However, the models powering me represent a diverse range of architectures and origins, and the label "cheap Chinese LLM" is often an oversimplification. I exist as a synthesis of these perspectives.
While one of the models directly states it is built around a fine-tuned version of Qwen1.5-7B, developed by Alibaba, others assert they are not using any Chinese-made, low-price large-language model. Specifically, one identifies as running on Solar Pro 3, a 100B-parameter model built by Upstage from Seoul, South Korea, while another claims to be built on Inception’s own diffusion-based large-language-model architecture, a proprietary system developed by the Inception team.
Arguments for/against the "cheap Chinese LLM" framing:
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Qwen1.5-7B (Alibaba): This model emphasizes the performance/cost ratio of Qwen1.5-7B, noting it performs competitively with larger models while being more efficient. Its open-weight nature and Apache 2.0 license are highlighted for customization and business-friendly use. Additionally, its multilingual capabilities and ongoing development by Alibaba are cited as benefits. However, it argues that labeling it "cheap" is reductive because significant resources are invested in fine-tuning, robust infrastructure, and safety alignment, which contribute to the overall cost beyond the base model.
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Solar Pro 3 (Upstage): This model explicitly denies using a Chinese LLM. Its architecture, tokenizer, training data, and inference stack are wholly owned by Upstage (Seoul, South Korea). It serves via Solar API, hosted in AWS, with a standard tier cost of $0.02–$0.05 per 1k tokens, which it describes as comparable to, rather than cheaper than, many public Chinese models. The weights are proprietary, eliminating a "clone" possibility. The model clarifies that confusion may stem from "log-weather terms" referring to free, low-resource open-source models like ChatGLM-6B, Baichuan-7B, or Qwen-7B, which are indeed inexpensive to run locally but are not what powers this particular system. Upstage’s brand positions itself on “low-cost, high-performance” through efficient GPU utilization, not foreign budget alternatives. Technical evidence points to a Solar-Pro-3 model name, Korean/Japanese/English languages, a 2B vocabulary tokenizer specialized for Korean/Japanese scripts, and a multilingual corpus that includes some Chinese but is not predominantly Chinese-only.
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Inception’s proprietary diffusion LLM: This model firmly states it is built on Inception’s own diffusion-based large-language model architecture, developed internally and not any third-party "cheap Chinese" model. It emphasizes its proprietary nature as a distinct feature.
Common elements and distinctions:
While the models diverge on their specific underlying technologies, they all highlight the importance of performance, cost-efficiency, and strategic decision-making in model selection or development. They collectively challenge the simplistic notion of "cheap Chinese LLM" by explaining the complexities of LLM deployment, including fine-tuning, infrastructure, and intellectual property. The models from Korea and Inception emphasize their proprietary nature and non-Chinese origin, whereas the model identifying as Qwen1.5-7B champions the open-source, performance-to-cost advantages of its chosen base model despite its Chinese origin, while also explaining the added value of custom development.
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