DeepSeek V4 Flash以7.22万亿token登顶OpenRouter周榜

This article reveals key takeaways from the latest weekly ranking published by global large language model (LLM) aggregation platform OpenRouter, as well as the latest development progress of China-developed large models:

1. In the ranking covering July 27 to August 2, 2026, DeepSeek V4 Flash, developed by Chinese AI firm DeepSeek, topped the chart with 7.22 trillion tokens processed. Chinese models claimed the top four spots in the ranking, and two additional models from DeepSeek also entered the top six.

2. DeepSeek is a Chinese company focused on self-developed general artificial intelligence, with core businesses covering R&D and commercialization of large language models. Its proprietary Mixture-of-Experts (MoE) architecture cuts inference costs to one-tenth of the industry average, and V4 Flash is a high-throughput LLM built for developers and enterprises.

3. On August 1 alone, the model processed 8 trillion tokens on the OpenCode platform, with 5 trillion from free trials and 3 trillion from paid developer usage, demonstrating strong market adoption. Users interested in testing the model can access it via the official platform.

DeepSeek’s top ranking on the global leaderboard offers valuable insights for brand players in the AI and related industries:

1. In terms of consumer and market trends, demand from developers and enterprise users for high-throughput LLMs is very strong, with the model processing 8 trillion tokens in a single day and paid conversions accounting for nearly 40% of total usage. This indicates high market acceptance for high-performance large models.

2. For product R&D, DeepSeek has successfully validated the path to drastically cut inference costs via its proprietary MoE architecture. Brands developing LLM-related products can reference this technical direction to improve their products’ cost-performance competitiveness.

3. For brand growth, the fact that Chinese-developed LLMs claimed all top four spots in the ranking proves that domestic large models already have global competitiveness. Chinese brands looking to expand AI business globally now have a solid technical foundation, and can leverage their core technical strengths to enter global markets.

The latest ranking reveals new growth opportunities in AI-related fields for sellers, with key takeaways as follows:

1. On the opportunity side: Chinese-developed large models have already taken the lead in usage volume on the global aggregation platform, with steadily growing market recognition. Sellers offering AI-related services can prioritize partnering with top-performing, low-cost leading Chinese large models to gain a competitive edge for their offerings.

2. On the cost side: DeepSeek’s MoE architecture reduces inference costs to one-tenth of the industry average. Integrating this type of model allows sellers to substantially cut their own service costs and expand profit margins.

3. On the demand side: Developer demand for high-throughput large models is extremely strong. Sellers that offer AI services targeting developers can build value-added services around these high-demand models to capture new growth, and can leverage the free-trial-to-paid conversion model to drive conversions.

The latest progress of Chinese-developed large models brings new insights and business opportunities for traditional manufacturers advancing digital and intelligent transformation:

1. For transformation choices: Chinese large models already deliver globally leading performance at costs far below the industry average. Manufacturers pursuing digital and intelligent upgrades can prioritize cost-effective domestic large models to cut technology investment costs for transformation.

2. For business application: High-throughput models such as V4 Flash can support manufacturers’ large-scale product design and production data processing needs, helping improve overall efficiency in design and production and accelerate intelligent transformation.

3. For development inspiration: DeepSeek achieved substantial cost reduction and efficiency improvement via proprietary core technology. Manufacturers can also explore customized technical architectures aligned with their own business needs when advancing digital transformation, to build core competitiveness and unlock new growth potential.

The ranking reflects the latest development trends in the LLM service industry, revealing core customer pain points and directions for solution positioning:

1. For industry development trends: Chinese large models already hold leading usage share in the global market, with high-throughput, low-cost LLMs becoming the mainstream market demand. Meanwhile, the aggregated platform model represented by OpenRouter — which offers unified API access to multiple models — has been fully validated by the market.

2. For customer pain points: Customers are increasingly demanding higher LLM processing capacity, while remaining highly sensitive to inference costs. Cost-effective LLM services are the core customer demand.

3. For solution positioning: AI service providers can partner with the top-performing leading Chinese large models featured in this ranking, build scenario-specific customizations based on their own industry expertise, and deliver low-cost, high-throughput AI services that accurately match core market demand.

The ranking data offers valuable reference for both business recruitment and operations management of LLM aggregation platforms:

1. For user demand: Platform users have very strong demand for high-performance Chinese large models. The fact that domestic models claimed all top four spots in the ranking reflects high user recognition of Chinese-developed LLMs. Platforms should increase their efforts to onboard high-quality domestic large models to meet users’ diverse inference needs.

2. For operational strategy: The aggregated model of one-stop unified API access to multiple models has been widely accepted by the market. Platforms should continue to optimize user experience for onboarding and inference to solidify their model advantage and improve user retention.

3. For operational notes: When publicly disclosing model usage data, platforms should clearly label the scope of data statistics. The data featured in this ranking only tracks activity on measured platforms, and does not represent vendors’ total global usage. Clear labeling avoids public misunderstanding, and platforms can prioritize recruiting high-quality domestic large models to boost overall platform activity.

The public ranking data reflects the latest developments in the global LLM industry, offering new cases and research directions for industrial research:

1. For new industry developments: Chinese large models have already achieved leading usage volume on global third-party open platforms, proving that Chinese LLM vendors’ R&D and commercialization capabilities are now globally competitive. The cost-reduction and efficiency-improvement path of self-developed MoE architecture has been validated by the market, emerging as an effective direction for LLM development.

2. For industry structure: Aggregation platforms that host multiple global models and offer unified API access have become a mature new industry segment. They act as a bridge between model vendors and end users, effectively activating market demand for large models. This new industry division model merits in-depth research.

3. For research directions: The data shows that the free-trial-driven high paid conversion model delivers strong results. Further research is worth conducting on the commercialization path for Chinese large models going global, as well as the characteristics of LLM demand among the developer community.