Introduction
In recent years, artificial intelligence (AI) has transformed from an experimental technology into a pervasive force that underpins innovations across myriad industries. Among the most notable advancements are the emergence of large language models (LLMs) and multimodal systems, which have accelerated the pace of innovation in fields ranging from natural language processing and computer vision to robotics and healthcare.
Yet alongside these advances, the concept of commoditization has emerged as a critical point of debate. Commoditization, in the AI context, refers to the process by which AI models evolve from proprietary, cutting‐edge innovations into standardized, ubiquitous tools that are widely available and often interchangeable.
This article examines, from a holistic perspective, whether AI models are truly being commoditized. It weighs the arguments for and against this notion, scrutinizes the latest advancements from prominent industry leaders such as Google’s Gemini 2.5, Anthropic’s Claude Sonnet 3.7, x.AI’s Grok 3, DeepSeek’s DeepSeek V3, and OpenAI’s latest LLMs, and explores the implications of these developments. A critical question in this analysis is whether frontier AI companies should pivot away from investing predominantly in model development and instead concentrate on developing products and services that effectively leverage these models.
In what follows, the discussion traverses several key sections: an overview of the latest AI model developments; an examination of the factors driving commoditization; a presentation of counterarguments that emphasize differentiation; an investigation into why the industry may benefit from a pivot toward products and services; an exploration of the broader implications for startups, investors, consumers, and society; and, finally, a forward-looking analysis of the future landscape of AI. Drawing on multiple perspectives and evidence from sources across the internet, this article offers an in-depth exploration that is intended to be one of the most comprehensive analyses available on the commoditization of AI models.
Section 1: The Latest Developments in AI Models
The AI industry is characterized by rapid innovation and fierce competition. Leading tech companies continue to develop new models that push the boundaries of what AI can achieve. A closer look at recent releases reveals both the technical nuances that differentiate these models and the common trends that hint at a broader commoditization process.
Gemini 2.5 (Google):
Google’s Gemini 2.5 is emblematic of the current wave of multimodal and reasoning-enhanced AI models. Gemini 2.5 is designed with advanced capabilities that include handling multiple types of input such as text, images, audio, and video. Its ability to manage context windows of up to one million tokens—with future plans to extend that capacity—positions it as a robust tool for complex tasks such as coding, advanced reasoning benchmarks (e.g., GPQA and AIME 2025), and integrative analytical functions. Gemini 2.5’s emphasis on “thinking” before responding aims to enhance accuracy in problem solving, thereby making it ideal for enterprise applications that necessitate high levels of reliability and precision (TechCrunch).
Claude Sonnet 3.7 (Anthropic):
Anthropic’s Claude Sonnet 3.7 takes a hybrid approach by integrating both rapid response capabilities and deep reasoning. Designed to excel in customer interaction-focused scenarios, it has been benchmarked using the SuperGLUE suite where it achieved impressive scores. This model is purpose-built for applications in customer service, education, and tailored content generation. Moreover, Claude Sonnet 3.7 has been engineered to minimize unnecessary refusals—a critical improvement that enhances its usability in real-time applications. Although it demonstrates slightly lower performance in specialized tasks like coding compared to Gemini 2.5, its unique blend of rapid responsiveness and deep analytical capacity gives it a competitive edge in service-oriented environments (TechCrunch).
Grok 3 (x.AI):
Grok 3, developed by x.AI, represents a focus on high performance and ethical AI practices. Built atop impressive computational resources—including the Colossus Supercomputer powered by 200,000 NVIDIA H100 GPUs—Grok 3 is optimized for speed, complex reasoning, and precision in problem solving. It features an innovative “Big Brain Mode” which allows for extended periods of internal reasoning, thereby delivering more accurate results in specialized tasks such as financial analysis, code debugging, and medical diagnostics. Additionally, the inclusion of sophisticated bias-reducing algorithms in Grok 3 reflects an industrywide recognition of the need for responsible AI (Accredian Blog).
DeepSeek V3 (DeepSeek):
DeepSeek V3 is a noteworthy example of the open-source movement within AI. Released under the MIT license and available on platforms such as Hugging Face, DeepSeek V3 leverages a Mixture of Experts (MoE) architecture that activates only the relevant parameters during inference. This design not only optimizes computational efficiency but also extends the model’s capacity for long-text context retention and multi-token prediction. Its availability as an open-source alternative has fostered a culture of collaboration and rapid innovation in research and enterprise applications alike (GizChina).
OpenAI’s Latest LLMs (GPT-4o Series):
OpenAI continues to lead the market by evolving its product offerings into a suite known as the GPT-4o series. These models emphasize multimodal capabilities—supporting text, image, and audio processing—and are engineered to be cost-effective through competitive pricing strategies for both input and output tokens. The GPT-4o series is designed for a wide array of professional applications, including transcription, content creation, and ideation processes. By integrating these models with sophisticated APIs and user-friendly tools, OpenAI has effectively democratized access to state-of-the-art AI capabilities, making these models a staple in professional workflows (Simon Willison’s Blog).
Despite the differences in architecture and intended applications, these models exhibit converging trends. As companies enhance models with improved reasoning, multimodality, and specialization, the boundaries between proprietary and open-source systems blur. The remarkable performance improvements achieved by these models suggest that while technical sophistication continues to advance, the underlying technology is rapidly approaching a point where differentiation becomes subtle, thus fueling the discussion around commoditization.
Section 2: Factors Driving the Commoditization of AI Models
The ongoing commoditization of AI models is propelled by several interrelated forces. Chief among these are advances in open-source technology, significant reductions in development costs, and growing market saturation. Together, these factors contribute not only to a more interconnected and accessible AI ecosystem but also to the erosion of competitive differentiation that once characterized the field of AI development.
Open-Source Innovation and Democratization:
Open-source AI projects have disrupted traditional business models by providing high-quality models at either no cost or a fraction of the expense associated with proprietary systems. Platforms like Hugging Face have become hubs for community-led innovation, where models such as DeepSeek V3 and Meta’s LLaMA are freely available. This democratization of AI enables smaller players and research institutions to access, experiment with, and improve upon state-of-the-art technology without substantial financial barriers. The widespread adoption of open-source platforms accelerates innovation while simultaneously driving standardization, a hallmark of commoditization. By lowering the cost of entry and facilitating widespread experimentation, open-source models have contributed to an environment where accessibility trumps exclusivity (Unite.AI).
Substantial Cost Reductions:
The financial barriers associated with training large language models and deploying them at scale have diminished markedly. Advances in hardware, including specialized GPUs and tensor processing units, along with algorithmic breakthroughs like quantization and efficient transformer architectures, have collectively reduced the cost of training and inference. For instance, OpenAI’s reported 150x reduction in token costs between 2023 and 2024 is a striking metric that underscores this trend. As computational and operational costs fall, even well-funded startups can afford to experiment with cutting-edge AI technologies. The cascading effect of cost reductions reinforces the commoditization narrative by making advanced AI models available to a broad spectrum of users and enterprises.
Market Saturation and Intensified Competition:
The proliferation of AI models in the market has introduced a scenario of intense competition, where multiple vendors offer systems that are increasingly similar in functionality. Companies like Google, OpenAI, Anthropic, and others are in a race to deliver the most capable models, leading to an eventual blurring of distinct competitive advantages. In this environment, price becomes a critical lever. With several players offering comparable services, the focus shifts toward cost-efficiency and ease of integration rather than unique technological breakthroughs. This saturation not only encourages a “race to the bottom” in terms of pricing but also compels service providers to seek differentiation through applications and ecosystems rather than the underlying model technology itself (Forbes).
Technological Advancements in Energy Efficiency:
Energy-efficient AI techniques, such as the implementation of binary transformers and 1-bit operations, have further accelerated commoditization. These innovations substantially cut down the computational demands associated with deploying AI models. The ability to run complex models on lower-powered hardware not only broadens the accessibility of AI but also encourages deployment in cost-sensitive environments, from small enterprises to large-scale industrial systems. As firms adopt energy-efficient practices, the marginal cost of generating AI-driven outcomes continues to decline, reinforcing the trend toward commoditized technology.
Emergence of AI “Wrappers” and Application Layers:
An increasingly significant phenomenon is the rise of AI “wrappers”—software companies that build user-friendly, domain-specific applications on top of underlying AI models. These wrappers abstract away the complexities of the models themselves and offer streamlined, productized interfaces that cater to particular market needs. Examples of this trend include platforms that integrate AI for tasks such as document summarization, customer engagement, and data analytics. With the shift of value from raw model capability to the overall user experience, the commoditization of foundational models becomes even more pronounced. In this context, the differentiation is no longer about the model itself but rather about the ecosystem and supplementary services that enhance its usability (Medium).
Conclusion on Driving Factors:
The interplay of these factors—open-source innovation, cost reductions, market saturation, energy efficiency advancements, and the rise of AI-centric ecosystem services—creates a multifaceted landscape that pushes AI models toward commoditization. This scenario not only democratizes access to advanced AI but also necessitates a re-examination of competitive strategies in the industry. As AI models become more standardized, the focus shifts from the models themselves to the applications built around them, marking a pivotal change in the way AI is monetized and applied across sectors.
Section 3: Counterarguments to Commoditization
Despite the compelling case for commoditization, several counterarguments underscore why AI models may retain distinctive, value-adding characteristics even as they become more widely accessible. These counterarguments center on the importance of proprietary research, the role of specialization, innovation in applications, and the continued relevance of human expertise.
Proprietary Research and Technological Moats:
Even in an era of democratized AI, significant investments in proprietary research continue to create technological moats. Companies like OpenAI, Google, and Anthropic devote large budgets to research and development, which leads to innovations that are difficult for competitors to replicate. Proprietary datasets, which often comprise vast amounts of carefully curated and proprietary information, are particularly valuable. These datasets enhance the training of AI models in ways that generic, open-source models may not match. The distinctive algorithms and training techniques developed internally by these organizations can produce models with performance advantages in niche or specialized applications, thereby limiting the extent of commoditization.
Specialization Through Domain-Specific Models:
A major argument against complete commoditization lies in the development of domain-specific models. Whereas general-purpose models aim to provide broadly applicable capabilities, specialized models are tailored, often through fine-tuning on proprietary datasets, to excel in specific fields such as healthcare, finance, or legal research. For instance, a model fine-tuned for medical diagnostics can leverage subtle patterns in clinical data that general models might overlook. Such specialization is inherently resistant to commoditization because it relies on unique, context-rich information and deep domain expertise. Enterprises that adopt domain-specific AI can maintain competitive advantages by creating solutions that are finely attuned to the unique nuances of their markets (TechCrunch).
Innovation in How AI is Applied:
The value of AI lies not solely in the underlying model architecture but in how the technology is integrated into products and solutions. Companies like Netflix and Amazon have demonstrated that the transformative impact of AI is achieved when it is applied creatively to solve specific problems. Netflix’s recommendation system and Amazon’s logistics optimization are prime examples in which AI is not merely an add-on but a fundamental component of the service offering. By embedding AI into broader operational ecosystems and designing user-centered applications, organizations create value propositions that are far more complex and dynamic than the sum of their parts. This innovation in application—and the systems and processes built around AI—serves as a robust counterforce to commoditization.
The Enduring Role of Human Expertise:
A frequently cited counterargument is the indispensability of human expertise. No matter how sophisticated AI models become, their effectiveness is ultimately determined by the people who design, interpret, and manage these systems. The combination of human knowledge and AI capabilities often yields results that are more nuanced and context-aware than what any machine could achieve on its own. This synergy between human judgment and AI-driven insights is particularly evident in complex decision-making workflows, advanced diagnostics, and creative industries. Such instances where human-AI collaboration is central not only undermine the idea that AI can be reduced to a mere commodity but also reinforce the notion that the human element remains pivotal.
Ethical and Governance Considerations:
As discussions around ethical AI gain traction, the ability of companies to differentiate themselves based on responsible AI practices becomes another critical factor. Transparency, fairness, and accountability in algorithmic decision making are increasingly valued both by regulatory bodies and by the public. Organizations that prioritize robust ethical frameworks and compliance with evolving regulatory standards can carve out unique niches in the market. By adhering to strict ethical guidelines, such companies build trust and loyalty among customers—advantages that are difficult to commoditize because they involve nuanced considerations of social responsibility and corporate governance (Frontiers).
Conclusion on Counterarguments:
While the factors driving commoditization are powerful, the counterarguments highlight that AI is not destined to become a bland, undifferentiated utility. Proprietary research, specialization, innovative application, and human expertise ensure that certain aspects of AI remain unique and capable of driving competitive differentiation. The challenge for companies, therefore, is to balance open access with continuous innovation, ensuring that even as models become more widely available, their application leads to unique, value-adding products and services.
Section 4: The Pivot from Model Development to Products and Services
In light of the dual trends of commoditization and rapid technological advancement, an increasingly pertinent question is whether AI companies should pivot from a singular focus on model development to an emphasis on creating comprehensive products and services. This strategic pivot is being debated widely in industry discussions and echoes broader shifts in how technology companies derive value.
Understanding the Pivot:
The pivot toward products and services involves transitioning from concentrating exclusively on the underlying AI models to developing customer-centric applications that integrate these models into end-to-end solutions. Instead of viewing AI as an isolated technological commodity, companies adopting this strategy use AI as one component of a broader ecosystem. The value proposition, in this context, lies not just in the model’s performance but in the overall user experience, data integration, responsiveness, and support ecosystem that surrounds it.
Benefits of the Pivot:
Moving toward products and services offers several distinct advantages. First, it opens up opportunities for revenue diversification. Companies can generate stable income through subscription models, licensing agreements, and ecosystem services that add recurring value for their customers. OpenAI’s ChatGPT, for example, has evolved from a demonstration of advanced language modeling into a subscription-based product that supports a wide range of professional applications.
Another benefit is market differentiation. When competing AI models become increasingly similar in technical capacity, the differentiator shifts to the quality of the applications built around them. Companies that successfully integrate AI into products tailored to specific industries—such as healthcare diagnostics platforms, personalized educational tools, or intelligent financial analysis systems—can command higher pricing and foster deeper customer loyalty.
Customer-centric innovation is another key driver. By focusing on the end user’s needs, companies can develop solutions that solve real-world problems rather than merely showcasing technical prowess. This approach not only improves customer satisfaction but also builds a competitive moat that is resistant to commoditization, as it combines technical excellence with domain expertise and bespoke service delivery.
Risks and Challenges:
The pivot is not without challenges. Developing robust, customer-facing products requires significant investment in infrastructure, user interface design, customer support, and continuous product iteration. For companies that have traditionally focused on model development, this transition entails acquiring new talent in product management, design, and marketing—and resources may be stretched during the transition period.
There is also the challenge of market alignment. Firms must understand their customers’ varied needs and ensure that the new offerings do not alienate the existing user base. Overcommitting to one product line could lead to strategic misalignment if the market’s demands shift. Additionally, as products become more integrated into daily workflows, issues surrounding data privacy, regulatory compliance, and ethical usage become increasingly pronounced.
Case Studies and Industry Examples:
Several companies provide instructive examples of how to pivot successfully in the AI space. OpenAI’s transition from developing breakthrough models to offering products like ChatGPT has been met with widespread admiration. By building user-friendly applications that harness the power of its advanced models, OpenAI has demonstrated that the future of AI lies in its integration into everyday workflows. Similarly, Amazon Web Services (AWS) has repositioned itself as more than just a cloud infrastructure provider by launching a suite of machine learning services like Amazon SageMaker, which enables businesses to build and deploy AI solutions without delving into the complexities of model development. Google, too, has embedded its AI capabilities into widely used products such as Google Assistant, Google Photos, and Google Translate, thus ensuring that its sophisticated models power tangible, daily-use services.
Conclusion on the Pivot:
The pivot from model development to products and services is emerging as a viable strategy for AI companies, particularly in an era where foundational AI capabilities are increasingly accessible. By focusing on creating comprehensive solutions that address specific market needs, companies can secure sustainable revenue streams and differentiate themselves even in a commoditized landscape. The challenge lies in executing this transition efficiently and aligning the product offerings with market demands, yet the potential rewards—a resilient business model and deeper customer engagement—make the pivot an enticing prospect.
Section 5: Broader Implications
The dual phenomena of AI model commoditization and the strategic pivot towards products and services have ramifications that extend far beyond the confines of technology companies. The broader implications of these trends affect startups, investors, consumers, and society as a whole.
Impact on Startups:
For startups, the commoditization of AI models has lowered the barriers to entry, enabling smaller players to harness powerful AI tools without incurring prohibitive R&D costs. This democratization catalyzes innovation, allowing nimble startups to develop niche and industry-specific applications that address unique market challenges. At the same time, the crowded marketplace raises the stakes for differentiation. With many companies having access to similar foundational models, startups must rely on proprietary data, innovative application layers, and agile business models to carve out sustainable niches. The ability to rapidly deploy tailored solutions can become a competitive advantage, although the pressure to continuously innovate remains high.
Impact on Investors:
Investors have increasingly recognized that the true value in the AI space may not lie solely in the development of models but in the application of AI to real-world problems. Venture capital investments have shifted towards companies that successfully integrate AI into products and services with demonstrable market traction. This evolving focus reflects an appreciation for businesses that leverage differentiation through proprietary data, specialization, and unique ecosystem partnerships. As a result, investment strategies have gradually shifted from a pure technology play to a broader vision that encompasses consumer adoption, revenue models, and service scalability. Investors are actively seeking companies that not only innovate technologically but also deploy their innovations in ways that promise robust, recurring revenue streams.
Impact on Consumers:
For consumers, the commoditization of AI models translates into more affordable, accessible, and versatile technology solutions. The competitive landscape drives down costs, thereby enhancing affordability across a wide range of applications—from personalized content recommendations and virtual assistants to healthcare diagnostics and educational tools. However, this democratization also brings risks. Consumers may encounter generic, one-size-fits-all solutions that fail to address specific needs if the underlying AI models are not sufficiently tailored. The challenge, therefore, is ensuring that the benefits of commoditized AI are realized through thoughtfully designed products that engage users on a personal level.
Societal Implications:
At a societal level, the broad deployment of commoditized AI coupled with the rise of integrated AI products and services is reshaping job markets, ethics, and regulatory frameworks. The democratization of AI enables enhanced access to technology across industries, contributing to greater economic inclusion and innovation. However, it also raises ethical concerns ranging from data privacy and algorithmic fairness to the potential displacement of workers in roles susceptible to automation. For instance, as AI becomes more embedded in decision-making processes across sectors, it is imperative that robust governance and ethical standards are enforced to prevent bias and ensure accountability. Educational institutions and policymakers must also adapt to prepare the workforce for a future where human creativity and complex problem-solving are paramount.
Conclusion on Broader Implications:
The broader implications of AI commoditization and the ensuing strategic pivot underscore a transformative period in technology and society. While the accessibility and affordability of AI promise significant gains in innovation and efficiency, there is an accompanying need for vigilance around ethical standards, workforce adaptation, and the sustainable integration of technology into daily life. The future success of the AI ecosystem will depend on the capacity of all stakeholders—startups, investors, consumers, and regulators—to collaborate and navigate these challenges.
Section 6: Future Outlook
Looking forward, the evolution of AI will likely be characterized by a continuing trend toward commoditization of foundational models, paired with increasing differentiation through applications, products, and services. Several predictions can be made regarding the trajectory of the AI industry in the coming years:
Continued Lowering of Barriers:
Technological advancements and open-source contributions are expected to further reduce the cost of AI development and deployment. As energy-efficient techniques become standardized and hardware continues to improve, advanced AI models will be within reach for an even broader audience. This increased accessibility will likely spur innovative applications across verticals that have yet to fully exploit AI’s potential.
Deep Specialization and Domain-Specific Solutions:
While foundational models may become commoditized, the true competitive edge will emerge from deep specialization. Companies that invest in fine-tuning models for specific industries—whether healthcare, legal, financial services, or education—will continue to find value in bespoke solutions. The ability to leverage proprietary data and develop industry-specific expertise will maintain differentiation even in a market saturated with general-purpose AI models.
Hybrid Business Models:
The future likely holds a blend of commoditized AI capabilities and differentiated application layers. We can expect to see companies evolving into platform providers, offering modular AI services that can be customized and integrated into a variety of contexts. This hybrid model will emphasize the interplay between standardized machine intelligence and unique, customer-tailored innovation—a balance that will be crucial for sustained market leadership.
Regulatory and Ethical Maturation:
As AI technology becomes increasingly embedded in everyday applications, regulatory frameworks and ethical standards will mature in tandem. Governments and international bodies are expected to implement policies that promote transparency, fairness, and accountability in AI usage. Companies that proactively shape and adhere to these standards will be better positioned to build trust and maintain competitive advantages in a regulated market.
Enhanced Human-AI Collaboration:
The future of AI is not simply about replacing human functions but about augmenting human capabilities. Enhanced human-AI collaboration promises to yield innovative solutions in complex problem-solving, creative industries, and scientific research. By integrating AI systems with robust human oversight and intuition, organizations can bridge the gap between commoditized technology and specialized, context-aware decision-making.
Investment in Ecosystems and Platforms:
Investors are concluding that the most lucrative opportunities lie in companies that build comprehensive ecosystems around AI tools—providing not just the raw model but a suite of integrated services, support, and applications. This trend encourages a convergence toward platforms that facilitate seamless integration with existing enterprise workflows, user interfaces, and customer support systems. The success of such ecosystems will determine which companies emerge as leaders in the next generation of AI-driven innovation.
Conclusion on Future Outlook:
The evolution of the AI landscape is set on a path where commoditization of foundational capabilities coexists with intense specialization and innovation in applied solutions. Companies that invest in a balanced strategy—harnessing widely accessible models while also building unique, customer-centric products—will be best positioned to thrive. The future of AI, therefore, lies not merely in the sophistication of the models but in the ingenuity of their application.
Conclusion
It is evident that the AI industry is undergoing a profound transition. The commoditization of AI models—driven by open-source innovation, reduced costs, market saturation, and technological advancements—has made sophisticated machine intelligence accessible to a wider array of users. However, this process does not signal the end of competitive differentiation. Rather, it has shifted the battleground. The true value is now emerging from how companies leverage these standardized models through proprietary research, domain specialization, and the creation of comprehensive products and services.
As demonstrated by the latest releases from industry leaders such as Google’s Gemini 2.5, Anthropic’s Claude Sonnet 3.7, x.AI’s Grok 3, DeepSeek V3, and OpenAI’s GPT-4o Series, the technical prowess behind AI systems continues to evolve at a breakneck pace. Yet beneath this evolution lies a critical strategic imperative: AI companies must decide whether to continue racing toward incremental improvements in model performance or to pivot their focus toward developing applications that deliver tangible, real-world value.
The arguments in favor of commoditization emphasize that open-source models and cost efficiencies have spurred a democratization of AI, making it a utility that underpins a variety of services. In contrast, counterarguments highlight that proprietary research, deep specialization, innovative applications, and the irreplaceable role of human expertise ensure that AI remains a field where differentiation is possible and valuable.
For startups, the lowered entry barriers foster innovation, but heightened competition necessitates a clear, niche focus. Investors are increasingly seeking companies that not only leverage commoditized AI models but also build robust, scalable ecosystems around them. Consumers, while reaping the benefits of lower costs and improved services, must remain vigilant against generic implementations that might lack the nuance required for specialized applications. In the broader context, society must navigate ethical, governance, and workforce adaptation challenges in a landscape where AI is both ubiquitous and transformational.
Looking forward, the future of AI will be defined by a balance—between commoditized, standardized models and the differentiated application layers that make them unique. Companies that succeed will be those that adapt to this hybrid environment by not only harnessing the power of accessible AI but by transforming it into innovative, customer-centric solutions that drive competitive advantage.
In conclusion, while AI models are undeniably tending toward commoditization, the journey is far from a narrative of uniformity. Instead, it is a call for strategic evolution—a shift from high-profile model development toward building comprehensive products and services that deliver real, measurable value. For practitioners, entrepreneurs, and investors alike, the imperative is clear: the future of AI is in the applications, and the true competitive edge lies in the creative integration of technology with human insight.
This holistic analysis underscores a pivotal truth about the AI revolution. As the tools become ubiquitous, their transformative power will hinge on how effectively they are applied. In the emerging era of commoditized AI, those companies that adeptly pivot to holistic, customer-focused ecosystems while continuing to push the boundaries of innovation will lead the next wave of technological and societal progress.
References
Google’s Gemini 2.5:
TechCrunch. “Google Unveils a Next-Gen AI Reasoning Model.” Retrieved from techcrunch.com.
Anthropic’s Claude Sonnet 3.7:
TechCrunch. “Anthropic Launches a New AI Model That Thinks as Long as You Want.” Retrieved from techcrunch.com.
Grok 3 (x.AI):
Accredian Blog. “Grok 3: High-Performance AI for the Enterprise.” Retrieved from blog.accredian.com.
DeepSeek V3:
GizChina. “DeepSeek Unveils AI Model DeepSeek V3.” Retrieved from gizchina.com.
OpenAI’s GPT-4o Series:
Simon Willison. “New OpenAI Audio Models.” Retrieved from simonwillison.net.
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