Yuxu Ge
Article · 2025-04-22

A Survey of Mainstream LLM Vendors and Their Representative Models in 2025

OpenAI's progression from GPT-3 through GPT-4.5 demonstrates how each generation brought distinct capabilities:

GPT-3 (2020) was the first model to demonstrate surprising language generation at scale. With roughly 175 billion parameters, it could generate coherent multi-sentence continuations from a single prompt—a threshold that fundamentally changed perceptions of what neural networks could do.

GPT-3.5 (circa 2022) optimized GPT-3 for dialogue. ChatGPT's initial backbone, it produced more natural conversational responses with fewer errors. Think of it as an incremental refinement: the same underlying architecture tuned for how people actually interact with AI.

GPT-4 (2023) represented OpenAI's next leap. Though exact parameter counts remain undisclosed, industry estimates suggest potentially trillions of parameters—far beyond GPT-3. The major breakthrough was multimodal capability: GPT-4 can interpret images, not just text. You can upload a photograph and ask it to describe or answer questions about its contents. This raised the model from text-only to vision-and-language reasoning. GPT-4 showed stronger inference and understanding across professional exams and specialized tasks.

GPT-4.5 (early 2025) sits between GPT-4 and a hypothetical next flagship. CEO Sam Altman described it as "a huge and expensive model." Compared to GPT-4, performance improved across tasks, with particular gains in non-English languages and creative work. Reports indicate GPT-4.5 surpassed the original GPT-4 on benchmarks across 15 languages. The cost reflects the capability jump: API pricing is several times that of GPT-4, so GPT-4.5 targets premium users and enterprises, while GPT-4 remains common in everyday applications due to lower cost.

OpenAI's naming scheme is straightforward: higher numbers mean newer generations. Integer versions (like 4) typically signal architectural shifts; point versions (like 4.5) mark iterative improvements. Practically, GPT-3.5 suits everyday conversation and general content; GPT-4 handles complexity and demands strict accuracy; GPT-4.5 serves high-stakes professional work requiring creative sophistication and multilingual strength. As the industry benchmark, OpenAI's GPT line leads in conversational reasoning, coding assistance, and content generation, though its closed commercial model keeps implementation details private.

Anthropic's Claude: Safety-First Alternative

Anthropic, founded by former OpenAI researchers, entered the competition with Claude—a name deliberately chosen to sound like a person rather than a product designation, reflecting their positioning: an "AI assistant" that operates as a thoughtful partner. Anthropic emphasizes AI safety and reliability, introducing concepts like an "AI constitution" to ensure models act in human interest without causing harm.

Claude 1 launched in March 2023 for limited testing. Claude 2 released publicly in July 2023. Two improvements stood out: vastly longer context length and support for file attachments. Context length is how much text a model can "remember" at once. Claude 2 expanded this from roughly 9,000 words to 100,000 words—enough to read an entire novel and answer questions about it. GPT-4's standard version supports 8,000 words; even its extended version tops out around 32,000. Claude's capacity for long-document analysis proved powerful for legal contracts, technical documentation, and similar use cases. The file-attachment feature let users upload PDFs directly; Claude could summarize or answer questions about their contents.

Claude's naming lacks GPT's clear numerics, but versions do evolve internally. After Claude 2, Anthropic continued improving—Claude 2.1 (November 2023) doubled context length to 200,000 words. By 2024, Claude 3 arrived, setting new benchmarks in reasoning, code generation, and cross-task performance. Reports credit Claude 3 with novel features like extended reasoning chains and tool use, enabling more complex cognitive tasks, including multi-step reasoning and autonomous action. These advanced versions remain in limited professional testing; most users encounter Claude 2 family models.

Overall, Claude excels at conversational safety and long-text handling. Its tone tends toward politeness, strictly adhering to safety guidelines—sometimes criticized as overcautious. On performance, Claude 2 already competed with GPT-4 on many tasks while holding advantages in long-text comprehension. For users handling massive documents or valuing reliable, carefully bounded responses, Claude offers a compelling choice.

Google DeepMind's Gemini

Google, a longtime AI player, committed enormous resources to large models. In 2023, Google unified its Brain team with DeepMind to concentrate firepower on next-generation foundation models. The result: Gemini, a model family that takes its name from the zodiac symbol suggesting combined capabilities—"twin" traits in one system.

Gemini 1.0 (late 2023) shipped in three tiers: Ultra (maximum performance), Pro (general purpose), and Nano (mobile devices). Ultra targets the most demanding tasks; Pro suits most applications; Nano embeds in phones. At launch, Google integrated Gemini Pro into Bard and deployed Nano on Pixel phones. Google's strategy: multiple models at different scales across cloud and edge.

Gemini's native multimodal training is a major selling point. Google trained Gemini on text, images, audio, video, and code simultaneously. This means Gemini can understand images, process sound, and write code alongside generating text—capabilities built in from the start. DeepMind's CEO Demis Hassabis suggested Gemini could combine the strategic reasoning of AlphaGo with the language capacity of large models, potentially exceeding ChatGPT on general intelligence. Google recalled founder Sergey Brin to help. Training incorporated massive datasets including YouTube transcripts.

By 2024, Google iterated rapidly. Gemini 1.5 entered developer testing mid-year; Gemini 2.0 launched in December 2024. Gemini 2.0 strengthened real-time multimodal handling—real-time audio and video interaction for smarter voice assistants. Google also introduced "Agentic AI," exploring how models can drive software agents to complete tasks for users. The launch framed Gemini 2.0 as "a model for the autonomous AI agent era." Agentic AI means AI acting like an autonomous agent: reading emails, checking schedules, searching online, and executing multi-step tasks. Gemini's architecture supports this with tool-use capabilities—calling external applications and APIs (learning to use browsers, calculators, and similar utilities).

Google uses version naming like software (1.0, 2.0, 2.5) with sub-model names (Pro, Flash). Each major bump signals capability leaps; jumping from 1.0 to 2.0 showed dramatic progress in real-time multimodality. Google tends to embed models directly in products rather than offer standalone paid APIs. Many users benefit from Gemini in Google Search, Translate, and Assistant without realizing which version powers it. Overall, Google DeepMind—leveraging research depth and data scale—positions Gemini as a strong OpenAI competitor, particularly in multimodal fusion and tool use.

DeepSeek: The Disruptor

Late 2024 saw an unexpected entrant: DeepSeek, an open-source-first startup that shook the industry. Rather than a traditional tech giant, DeepSeek blended open-source principles with startup agility, pursuing an open-plus-low-cost strategy that caught competitors off guard. In industry terms, it played the role of an aggressive competitor, disrupting a landscape dominated by incumbents.

DeepSeek's naming differs from competitors. Two product lines exist: a general foundation series marked with "V" and version numbers (V1, V2, V3), and a reasoning-focused series marked "R" (R1). DeepSeek-V3 (as of early 2025) represents the latest general-purpose model; DeepSeek-R1 is the first "deep reasoning" model, emphasizing complex reasoning and tool use. The V series iterates like a continuously upgraded engine; the R series adds navigation and a tool kit on top—AI that not only thinks but actively retrieves information and uses tools to solve problems.

DeepSeek's arrival alarmed competitors. Reports credit DeepSeek-R1 with a disruptive entry: by open-sourcing model code and weights while charging far less than OpenAI, it forced industry-wide strategy adjustments. Developers and enterprises could run DeepSeek locally for free or use its cloud API at a fraction of commercial rates. High performance at low cost prompted client migrations. Closed-source vendors responded: OpenAI accelerated GPT-4.5 launch timelines; Google opened Gemini to more users faster; in China, Baidu announced Wenxin free for all users and plans to open-source its next generation.

DeepSeek-V3 performs competitively—reportedly near GPT-4.5 on general benchmarks or surpassing it in some. DeepSeek-R1 pioneered deep reasoning and autonomous action, including web search and plugin tools. R1 currently lacks multimodal strength (text attachments only; image recognition remains limited).

DeepSeek exemplifies open-source LLM growth, enabled by Meta's LLaMA releases and backed by global developer collaboration. For organizations prioritizing data privacy, wanting proprietary customization, or constrained by budget, open-source models offer affordable, controllable alternatives. DeepSeek seized this demand, delivering quality through community-driven development, becoming a force the industry cannot ignore.

Baidu Wenxin: Homegrown Strength

China's AI competition centers on Baidu. Baidu invested early in pretraining, launching the Wenxin series (ERNIE in English). When ChatGPT sparked domestic interest in 2023, Baidu swiftly released Wenxin Yiyan as a rival conversational product, backed by Wenxin models. Over two years, iteration accelerated: versions progressed from 3.0 through 4.0 and 4.5, plus a specialized variant X1, forming a coherent lineup.

Wenxin 3.0 predates 2023. With billions of parameters, Baidu's research papers describe Wenxin 3.0 Titan at 260 billion parameters—among the world's largest Chinese-language pretraining models at the time, establishing the series foundation.

Wenxin 4.0 (H2 2023) represented a comprehensive upgrade: new architecture, improved training data, leap in performance. Reports claim Wenxin 4.0 reached GPT-4 parity on Chinese language understanding benchmarks. Baidu rebranded the Wenxin Yiyan app to "Wenxiao Yan," sharpening product positioning.

Wenxin 4.5 (March 2025) introduced Baidu's first natively multimodal model. Rather than retrofitting vision, Baidu fused image, audio, and text data during pretraining for better cross-modal understanding. Baidu claims 4.5 surpasses GPT-4 (original) in multimodal reasoning. Users can upload images or audio; the model analyzes and answers questions integrating text. This evolved Wenxin from text-only chat to a multimodal assistant. Wenxin 4.5 also strengthened Chinese text generation—Baidu touts superior creative writing compared to DeepSeek-V3 and GPT-4.5.

Wenxin X1, released alongside 4.5, targets "deep reasoning"—emphasizing inference, tool use, and autonomous refinement. It employs progressive reinforcement learning, chain-of-thought plus chain-of-action training, and multi-objective reward mechanisms, training models to introspect and plan. X1 goes beyond conversation: it calls external tools (online search, code execution, visualization) for complex problem-solving. This mirrors DeepSeek-R1 and Claude 3 directions, shifting AI from passive answering to active problem-seeking. X1 signals Baidu adopting the global trend toward "cognitive restructuring"—AI with human-like reasoning chains.

Baidu's naming uses numbers plus optional letters: numbers indicate generational progression; letters like "X" mark specialized capabilities. "Wenxin 4.5" is the baseline 4th-generation mid-cycle upgrade; "X1" is a separate reasoning track (X perhaps suggesting "expert" or "thinking"). This clarifies each model's purpose. Base versions (4.0, 4.5) suit dialogue, Q&A, and routine content; X1 suits complex reasoning and multi-tool scenarios—professional Q&A and decision support.

As China's AI leader, Baidu pursues open-source and industry deployment. Late 2024 Baidu freed Wenxin Yiyan for public use; 2025 plans include open-sourcing next-generation models. This reflects competitive adaptation: openness attracts developers; low cost lowers adoption barriers. Today, Wenxin serves diverse sectors—finance, healthcare, education—through Baidu Intelligent Cloud's "Qianfan" platform, offering customized solutions. In Chinese language and domestic applications, Wenxin leads.

Other Players: Meta and Domestic Innovators

Meta and other Chinese tech firms occupy the competitive space. Meta published LLaMA series models (LLaMA 1, LLaMA 2) in 2023. While not providing public chat services, open-source release enabled widespread academic and developer use. LLaMA 2 (7B and 70B parameter versions) became the de facto open-source standard, foundation for later projects like DeepSeek. Meta's openness indirectly spawned derivatives and improvements. In 2024, Meta reportedly develops larger models (possibly LLaMA 3) on open lines, arming the open community with tools to compete with commercial giants.

Domestically, beyond Baidu, Chinese tech companies pursue LLMs:

The landscape now spans OpenAI, Anthropic, Google, Meta as international players; Baidu, Alibaba, Tencent, iFlytek, Huawei domestically; plus thriving open-source contributions. Performance-wise, top closed-source models (GPT-4.5, Gemini) hold advantages in breadth, yet open and domestic alternatives are closing gaps, with unique edges in localization and customization.

Competition Ahead

2025's LLM landscape features simultaneous battles between incumbents and disruption by open-source challengers. For most people, tracking vendors and models requires no technical depth—life analogies suffice. Multimodal means AI "sees and hears" like people; parameter scale reflects "brain capacity."

No single model dominates all domains. OpenAI's GPT-4 series excels at conversational fluency and creative generation. Anthropic's Claude shines at long documents and safety. Google's Gemini leads in multimodal interaction and tool use. DeepSeek-family open models offer cost and flexibility. Baidu Wenxin targets Chinese speakers and domestic applications. These models will compete, each spurring the others toward better systems.

The competition is only beginning. Reading "The Singularity Is Near" years ago feels like ancient history now.

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