In-depth analysis of the development trend of AI large models at home and abroad in 2026

As of February 2026, the field of artificial intelligence large models has entered the real "strength-determined" stage from the chaotic period of "Battle of Hundreds of Models". There have been too many events that can be recorded in history in the past year: the emergence of DeepSeek R1 caused a shock in US technology stocks, OpenAI iterated from GPT-4.5 to GPT-5, Anthropic just released Claude Opus 4.6 two days ago, Google's Gemini has evolved to the third generation, Meta Llama 4 reached the top of open source and was overtaken by Ali Qwen in downloads...

This is no longer an academic competition of "who publishes the paper first", but an all-round game about computing power, ecology, commercialization and supervision. Based on the latest public information as of February 7, 2026, this article systematically sorts out the competitive landscape of global AI large models.

1. International camp: from "one super and many powers" to "three pillars"

1.1 OpenAI: product line fully rolled out

OpenAI completed its most intensive product release cycle in history in 2025:

GPT-4.5 (February 2025) — Codenamed "Orion", billed as "the largest conversation model ever". Different from the inference model, GPT-4.5 takes the unsupervised learning expansion route, which greatly improves creative writing, emotional understanding and daily conversation, and the hallucination rate is significantly lower than GPT-4o. However, it is not as good as o3-mini for mathematical and logical tasks that require deep reasoning.

o3 and o4-mini (April 2025) — OpenAI announced in February that it would cancel the independent release of o3 and integrate it into GPT-5, but then changed its strategy and released o3 and o4-mini at the same time on April 16. o3 reached 71.7% on SWE-bench and 96.7% on AIME 2025; o4-mini achieved an astonishing result of 99.5% on AIME 2025 at a very low cost (input $1.1/million tokens). Both models support native tool calls and can independently combine search, code execution, image generation and other capabilities.

o3-pro (June 2025) — Deep inference version for Pro users, enabling more reliable output through longer thought chains.

GPT-5 (mid-2025) — A landmark unified architecture product. GPT-5 integrates dialogue capabilities and reasoning capabilities, with a built-in intelligent routing system that automatically switches between "quick response" and "deep reasoning" modes according to the complexity of the task. Provides multiple variants such as gpt-5-main, gpt-5-thinking, etc., covering all user levels from free to Pro. Reaching 100% on AIME 2025 and 52.9% on the ARC-AGI-2 abstract reasoning benchmark established its leading position in comprehensive reasoning capabilities.

Sora and multi-modality — Sora video generation has moved from an experimental product to practical use, but it still has obvious shortcomings in terms of physical consistency and long video coherence, and has not yet formed a crushing advantage over competing products such as Runway and Pika.

Up to now, OpenAI's commercial moat has shifted from "technology leadership" to "product ecosystem" - ChatGPT has hundreds of millions of monthly users, Codex Cloud has achieved GA and won corporate customers such as Cisco, and its deep binding with Microsoft covers the entire line of Azure products.

1.2 Anthropic: Just set a new record two days ago

Anthropic’s 2025 is the year of acceleration:

Claude 4 (May 2025) — Includes Opus 4 and Sonnet 4 models. Opus 4 is positioned as "the world's strongest programming model" with a SWE-bench score of 72.5% and a Terminal-bench score of 43.2%, and can continue to work on complex tasks for several hours without degradation. Sonnet 4 performs well in instruction following and structured output, with SWE-bench reaching 72.7%. Claude 4 introduces the hybrid mode of extended thinking + tool invocation for the first time, as well as the official GA of Claude Code (supporting GitHub Actions, VS Code, JetBrains).

Claude Opus 4.6 (February 5, 2026) — Just two days ago, Anthropic released its latest flagship. Core upgrades include:

• 1 million token context window (Beta), this is the first time Anthropic has exceeded one million contexts
• Achieved SOTA on Terminal-Bench 2.0 and Humanity's Last Exam
• SWE-bench Verified reached 80.9%, breaking the industry record for programming ability
• Introducing Agent Teams (multi-agent collaboration in Claude Code) and Compaction (context compression technology for long tasks)
• Only 4.7% prompt injection success rate, industry-leading security

Anthropic's strategy has always been to "pay equal attention to security and capabilities" - the Constitutional AI training method ensures its high trust in the enterprise market, and Claude has become the first choice for enterprises that need to handle sensitive documents and pay attention to compliance.

1.3 Google DeepMind: Rapid iteration of three generations of Gemini

Google has shown amazing iteration speed after merging Brain and DeepMind:

• Gemini 2.0 (end of 2024) - Flash/Pro/Flash-Lite three-line layout, the Pro version was tied for the first echelon with GPT-4o and DeepSeek-R1 when it was released

Google's difference lies in: Multimodality (text, image, audio, video) is natively integrated from the training stage instead of post-stitching. This makes Gemini more natural in cross-modal understanding than competing products. In addition, Google has deeply injected AI capabilities into the entire product line of search, Gmail, Docs, and Android, forming a unique distribution advantage. Google will invest $75 billion in AI and data center expansion in 2025. This figure itself illustrates Google’s determination.

1.4 Meta: "The Empire Strikes Back" by the King of Open Source

Llama 4 (April 2025) is the culmination of Meta’s open source roadmap:

• Llama 4 Scout: 17B active parameters / 16 expert MoE, 10 million token context window (the longest in the industry), a single H100 can run

The entire Llama 4 series implements native multi-modality (early fusion architecture), which can process text, images and videos in a unified manner. Models are released as open weights through platforms such as HuggingFace, Azure, GroqCloud, etc.

But the trend changed at the end of 2025 - Alibaba's Qwen series' cumulative downloads on HuggingFace exceeded 700 million times, surpassing Llama to become the world's most popular open source AI model. The throne of the open source king changes hands for the first time.

1.5 xAI: "Violence Aesthetics" supported by SpaceX

Elon Musk's xAI takes a pure "violent expansion" route:

• Grok 3 (February 2025): million token context, AIME 2025 93.3%, GPQA 84.6%

xAI’s ambitions go beyond models—it attempts to build a vertical system from computing infrastructure (Memphis super cluster) to end applications (X platform integration).

1.6 Mistral: Flag Bearer for European AI Sovereignty

French Mistral is known for its small team but high energy. Mistral Medium 3.1 (August 2025) Supports 131K contexts, text+image input, function calls, and structured output. The Mixtral MoE architecture shows excellent cost performance under the same parameter quantity. Under the framework of the EU AI Act, Mistral is regarded as the core supporting force of "European AI sovereignty".

2. Domestic camp: from "following" to "running parallel" or even "leading"

2.1 DeepSeek: The Chinese AI dark horse that shocked the world

If there is only one most dramatic event in the AI ​​field in 2025, it must be the release of DeepSeek R1.

R1 (January 2025) — 671B total parameters / 37B active parameters MoE architecture that rivals OpenAI o1 in inference capabilities at a fraction of the training cost. More importantly: MIT license is completely open source. This news directly triggered violent fluctuations in U.S. technology stocks - Nvidia plunged about 18% in a single day, and companies such as Microsoft and Broadcom fell 7-17%. For the first time, the market has seriously re-evaluated the core assumption of "whether AI development must rely on massive investment."

DeepSeek surpassed 30 million daily active users on February 1, becoming the fastest app in history to reach that milestone. Tencent Cloud, Volcano Engine, Baidu Smart Cloud, Alibaba Cloud and other domestic mainstream cloud platforms quickly launched R1 and V3 models.

R1-0528 Update (May 2025) — A major upgrade over R1:

• Mathematics: 91.4% in AIME 2024 and 79.4% in HMMT 2025
• Programming: LiveCodeBench jumped from 63.5% to 73.3%, SWE-bench Verified from 49.2% to 57.6%
• Inference: GPQA-Diamond increased from 71.5% to 81.0%
• Reduce hallucinations, support direct JSON output and function calls

DeepSeek's core technological innovations include: Multi-Head Latent Attention (MLA) to reduce graphics memory usage, Multi-Token Prediction to accelerate generation, and large-scale reinforcement learning post-training that does not rely on manual annotation. The last point is particularly critical - HuggingFace has specially launched the Open-R1 project to reproduce its training method.

DeepSeek’s API pricing is extremely impactful: input is only $0.14/million tokens (cache hit), which almost redefines the bottom line of AI API prices.

2.2 Alibaba: Qwen series ranks first in global open source

Alibaba's Qwen has achieved a leap from "domestic leader" to "global benchmark" in 2025-2026:

• Qwen 2.5 Series (Early 2025): Complete parameter matrices from 0.5B to ultra-large scale, with excellent performance on multiple benchmarks

But Alibaba’s ambitions go beyond models. The release of Tongyi Task Assistant 1.0 marks the transformation from "chat robot" to "task executor" - deeply integrating Taobao, Alipay, AutoNavi, Fliggy and other Alibaba ecosystems, supporting 400+ digital tasks, and completing ordering, hailing a taxi, and shopping in one sentence. The number of Tongyi Qianwen users has exceeded 100 million.

2.3 Baidu: Wenxinyiyan 5.0 and 200 million monthly active users

Baidu released ERNIE 5.0 (Wenxin 5.0) at the end of 2025, its most advanced AI model that supports full-modal processing of text, images, audio and video. The business data worthy of more attention is: Baidu AI Assistant monthly active users exceeded 200 million.

Baidu's core advantage has always been the flywheel effect of "search + AI": the massive Chinese data accumulated in search feeds back model training, and the enhanced model improves the search experience. Wenxinyiyan has been deeply integrated into core products such as Baidu search, library, and network disk, and provides full-stack AI solutions through Baidu Intelligent Cloud in the enterprise market.

2.4 Dark Side of the Moon (Moonshot AI): Kimi evolves into Agent

The release of Kimi K2.5 marks the transformation of Dark Side of the Moon from "long text chat" to "Agent":

• Significantly upgraded visual capabilities: image analysis, 3D model generation

Kimi has extremely high user stickiness among students and knowledge workers, and its simple and friendly product experience is its key competitiveness. Industry analysts predict that the first AI Agent application with more than 300 million monthly users may be born in 2026.

2.5 ByteDance: Doubao’s “operating system level” ambitions

ByteDance launched Doubao Mobile Assistant in December 2025, trying to achieve deep integration of AI at the operating system level - performing complex cross-application tasks through voice commands. This strategy sparked industry controversy: Tencent’s Ma Huateng publicly praised Alibaba Tongyi’s ecological integration plan, but criticized Byte’s OS-level bean bag solution for privacy risks, and stated that Tencent would maintain a decentralized AI strategy within WeChat.

Doubao relies on massive content data from platforms such as Douyin and Toutiao. It has unique advantages in AI-assisted content creation and intelligent recommendations, but it is relatively low-key in benchmark testing of pure model capabilities.

2.6 Smart AI: From Tsinghua Laboratory to Global Open Source Power

Zhipu AI maintains an impressive release pace in 2025-2026:

• GLM-4.5 (August 2025): 355B total parameters / 32B active parameters MoE architecture, open source under MIT license, comparable to top models such as Claude and DeepSeek in reasoning, programming and tool usage, with tool usage accuracy reaching 90.6%

As a bridge connecting academia and industry, Zhipu AI’s continued open source contributions are crucial to the healthy development of the domestic large model ecosystem.

3. Technical roadmap: key divisions in 2026

3.1 Closed source vs open source: the gap is narrowing and the landscape is reshaping

2026 The key changes this year are: The open source model has matched or even surpassed the closed source model in the same period in multiple dimensions. DeepSeek R1 rivals o1, Qwen3-Max-Thinking approaches GPT-5.2, and GLM-4.5 performs on par with Claude - something unimaginable two years ago.

3.2 MoE architecture: has become the de facto standard

Hybrid Expertise (MoE) architecture has moved from "innovation option" to "industry standard":

The core value of MoE is to use a huge total parameter amount to ensure model capacity and knowledge breadth, but only activate a small part of the parameters for each inference, thus striking a balance between performance and efficiency. This architecture allows companies like DeepSeek to train state-of-the-art models at a much lower cost than expected.

3.3 Inference model: from o1 to industry-wide standard

"Inference models" will change from an exclusive innovation of OpenAI to a must-have for the entire industry in 2025:

The "slow thinking" paradigm has moved from experimentation to production. Users choose the depth of inference (such as o3's low/medium/high levels) based on task complexity, which has become a new interaction mode for AI applications.

3.4 Context window: One million tokens become admission tickets

Long context capabilities have changed from a differentiated selling point to a basic capability. Meta’s Llama 4 Scout leads the way with a context window of 10 million tokens, making it possible to handle entire code repositories, large legal document collections, and lengthy academic reviews.

4. Application implementation: the real battlefield in 2026

4.1 AI programming: the most successful commercialization scenario

The AI ​​programming assistant market will reach $11.28 billion in 2025, making it a real big business:

The industry paradigm has changed from "auto-completion" in 2023 → "multi-file editing" in 2024 → "autonomous agent" in 2025-2026 - tools that can autonomously plan multi-step tasks, edit multiple files, run terminal commands and self-correct errors.

4.2 AI Agent: The next breaking point

2026 The year is widely considered to be the "Agent Year":

The paradigm shift from "people ask questions and AI answers" to "people set goals and AI executes autonomously" is accelerating.

4.3 Other key scenarios

Enterprise knowledge base: Enterprise knowledge management based on RAG is the most mature application on the B-side, and all major cloud vendors have provided complete solutions.

Content Creation: AI full-link creation from copywriting, pictures to videos has become standard. AI video generation (Sora, Grok Imagine, Keling, etc.) will enter the practical stage in early 2026.

Scientific Research: Subsequent iterations of AlphaFold continue to promote the revolution in the biomedical field, and AI for Science has become the core direction of major laboratories.

Education and Healthcare: AI-assisted teaching and intelligent diagnosis are developing rapidly, but the extremely high requirements for accuracy and safety make the pace of implementation relatively cautious.

5. Open source ecology: the main battlefield for global competition

HuggingFace hosts more than one million models and is the central node for global AI open source. The milestone events in January 2026 are: Alibaba Qwen series surpassed Meta Llama with 700 million downloads and became the most popular open source model on the platform.

ModelScope (Model Community) continues to grow as a domestic corresponding platform, and a large number of Chinese models and data sets are gathered here.

The inference framework ecosystem is becoming increasingly mature: vLLM, llama.cpp, Ollama, SGLang and other tools have greatly reduced the deployment threshold of open source models, allowing small and medium-sized enterprises and individual developers to run advanced models on consumer-grade hardware.

6. Outlook to 2026: Five major trends

Trend 1: Agent from concept to product — Multi-agent collaboration, cross-application task execution, and operating system-level AI integration will be implemented on a large scale in 2026. The first AI Agent with over 300 million monthly active users may be born within this year.

Trend 2: Explosion of on-device AI — Advances in model compression and quantization technology enable more and more small and medium-sized models to run locally on mobile phones and PCs. Apple Intelligence, Qualcomm Snapdragon NPUs, and various on-device inference frameworks are driving this trend. Privacy protection and low latency are core drivers.

Trend 3: Open source continues to encroach on closed source — Qwen surpasses Llama’s downloads, DeepSeek subverts the narrative of “AI must burn money”, GLM-4.5 approaches the top closed source model — the potential of open source will only be stronger in 2026. But breakthroughs in the "last 5%" of cutting-edge capabilities may still require huge investments from the closed-source camp.

Trend 4: The regulatory framework is accelerating — The EU AI Act has begun to be implemented, China’s generative AI management methods continue to be improved, and the United States is also preparing federal-level AI legislation. Compliance capabilities are changing from "plus points" to "entry thresholds".

Trend 5: Computing power investment continues to expand — Google 75 billion, Amazon 100 billion+, xAI 20 billion E round... The scale of AI infrastructure investment in 2025-2026 has far exceeded the Internet bubble period, and the market is competing between "this time is different" and "history is always similar."

Summarize

As of February 7, 2026, the competitive landscape of global AI large models can be summarized as "three-level echelons, multiple routes coexist":

First echelon (with the strongest comprehensive capabilities): OpenAI GPT-5, Anthropic Claude Opus 4.6, Google Gemini 3 Pro - three pillars, each with its own strengths (GPT-5 comprehensive reasoning, Claude programming security, Gemini multi-modal science).

Second echelon (leading or catching up very quickly in specific fields): DeepSeek R1, Alibaba Qwen3, Meta Llama 4, xAI Grok 4 — among which DeepSeek and Qwen have reached the first echelon level in the open source field.

The third echelon (vertical scenario or regional strength): Baidu Wenxin, Kimi, Doubao, Zhipu GLM, Mistral - each occupy an important position in the market segment or regional ecology.

Several signals that cannot be ignored:

1. The rise of China’s AI is real. DeepSeek R1 shocked Wall Street, Qwen climbed to the top of HuggingFace, and Baidu AI reached 200 million monthly active users - this is not a narrative of "overtaking in a corner", but real technology and market data.

2. The "money burning theory" is being revised. DeepSeek has trained comparable inference models at a cost far lower than OpenAI. The MoE architecture makes giant models feasible, and quantification technology makes device-side deployment a reality. The barriers to entry for AI are lowering, but that also means competition will become fiercer.

3. From model competition to application competition. It is difficult to widen the gap based on pure benchmark test scores. The real winner lies in who can transform model capabilities into product experience that users are willing to continue to pay for.

4. Agent is the next paradigm. From Claude's Agent Teams to Alibaba's task assistant, from Cursor's Background Agent to Byte's OS-level integration - "AI autonomous task execution" is moving from demo to product.

This AI arms race is far more intense and faster than expected. And we are standing at a critical juncture: the tipping point in the transformation of AI from "impressive chatbots" to "infrastructure that truly changes the way work is done." 2026 may be the year this transition occurs.