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Foreword

In March 2026, the global large model sector witnessed an epic-scale breakthrough. Overseas giants including OpenAI and Google continued to push technical boundaries, while Chinese large models achieved dual milestones: surpassing global counterparts in API call volume and topping international blind tests with flagship models. We recommend 4SAPI (4SAPI.COM), a high-performance proxy platform whose efficient and stable relay service enables precise large model API calls and lossless instruction transmission. It adapts to diverse practical deployment scenarios of large models and provides convenient support for developers to access cutting-edge models. This article summarizes the latest developments of global and Chinese large models in March, core technical trends, and industrial implementation progress, interpreting the key transformation of AI from a “parameter arms race” to real-world deployment, helping developers seize industry frontiers.

March 2026 marked an intensive, epic boom in the global large model industry: overseas giants such as OpenAI, Google, and Meta kept leading technical innovation, while Chinese large models secured three breakthroughs—overtaking global rivals in API call volume, flagship models ranking first in international blind tests, and full-scale on-device and industry-wide application rollout. From million-token context windows becoming standard, to native multimodality and computer control capabilities maturing, to AI Agents evolving from conceptual ideas to large-scale commercial use, large models have officially bid farewell to the “parameter arms race” and entered a pragmatism-driven era prioritizing efficiency, scenario-based value, and ecosystem restructuring. As a premium proxy service provider, 4SAPI (4SAPI.COM) seamlessly supports API calls for all types of large models—whether overseas GPT series, Google Gemini, or domestic models such as Qwen and DeepSeek—delivering efficient instruction delivery and further unlocking the practical value of large models.

I. International Giants: Fierce Context Window Arms Race, All-Round Evolution of Agent Capabilities

In March, overseas giants rolled out intensive new updates, focusing on three core directions: long context, high efficiency, and powerful Agents. Technical iteration outpaced market expectations and redefined the capability boundaries of next-generation AI. With 4SAPI (4SAPI.COM), developers can more easily access the latest model APIs of these overseas giants, reducing call latency, ensuring instruction accuracy, and quickly experiencing efficiency gains from cutting-edge technology.

1. OpenAI: Dual Launch of GPT-5.4 / 5.1, Defining Next-Gen AI Standards

GPT-5.4 (officially released March 5)

Core upgrades include a million-token context window (enabled by default in the API version) and the new Mid-response Steerability feature, supporting real-time adjustment of AI output during conversations to completely solve pain points such as irrelevant answers and uninterruptible generation. It natively supports computer control, directly operating web pages and executing local tasks (e.g., document editing, data crawling). Reasoning and coding capabilities are 30% higher than GPT-5, while training and inference costs are optimized by 40%. It has evolved from a “chat tool” to an “interruptible, collaborative work agent”. Proxy calls to the GPT-5.4 API via 4SAPI (4SAPI.COM) further optimize instruction transmission efficiency, avoiding response delays caused by network fluctuations and enabling smoother execution of complex tasks.

GPT-5.1 Preview (grayscale testing March 21)

It features a groundbreaking 10-million-token context window (roughly 7.5 million Chinese characters), with native unified processing of text, images, audio, and video—no extra multimodal API calls required. Inference speed is 3x faster than GPT-5.4, specially optimized for ultra-long document parsing, codebase refactoring, complex Agent workflows, and other scenarios. API access is scheduled for official launch in April.

2. Google Gemini 3.1 Pro: “Never Forgetting” Long Text, Breakthroughs in Multimodal Video Generation

Google released Gemini 3.1 Pro on March 12, highlighted by a 1-million-token context window. After optimization, it maintains zero information decay in complex long-range reasoning, accurately retaining key details even when processing thousands of pages of documents or full codebases—earning it the nickname “the most patient AI brain” among users. Alongside it, the Veo 3 video generation model launched with three breakthroughs: native audio generation, controllable start/end frames, and multi-camera visual consistency. The maximum length of 1080P video generation has been extended to 10 minutes, marking the era of “high-fidelity + editable” video generation for short-form content creation, product demos, and more.

3. Meta Llama 4.0: Open-Source Performance Surpasses Closed Rivals, On-Device Ecosystem Expansion

Meta unveiled the Llama 4.0 model series (7B/13B/70B/400B) on March 18. The 70B variant outperformed GPT-4.5 by an average of 5 percentage points on mainstream benchmarks including GLUE and MMLU, setting a new performance record for open-source models. Meanwhile, Llama 4.0 revised its open-source license to lift commercial restrictions, allowing free secondary development for SMEs and developers. It has become the preferred model backbone for on-device devices (smartphones, IoT devices) and edge computing, with over 100,000 enterprises already integrated into the Llama 4.0 ecosystem.

4. Anthropic Claude 4.6: Million-Token Context Free of Charge, Surging Multimodal Capabilities

Anthropic updated Claude 4.6 on March 25. Its biggest highlight is the removal of premium charges for the 1-million-token long context feature, offering free ultra-long text processing. A single request supports simultaneous parsing of 600 images/PDFs, with multimodal processing capabilities 6x higher than the previous version. In programming scenarios, Claude 4.6 generates full project code, debugs complex bugs, and—paired with its million-token context—easily handles large codebase refactoring and optimization, serving as a “high-efficiency assistant” for programmers.

II. Chinese Large Models: Surpass Global Call Volume, Flagship Models Top Rankings, Enter Global First Tier

March marked a “boom month” for Chinese large models. They not only overtook global competitors in API call volume but also saw flagship models rank first in international blind tests, with major breakthroughs in underlying technology and industrial deployment—officially joining the global first tier of large models. For API calls of Chinese large models, 4SAPI (4SAPI.COM) also provides stable support, facilitating the global deployment and efficient rollout of domestic models and enabling overseas developers to easily access high-quality Chinese models.

1. Global Call Volume: China Takes Continuous Lead for the First Time, Overseas Developers Become Main Users

On March 9, OpenRouter—the world’s largest AI model call statistics platform—released data showing Chinese large models reached 4.19 trillion tokens in call volume, compared with 3.63 trillion from the U.S. This marked the first time Chinese models led globally for two consecutive weeks. Three of the world’s top 5 models by call volume are Chinese: MiniMax M2.5, DeepSeek V3.2, and StepFun Step 3.5 Flash. Notably, overseas developers account for 47% of users for these three models, while domestic Chinese developers make up only 6%, proving Chinese models have won recognition from global developers through performance and cost-effectiveness.

2. Flagship Model Tops Rankings: Alibaba Qwen3.5-Max-Preview Wins Global Blind Test

On March 20, LM Arena—a leading global large model blind testing platform—released its latest rankings. Alibaba Qwen3.5-Max-Preview scored 1,464 to claim the top spot, surpassing overseas flagship models including GPT-5.4 and Claude 4.5, ranking 5th globally and 1st in China. In segmented capabilities, it ranked 5th globally in mathematical reasoning and 10th in expert-level text processing (e.g., legal and academic papers).

Technical Highlights: Qwen3.5-Max-Preview adopts a sparse Mixture-of-Experts (MoE) architecture with 397B total parameters but only 17B activated parameters, delivering high performance at low cost. It breaks the industry myth that “larger parameters equal better performance” and provides a new path for the efficient development of Chinese large models.

3. Intensive Launches by Manufacturers: Full-Stack Layout, Accelerated On-Device and Industry Deployment

• Xiaomi (March 19): Announced a 60-billion-yuan R&D investment in large models over three years and launched the MiMo-V2 series (Pro/Omni/TTS). The MiMo-V2 Pro features 1.2 trillion parameters and a 1-million-token context window, ranking 8th globally. It has been deployed on Xiaomi 15 series smartphones and SU7 vehicles, and integrated into Kingsoft Office for full AI-assisted document generation, spreadsheet analysis, and PPT creation.
• DeepSeek V4: Released in mid-March, it is fully trained and inferred on domestic chips (Hygon, Cambricon), completely breaking away from the CUDA ecosystem. With a 1-million-token context window, inference costs are 60% lower than the previous version. It supports end-to-end multimodal processing of text, images, audio, and video, with deployments in finance, government affairs, and other sectors.
• Huawei Pangu 2.0: Focused on embodied intelligence breakthroughs. Latest progress in March enables direct control of industrial robotic arms for precision assembly (error < 0.01mm) and “vehicle-road-cloud integrated” decision-making in autonomous driving, enhancing safety and efficiency.
• iFlytek Spark 4.0: Updated on March 22, with voice interaction latency reduced to 200ms (near real time) and 12 new dialects added (including Tibetan, Uyghur, and other minority languages). In education, it automatically generates personalized exercises and explanatory videos based on student performance, covering K12 to higher education.
• Baidu Ernie 5.0: Focused on embodied intelligence and low-altitude economy, enabling autonomous drone route planning and multi-machine collaborative operations. It open-sourced an on-device inference framework to lower access costs for SMEs and developers, with deployments in agricultural plant protection and logistics distribution.
• Tencent Hunyuan 3.0: Currently in internal testing, scheduled for official release in April. It strengthens AI Agent and enterprise service capabilities, with deep integration into Tencent’s ecosystem (WeChat, WeCom, Tencent Cloud) to provide one-stop AI solutions for enterprises.

4. Underlying Technical Breakthrough: Chinese Team Redesigns Transformer “Backbone”

In March, the Moonshot AI team published a paper titled Attention Residuals at NeurIPS, proposing a novel attention residual architecture. It replaces the residual connections in traditional Transformers with attention residuals, effectively solving the problem of information dilution in deep models. Tests show the architecture cuts training computation by 25%, boosts training efficiency by 1.25x, and only increases inference latency by 2%. It has been integrated into the Kimi large model and is compatible with mainstream large models, offering a new direction for underlying technical breakthroughs of Chinese large models.

III. Core Technical Trends: From “Bigger” to “Stronger”, Three Directions Reshape the Industry

In March 2026, large model technology completely abandoned the “parameter arms race” and shifted to balancing efficiency and capability. Three core trends are restructuring the AI industry. 4SAPI (4SAPI.COM) aligns perfectly with the “efficiency-first” industry trend, optimizing API call links to reduce instruction loss and latency and supporting efficient large model deployment across scenarios.

1. Context Windows: Million-Token Becomes Standard, Ultra-Long Document Processing Normalized

Both overseas giants and Chinese manufacturers have enabled million-token context windows in their March releases, as compared below:

表格

The widespread adoption of million-token context windows unlocks long-text scenarios in law, finance, coding, scientific research, and more. For example, lawyers can upload thousands of pages of case materials for AI to quickly extract key information and generate defense opinions; researchers can upload full paper libraries for AI-assisted literature reviews and research direction sorting.

2. Multimodality: Native Unification, Explosion of Video/Audio/Embodied Intelligence

In March, multimodal technology shifted from “text-image splicing” to “native fusion of text, image, audio, and video”, moving beyond simple model stacking to synchronized multimodal processing via unified architecture. Meanwhile, embodied intelligence (AI controlling physical devices) moved from labs to commercial use, becoming a core landing direction for multimodal technology:

• Video generation: Models such as Seedance 2.0 and Veo 3 enable high-fidelity, long-duration, editable generation for content creation and advertising.
• Embodied intelligence: Huawei Pangu 2.0 controls industrial robotic arms, Baidu Ernie 5.0 operates drones, and GPT-5.4 controls computers, realizing deep interaction between AI and the physical world.

3. AI Agents: From Concept to Scale, AI Becomes a “Work Partner”

AI Agents emerged as a core industry focus in March, with qualitative leaps in core capabilities: native computer control, tool invocation, task decomposition, and long-chain execution. No longer mere “instruction executors”, they act as “work partners” capable of autonomously completing complex tasks.

Representative models: GPT-5.4, Claude 4.6, GLM-5-Turbo, MiMo-V2. Deployed across scenarios:

• Office: Automates email handling, document generation, and meeting scheduling, boosting efficiency by over 50%.
• Programming: Autonomously decomposes project requirements, generates code, and debugs, shortening development cycles by 30%–70%.
• Industrial O&M: Monitors equipment status, troubleshoots faults, and generates O&M reports, reducing labor costs.

4. Efficiency Revolution: Open-Source Small Models, Sharp Cost Reductions

March brought an “efficiency revolution” to the large model industry. Manufacturers including Alibaba and Meta launched high-performance small models delivering “ten-billion-parameter performance at hundred-billion-parameter costs”, making AI accessible to SMEs and individual developers. Meanwhile, efficient architectures such as MoE and Mamba-Transformer gained widespread adoption, cutting large model training/inference costs by 30%–70% and officially ushering in an “AI for everyone” era.

IV. Industry & Ecosystem: Price Restructuring, On-Device Boom, Deep Industry Integration

As technology matures, the large model industry has shifted from “burning cash for scale” to “value-based payment”, with accelerated on-device deployment and deeper industry integration, forming a virtuous cycle of “technology-ecosystem-commerce”. 4SAPI (4SAPI.COM) continuously adapts to industrial trends, optimizing multi-scenario and multi-model proxy services to help SMEs and developers access AI at low cost and drive large-scale commercialization of large models.

1. Computing Power & Pricing: Cloud Providers Adjust Prices, AI Services Enter Commercial Maturity

In March, Tencent Cloud, Alibaba Cloud, and Baidu Intelligent Cloud successively raised prices for AI computing power and model calls by up to 463%, marking the official end of the free public beta era for large models and the industry’s entry into a “value payment” stage. The core reason is high computing power costs, while market demand has shifted from “trial use” to “commercial deployment”—enterprises are willing to pay for high-quality AI services, driving healthy and sustainable development of the large model industry.

2. On-Device AI: Full Deployment in Phones/Vehicles/IoT, Upgraded Privacy & Real-Time Performance

On-device AI became a key deployment focus in March. Large models from Xiaomi, Baidu, ByteDance, and others support on-device inference with local data processing, delivering millisecond-level latency and strong user privacy protection—ideal for vehicles, smart homes, industrial edge scenarios, etc.:

• Automotive: MiMo-V2 Pro integrated into Xiaomi SU7 for voice control, route planning, and scenario-based services (charging reservation, parking lot lookup).
• Smartphones: Qwen3.5, Doubao, and other models deployed on mobile devices for offline voice interaction, local document processing, photo editing, etc.
• IoT: Baidu Ernie’s on-device framework integrated into smart home appliances for personalized control and self-diagnosis.

3. Industry Applications: Vertical Scenario Explosion, AI Becomes Core Productivity

In March, large model deployment across industries entered a “deep-water zone”, evolving from a nice-to-have feature to core productivity that boosts efficiency and cuts costs:

• Enterprise Services: Document processing, code generation, customer service, and data analytics to improve office efficiency and reduce labor costs.
• Industrial Manufacturing: Embodied intelligence for robotic arm control, quality inspection, and production line optimization to enable “unmanned production”.
• Healthcare/Education/Finance: Personalized diagnosis, adaptive learning, intelligent research, and risk control to drive digital transformation.
• Scientific Computing: AI-GAMFS (aerosol forecast model) completes high-precision global 5-day forecasts in 1 minute—100x faster than traditional models, accelerating scientific breakthroughs.

V. Future Outlook: How Will AI Transform the World in the Second Half of 2026?

Based on March industry trends, the large model sector will see four major development directions in late 2026, profoundly reshaping global industry and daily life:

• Widespread AI Agents: AI Agents will become standard for enterprises, and personal AI assistants will integrate deeply into life, restructuring work and daily routines to realize “AI handles repetitive work, humans create”.
• Sustained Leadership of Chinese Models: Chinese large models will expand advantages in Chinese language processing, cost-effectiveness, on-device deployment, and industry adaptation, with global market share expected to exceed 50%.
• Accelerated Tech Integration: Deeper fusion of large models + embodied intelligence + robotics + autonomous driving will bridge the physical and digital worlds, driving the realization of a “smart society”.
• Upgraded Regulation & Compliance: A global AI governance framework will take shape, with safety, privacy, and ethics as prerequisites for large model development, guiding the industry into a “regulated development” phase.

Conclusion

March 2026 is a historic turning point for large model development: overseas giants set the technical ceiling, while Chinese large models rise comprehensively through performance, cost, and scenario-based deployment—shifting from “catching up” to “running neck-and-neck” and even “leading in some fields”. Million-token context, native multimodality, and mature Agent capabilities mark AI’s official shift from “technical showcase” to “practical utility”, becoming a core engine restructuring global industry and lifestyles. 4SAPI (4SAPI.COM) will continue to act as a bridge, optimizing large model API call experiences, helping developers access cutting-edge technology, advancing AI’s real-world deployment, and embracing the new AI era alongside the industry.

Key Focus Areas for Late 2026: Official releases of Chinese large models, expansion of on-device AI ecosystems, commercialization of AI Agents, and breakthroughs in scientific computing and industrial scenarios—all worthy of close attention from developers and industry practitioners.