Mid-2026 Large Models Landscape: The Parameter Race Is Dead—Only These Three Types of Models Have Survived

The landscape of large AI models has been completely transformed by 2026.

If the past two years were marked by an industry-wide frenzy over 'parameter scale' and 'benchmark rankings,' then by May 2026, competition among large models has quietly shifted to a new dimension. A recent in-depth research report titled 'Top Ten AI Models and Compute Challenges,' published by Xing Zhiheng, a signed expert at this publication, reveals a harsh truth:Competition in large AI models has fully transitioned from a pure 'parameter race' to a three-dimensional contest centered on 'architectural innovation + Agent capabilities + cost control.'

Amid this covert battle—intertwined with chip export restrictions, full-stack domestication efforts, and existential anxieties—the domestic large-model ecosystem is exhibiting an entirely new structure and set of trends.

Core Model Retrospective: Who Is Leading the New Wave?

1. Alibaba’s Qwen3.7-Max: Evolving from 'Conversational AI' to 'Autonomous Agent'

Unveiled at Alibaba Cloud’s summit in May 2026, Qwen3.7-Max marks a significant leap forward, with its core breakthrough lying inAgent-first design. On TerminalBench (a benchmark for real-world terminal programming tasks), Qwen3.7-Max scored an impressive 69.7, surpassing Claude's flagship model and becoming the only Chinese-developed model ranked among the global top 15 in fully blind evaluations of large language models.

Real-world tests show it possesses a formidable 'self-audit and evolution' capability. With absolutely no human intervention, it can autonomously evolve over 35 hours—writing its own code to optimize its kernel and achieving a tenfold increase in inference speed. Alibaba is aiming to position it as the 'Android of the AI era,' leveraging open-source adoption to build an ecosystem and monetizing through cloud computing power.

2. Zhipu GLM-5.1: The New Deity of Open-Source Programming

While Alibaba focuses on building a comprehensive ecosystem, Zhipu has delivered a god-tier breakthrough specifically in programming. GLM-5.1 adopts a novel DSA architecture and sparse attention mechanism, scoring 58.4 on the SWE-Bench Pro benchmark,ranking first globallyand marking the first time an open-source model has outperformed closed-source flagship models. It can run autonomously for up to 8 consecutive hours, handling the full workflow—from requirement decomposition and code writing to testing and bug fixing—and is fully open-sourced under the permissive MIT license, allowing free commercial use.

3. Kimi K2.6: King of Ultra-Long Contexts and Cost Efficiency

Moonshot AI (Kimi) continues to leverage its signature strength in ultra-long context handling (2M+ tokens). The latest K2.6 version employs a trillion-parameter Mixture-of-Experts (MoE) architecture, dramatically boosting its coding and agent-based task-planning capabilities. Although industry observers remain concerned about the profitability of startups relying on 'burning cash for growth,' Kimi has firmly retained a massive base of high-frequency users thanks to its low membership fee starting at just RMB 39 per month and its exceptional real-world applicability.

4. MiniMax M2.7: The Ultimate 'Cost Killer'

MiniMax has pursued an exceptionally differentiated strategy. Its newly released M2.7 model (with 229B open-source weights) is designed specifically for 'low-cost agents,' offering inference costs at roughly 5% of those of traditional large models. By enabling high-fidelity multi-turn editing, seamless multi-agent collaboration, and smooth support for consumer-grade hardware (such as local inference frameworks), MiniMax has turned extreme cost efficiency into its core competitive moat.

5. DeepSeek V4: A Milestone in Full-Stack Domestication and Its Dilemma

DeepSeek V4 is the world's first trillion-parameter model trained and deployed entirely on domestic computing infrastructure (Huawei Ascend CANN architecture). Its V4-Pro training cost was only USD 5.57 million—less than one-tenth that of GPT-4—once again redefining the 'cost revolution.'

However, low cost comes at a price: due to adjustments in chip architecture and engineering pipelines, DeepSeek’s iteration cycle has lengthened from once every two months to once every five months. Facing overseas models that update monthly, this slower pace is eroding its market agility.

The compute bottleneck: sufficient, yet extremely tight

On the flip side is computing capacity. Domestic leading enterprises are experiencing rapidly expanding inference compute demands, which now account for 70% of total usage. Take Alibaba as an example: U.S. chip sanctions are constraining both training and inference. While its 10,000-GPU cluster and in-house Panjiu system can support one-time training investments, mid-term inference compute remains severely constrained.

To overcome this bottleneck, the industry is accelerating the development of a 'domestic models–domestic chips' ecosystem. DeepSeek has deeply optimized for Huawei Ascend, Zhipu’s GLM-5 supports seven domestic chip types, and MiniMax is advancing hybrid compute solutions combining international and domestic chips.

Four key industry trends going forward

In summary, the large-model industry has reached the following consensus by 2026:

Architecture overtakes scale: The era of 'parameters equal supremacy' is over. Mixture-of-Experts (MoE) architecture has become the dominant paradigm, with major players aiming to achieve stronger performance using fewer activated parameters (e.g., trillion total parameters but only 32B–44B activated).

AI is evolving from a 'tool' into a 'colleague': Models are no longer just passively answering questions—they are now capable of autonomous planning and executing long-horizon tasks. Whether it’s Alibaba’s 35-hour self-evolution or Zhipu’s 8-hour continuous operation, both signal the true arrival of the Agent era.

Low cost is the ultimate competitive advantage: Computing power has become a daily consumable, and API price wars have driven per-token gross margins down to near cost levels. Whoever can lower inference costs further—such as MiniMax and DeepSeek—will survive the long haul.

Iteration speed determines survival: Market patience is shifting rapidly. A misstep in technical direction or delayed iteration will instantly lead to elimination amid the intense monthly update cycle.

The first half of the large model race was about capital and hype; the second half hinges on endurance, engineering optimization, and closed-loop commercialization. Chinese foundation models, caught between the dual pressures of computing power constraints and agent evolution, are carving out a unique and highly cost-effective path to survival.

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