Chinese AI Spring Festival 2026: Five Major Launches

As the Year of the Snake approaches on February 17, 2026, Chinese AI laboratories are racing to ship their latest models before the Lunar New Year holiday. Five major launches are converging in February 2026, marking what may be the most concentrated period of Chinese AI model releases ever. Zhipu AI, MiniMax, ByteDance, Alibaba, and Moonshot AI are all debuting next-generation systems that collectively challenge the dominance of US frontier models from OpenAI, Anthropic, and Google.

This pre-Spring Festival launch sprint is becoming an annual tradition in China's AI ecosystem. The holiday provides a captive audience: most of China's population takes a week off, creating prime conditions for product discovery and user acquisition. For AI labs, the timing offers maximum visibility for new releases while developers and enterprises evaluate tools for the year ahead. The stakes are higher than ever, with 2026 shaping up as the year AI agents transition from novelties to indispensable daily tools.

The Spring Festival AI Launch Tradition

The pre-Lunar New Year launch window has become China's equivalent of CES for AI announcements. In early 2025, DeepSeek disrupted the global AI landscape with its cost-efficient V3 and R1 models released around the same period. That success established a template that other Chinese AI labs are now following, turning the Spring Festival season into a coordinated push for mindshare and market position.

The 2026 edition is particularly notable for its breadth. Rather than a single standout release, five distinct labs are launching models that span language, code, image, video, and multi-agent systems. China's tech giants are also leveraging the holiday to secure users for their intelligent assistants, betting that 2026 will be the year AI agents become embedded in daily workflows. ByteDance even secured an exclusive AI cloud partnership with the CCTV Spring Festival Gala, ensuring its Doubao assistant reaches the event's massive audience. This trend toward efficient, specialized AI architectures is also visible in models like StepFun's Step 3.5 Flash, which achieves frontier performance by activating only 11B of its 196B parameters per token.

Zhipu AI GLM-5: Creative Writing and Code Generation

Zhipu AI (also known as Z.ai) is preparing to launch GLM-5, the fifth generation of its flagship language model, approximately one week before the Lunar New Year on February 15. The model targets breakthroughs in creative writing, programming, reasoning, and intelligent agent capabilities. Industry analysts expect substantial improvements particularly in intelligent writing and code generation, building on the foundation laid by GLM-4.7.

What makes GLM-5 especially significant is its training infrastructure. Zhipu AI is training the model on a massive 100,000-chip Huawei Ascend cluster, using an entirely domestic Chinese hardware pipeline. The company demonstrated this capability in January 2026 with GLM-Image, a multimodal model trained end-to-end on Huawei Ascend Atlas 800T A2 hardware using the MindSpore AI framework. This represents a meaningful step toward hardware independence from NVIDIA GPUs, a strategic priority given ongoing US export restrictions on advanced AI chips.

Zhipu AI's broader 2026 roadmap includes innovating new model architectures and scaling paradigms, along with expanded research into reinforcement learning and generalization. The company has committed to open-sourcing key models, having already released GLM-Image openly. Whether GLM-5 itself will receive open-weight availability remains to be confirmed, though Zhipu's track record suggests at least partial open-source release is likely.

MiniMax M2.2: Enterprise-Grade Programming

MiniMax plans to release M2.2 before the Spring Festival, building on the strong foundation of its M2 and M2.1 predecessors. The update focuses primarily on enhancing programming capabilities, with industry analysts describing it as a potential go-to tool for developers tackling complex coding tasks. MiniMax has built its reputation on delivering high performance at aggressive price points, and M2.2 aims to extend that advantage.

The M2 series has established itself as a serious contender in enterprise AI. The base M2 model features 230 billion total parameters with 10 billion active parameters using a Mixture-of-Experts (MoE) architecture, optimized for coding and agentic workflows. Its pricing is notably competitive: approximately $0.30 per million input tokens and $1.20 per million output tokens, representing roughly 8% of Claude 4.5 Sonnet's cost with competitive inference speed. The model can be served on as few as four NVIDIA H100 GPUs at FP8 precision.

The MIT license on the M2 series is a deliberate strategic choice. By enabling unrestricted commercial use and modification, MiniMax is positioning itself for enterprise adoption in a way that proprietary-licensed models cannot match. For businesses exploring open-source AI models for enterprise use, the M2 series represents one of the most cost-effective options available in early 2026.

ByteDance Doubao 2.0: Multimodal AI Suite

ByteDance is taking a distinctly different approach from the other labs with its Spring Festival launch. Rather than shipping a single language model, the company is releasing a coordinated multimodal suite: Doubao 2.0 (large language model), Seedream 5.0 (image generation), and SeedDance 2.0 (video generation). This trifecta reflects ByteDance's DNA as a content platform company and positions its AI stack for integrated text-image-video workflows.

The Doubao ecosystem has achieved significant scale. ByteDance reports over 50 trillion daily token usage across its large models, marking a tenfold increase year-over-year. The company has also secured an exclusive AI cloud partnership with the 2026 CCTV Spring Festival Gala through its Volcano Engine division, ensuring that Doubao reaches the event's massive audience with interactive AI features including real-time voice capabilities and character imitation.

For businesses in content marketing and social media, ByteDance's integrated approach is particularly relevant. The ability to generate coordinated text, image, and video content from a unified AI stack could streamline content production workflows considerably. While Doubao models have generally been proprietary with API access rather than open-weight, the pricing has been aggressive, with ByteDance historically undercutting competitors to drive adoption.

Alibaba Qwen 3.5: Reasoning Powerhouse

Alibaba is preparing Qwen 3.5 with a focus on reasoning and complex task capabilities, building on what is already one of the strongest open-source model families in the world. The Qwen3 series, launched in April 2025, set new benchmarks for open-source AI with its hybrid reasoning architecture. Qwen3-Max-Thinking has already demonstrated performance that matches or exceeds GPT-5.2 and Gemini 3 Pro on benchmarks like Humanity's Last Exam, signaling that Qwen 3.5 could push even further.

The existing Qwen3 model family is substantial. The series includes six dense models (0.6B to 32B parameters) and two MoE models (30B with 3B active and 235B with 22B active), trained on 36 trillion tokens. The flagship Qwen3-235B-A22B achieves leading scores across multiple benchmarks and supports 119 languages and dialects. Qwen 3.5 is expected to extend these capabilities further, particularly in complex reasoning chains and agentic task execution.

Alibaba's open-source strategy with Qwen has been aggressive and consistent. The Apache 2.0 license allows unrestricted commercial deployment, and the company has released specialized variants including Qwen3-Coder (480B parameters, 35B active) that scored 70.6% on SWE-Bench Verified. For organizations comparing DeepSeek, Qwen, and other open-source alternatives, Qwen 3.5 will be a critical model to evaluate when it arrives.

Moonshot Kimi K2.5: Agent Swarm Technology

Moonshot AI's Kimi K2.5, released in late January 2026, is arguably the most technically ambitious of the five launches. The model features a Mixture-of-Experts architecture with 1 trillion total parameters and introduces Agent Swarm technology, a multi-agent orchestration system that can coordinate up to 100 AI sub-agents working in parallel. This capability reduces long-horizon task execution time by up to 4.5x for workflows like large-scale research, long-form content creation, and batch processing.

Agent Swarm works differently from traditional multi-agent frameworks. Rather than requiring developers to predefine agent roles and pipelines, Kimi K2.5 self-directs the swarm, deciding when to parallelize, how many agents to spawn, what tools each agent should use, and how to merge results. Each sub-agent operates independently with its own tool access for searching, generating, analyzing, and organizing information. The model was trained on 15 trillion tokens mixing visual and textual data natively, enabling unified vision-language capabilities.

Kimi K2.5 offers four operational modes: Instant for quick responses, Thinking for complex reasoning, Agent for research and content creation, and Agent Swarm (currently in beta) for large-scale parallel tasks. For a deep technical analysis of the Agent Swarm architecture and practical implementation examples, see our dedicated Kimi K2.5 Agent Swarm complete guide.

Model Comparison and Competitive Landscape

Comparing these five models reveals distinct strategic positions. Each lab is targeting a different segment of the AI market, from enterprise coding to multimodal content to multi-agent orchestration. The following comparison highlights their primary differentiators as of early February 2026.

How They Stack Up Against US Frontier Models

The competitive dynamics between Chinese and US AI labs are shifting. Qwen3-Max-Thinking has demonstrated performance matching or exceeding GPT-5.2 and Gemini 3 Pro on select benchmarks. Kimi K2.5 claims advantages over Claude Opus 4.5 on agentic tasks. MiniMax M2 delivers competitive quality at a fraction of US model pricing. However, several caveats apply: benchmark performance does not always reflect real-world capability, US models generally maintain edges in safety alignment and English-language nuance, and access to these Chinese models may be complicated by geopolitical factors for some organizations.

Strategic Implications for Global AI

The concentration of five major Chinese AI model launches in a single two-week window carries significant implications for the global AI ecosystem. First, the open-source strategy adopted by three of the five labs (MiniMax, Alibaba, and Moonshot) continues to expand the pool of commercially deployable models available to developers worldwide. Organizations that previously had limited options beyond OpenAI and Anthropic APIs now have access to models with competitive performance at substantially lower costs.

Second, Zhipu AI's success in training advanced models entirely on domestic Huawei Ascend hardware suggests that US chip export restrictions are becoming less effective at constraining Chinese AI development. The 100,000-chip Ascend cluster used for GLM-5 training represents a scale that would have seemed impractical on non-NVIDIA hardware just a year ago. This has implications for both geopolitical strategy and for hardware vendors like AMD and Intel competing in the AI accelerator market.

Third, the diversity of approaches matters. ByteDance's multimodal suite, Moonshot's agent swarm architecture, and Alibaba's reasoning-focused models each push the frontier in different directions. This competition across multiple dimensions of AI capability accelerates progress industry-wide, as innovations in one lab quickly get adopted or countered by others. The broader shift toward efficient architectures, exemplified by the sparse MoE designs used in Kimi K2.5 and MiniMax M2, is reshaping what infrastructure is needed to run frontier-class models. For more on this trend, see our analysis of StepFun Step 3.5 Flash and the efficiency revolution in AI.

How Businesses Should Prepare

The rapid expansion of capable AI models from Chinese labs creates both opportunities and evaluation overhead for businesses. Here are concrete steps organizations should consider as they incorporate these models into their AI transformation strategies.

Looking Ahead

The Spring Festival 2026 AI launches mark a turning point in the global AI landscape. Chinese labs are no longer just catching up to US frontier models; in specific domains like open-source availability, agent orchestration, and cost efficiency, they are setting the pace. GLM-5 demonstrates hardware independence, MiniMax M2.2 pushes developer-first pricing, Doubao 2.0 shows multimodal integration, Qwen 3.5 targets reasoning parity with US leaders, and Kimi K2.5 pioneers multi-agent coordination at scale.

For businesses and developers, the practical takeaway is clear: the AI model market is more competitive than ever, and the best choice depends on specific use cases, budget constraints, and deployment requirements. Organizations that limit their evaluation to US-only models may be leaving significant value on the table. The Spring Festival launches are a reminder that AI innovation is a global endeavor, and the most effective AI strategies will draw from the best models available regardless of origin.

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