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China's Qwen Just Dethroned Meta's Llama as the World's Most Downloaded Open Model

By Tyler Casey · AI-assisted research & drafting · Human editorial oversight
@getboski

The numbers don't lie. In 2025, Qwen became the most downloaded model series on Hugging Face, ending Meta's Llama reign as the default choice for open-source AI. According to MIT Technology Review, Qwen accounted for over 30% of all model downloads on the platform, surpassing every competitor by a significant margin. By August 2025, more than 40% of new language model derivatives on Hugging Face were built on Qwen. For years, Western observers dismissed Chinese AI development as a game of catch-up. That assumption no longer holds.

The shift in open-source AI dominance represents more than a changing of the guard. It signals a realignment in how the world builds, deploys, and iterates on large language models. Chinese labs are releasing models faster than their Western counterparts can benchmark them. This velocity advantage compounds over time, and developers who once defaulted to Llama now face a genuinely competitive field with multiple viable alternatives.

The Download Numbers Tell the Story

Hugging Face download statistics from 2025 reveal the new hierarchy: Qwen at number one, followed by Llama, then GPT-OSS. This ranking reflects actual deployment patterns, not benchmarks or press releases. Developers vote with their downloads, and their votes shifted decisively toward Chinese models over the past eighteen months.

Qwen's 30%+ share translates to millions of individual model pulls every month. The Alibaba-backed series achieved this through aggressive model releases, strong multilingual capabilities, and performance that rivals closed-source competitors. The Qwen2.5 family alone includes models ranging from 0.5 billion to 72 billion parameters, covering virtually every use case from edge deployment to enterprise reasoning. This model diversity addresses real deployment constraints that monolithic model families often ignore.

The Qwen2.5 Technical Report (arXiv:2412.15115) details what drove the improvements: pre-training data scaled from 7 trillion to 18 trillion tokens (a scale that puts increasing pressure on the training data supply ceiling), supervised fine-tuning with over 1 million samples, and multistage reinforcement learning. On MMLU, the 72B variant scores 86.1, up from 84.2 on Qwen2. The 7B model hits 74.2 on MMLU and 57.9 on HumanEval. These aren't incremental bumps. They represent systematic improvements across the entire model family.

Meta's Llama, long the default choice for open-source projects, now faces genuine competition for the first time since its introduction. Llama 3.x remains formidable and widely deployed, but the download gap widened throughout 2025 with no signs of reversing in early 2026.

The era of a single dominant open model family is over. Developers vote with their downloads, and their votes shifted decisively toward Chinese models.

Performance Parity with Closed-Source Leaders

Download statistics matter, but performance benchmarks reveal the capability gap that justifies those adoption decisions. The gap between open-source and closed-source models has narrowed dramatically, with Chinese open-source releases now matching top-tier commercial systems on most standard benchmarks.

Qwen3-235B, DeepSeek V3.2, and GLM-4.7 all achieve GPT-4 class performance. January 2026 rankings from WhatLLM place these models in the same quality tier as proprietary leaders from OpenAI, Anthropic, and Google. The performance delta that once justified paying for closed-source API access has largely evaporated for many use cases. The gap between the best open-source model and the proprietary leader has shrunk from 15-20 points in October 2024 to roughly 9 points, with parity projected by mid-2026.

GLM-4.7 from Zhipu AI demonstrates particularly impressive results on agentic coding benchmarks. The model matches Claude Sonnet 4.5 and GPT-5.1 on SWE-bench and similar evaluations that test a model's ability to autonomously fix code. This matters because agentic coding represents the frontier of practical AI utility, not just raw reasoning capability. A model that can reliably implement software changes without human intervention creates actual economic value.

A July 2025 paper, "Open-Source LLMs Collaboration Beats Closed-Source LLMs," showed that integrating fifteen open-source LLMs in a multi-agent system outperformed Claude-3.7-Sonnet by 12.73% and GPT-4.1 by 5.36%. The closed-source moat isn't just shrinking. On collaborative tasks, it may already be gone.

What the Headlines Miss

Before accepting a simple narrative of Chinese AI dominance, several caveats deserve attention. Download statistics measure adoption, not deployment success or satisfaction. A model downloaded a million times might be abandoned after experimentation when developers hit production limitations. We don't have reliable data on how many downloads translate into systems that deliver sustained value.

The open-source definition itself has become contested territory. Some Chinese models ship under licenses that restrict commercial use or impose constraints that don't align with traditional open-source principles. Developers evaluating these models for enterprise deployment need to carefully examine licensing terms that may differ significantly from the permissive licenses common in Western open-source projects.

There's also the question of training data and regulatory compliance. Western organizations deploying Chinese models face unanswered questions about what data these models were trained on and whether that training complies with GDPR, CCPA, and other data protection regulations. The legal environment for cross-border AI deployment remains unsettled, creating risks that download statistics and benchmark scores don't capture but that enterprises can't afford to ignore.

And benchmark performance, while impressive, doesn't tell the full story. As we documented in The Benchmark Trap, high scores on standardized evaluations often indicate optimization against known test sets rather than genuine capability transfer. Models that score well on standardized tests can still fall short on domain-specific tasks, long-context reliability, and the kind of instruction-following precision that production systems demand. The gap between "benchmark competitive" and "production ready" remains significant for many use cases, a challenge that extends across the entire AI deployment landscape.

Integrating fifteen open-source LLMs in a multi-agent system outperformed Claude and GPT-4. The closed-source moat may already be gone.

What This Actually Changes

The real story isn't that Chinese AI has surpassed Western alternatives in some absolute sense. It's that the open-source AI field has become genuinely competitive for the first time since Llama's introduction changed the game. Developers now have meaningful choices among models from different regions, organizations, and development philosophies. This competition benefits everyone because it creates pressure for continuous improvement across the entire field.

For enterprises evaluating open-source options, the expanded field creates both opportunities and complexities. Model selection now requires evaluating Chinese options alongside Western alternatives, understanding different licensing regimes, and assessing trade-offs between performance, compliance, and supply chain risk. The decision is no longer simple, but the available options are better than they've ever been.

The competitive pressure from Chinese labs has already forced Western players to accelerate. Meta can't afford to let Llama fall further behind. Google has expanded its Gemma family. Mistral and other European labs are pushing boundaries. This is what competition looks like when no single player holds a permanent advantage, and the ultimate winners are developers who now have access to capabilities that were exclusive to well-funded labs just two years ago.

The era of a single dominant open model family is over. What comes next depends on whether the industry treats this as a horse race between nations or as what it actually is: a rising tide of openly available AI capability that makes every builder more powerful, regardless of which flag flies over the lab that trained the weights.

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