AI This Week: GLM-5, Qwen3.5, and the Chinese open-source wave

China dropped two frontier-class open-source models in five days. Europe put real money behind AI sovereignty. The gap between open and closed models got a lot smaller. Busy week.

Top stories this week

GLM-5: China's first publicly listed AI company ships a frontier model under MIT

Zhipu AI released GLM-5 on February 11th. The numbers: 744 billion parameters in a mixture-of-experts architecture, 40 billion active per token. It scores 77.8% on SWE-bench Verified, ahead of Gemini 3 Pro (76.2%) and within reach of Claude Opus 4.6 (80.9%).

The model was trained entirely on Huawei Ascend chips using the MindSpore framework. Zero NVIDIA hardware. For anyone tracking the US chip export controls, this is the clearest signal yet that sanctions haven't stopped Chinese AI progress. They've redirected it.

The license is MIT, not "open with restrictions" or "research only." You can run it, modify it, sell products built on it. At roughly $0.80 per million input tokens on OpenRouter, it costs about six times less than Opus 4.6. For teams that need strong coding performance without sending data to US clouds, that changes the math completely.

Zhipu's stock surged 34% in Hong Kong after the announcement. They're China's first publicly listed pure-play AI company. GLM-5 is the proof that being public and being competitive aren't mutually exclusive.

Qwen3.5: Alibaba answers with 201 languages and rock-bottom pricing

Five days later, on February 16th, Alibaba released Qwen3.5. Another MoE model: 397 billion total parameters, 17 billion active per token, trained on text and images simultaneously through early fusion. Apache 2.0 license, fully open-weight.

Qwen3.5 supports 201 languages and dialects, up from 119 in the previous version. For anyone building AI products outside the English-speaking world, that kind of multilingual coverage used to require a proprietary model.

On price, Alibaba claims 60% lower pricing and 8x throughput compared to their previous flagship Qwen3-Max. The 1M-token context window costs roughly $0.18 per million input tokens. A different pricing tier altogether.

I've been watching the Qwen releases closely. The MoE architecture, huge model but tiny active footprint, is exactly what makes self-hosting viable. 17 billion active parameters means you can run this on hardware that wouldn't touch a dense 397B model. For a team that wants to keep its data in-house, that matters a lot.

Mistral puts 1.2 billion euros into Swedish AI infrastructure

On February 11th, Mistral announced a 1.2 billion euro partnership with EcoDataCenter to build AI compute infrastructure in Borlange, Sweden. The facility will host NVIDIA Vera Rubin GPUs and run on renewable energy. Operational target: 2027.

This is Mistral's first infrastructure investment outside France. The stated goal: a fully European AI stack, designed, built, and operated across the entire value chain, data processed and stored locally in Europe.

For context, the EU just committed to 200 billion euros in AI infrastructure as part of its sovereignty push. Mistral is positioning itself as the model provider for that stack. Given their Apache 2.0 licensing on models like Mistral Large 3, the play holds up: European governments need compute and models they can actually audit and control.

Four EU governments commit to European-made AI for public services

At a February 12th summit in Belgium, Germany, Poland, Spain, and the Netherlands committed to deploying European-made AI in public administration. This lines up with the EU's new "Buy European" policy, which prioritizes European firms in strategic sectors.

Each country is backing its own initiative: Germany with SOOFI (targeted for public sector deployment by mid-2026), Poland with PLLuM, a Polish-language model now expanding into administrative tools, Spain with Alia, running on the MareNostrum 5 supercomputer for multilingual public services, and the Netherlands with GPT-NL for Dutch-language health, education, and government applications.

These are funded programs with deployment timelines. When four of the largest EU member states commit to sovereign AI at the same time, it creates a procurement pipeline that benefits every European AI builder, Mistral included.

What does this actually mean?

The week of February 9-15 was the week open-source AI reached credible frontier performance. GLM-5 and Qwen3.5 aren't "good for open-source." They're good, full stop. Both compete with the best proprietary models on coding and reasoning, at a fraction of the cost, with permissive licenses.

For practitioners, this shifts the default assumption. A year ago, you picked open-source if you couldn't afford proprietary. Now you pick it because the performance is there and you keep control of everything else. The closed-model tax is getting harder to justify.

Europe noticed. Mistral's infrastructure play and the EU sovereign AI commitments didn't come out of nowhere. When viable open-source models exist at frontier level, the argument for European AI sovereignty stops being theoretical. You can actually build it.

What to watch next week

• Claude Sonnet 4.6 is expected this week: Anthropic's next Sonnet release should land, potentially bringing Opus-level performance at Sonnet pricing. If it delivers, the cost-performance math shifts again.
• Gemini 3.1 Pro is rumored: Google may follow with its own reasoning-focused release, potentially doubling Gemini 3 Pro's performance.
• The February model rush continues: we're already at four frontier releases this month (Opus 4.6, GLM-5, Qwen3.5, plus earlier drops). By month's end, the count could hit seven. Unprecedented density.