Mistral released Mistral Small 3, a latency-optimized 24B dense model under Apache 2.0 — both pretrained and instruction-tuned checkpoints, 32K context, native function calling and JSON output, with strong system-prompt adherence.

It scores above 81% on MMLU at roughly 150 tokens per second, and Mistral claims parity with Llama 3.3 70B at three times the parameter count while being more than 3x faster on the same hardware; quantized, it fits a single RTX 4090 or a 32GB MacBook. Notably it was trained without RL and without synthetic data, positioning it as a base for reasoning work rather than a finished reasoner. Mistral simultaneously recommitted to Apache 2.0 for general-purpose models, moving away from its restrictive MRL license.

Italy's data protection authority imposed an immediate and definitive limitation on DeepSeek's processing of Italian users' personal data after the two Chinese companies behind the app answered its questions in a way it called totally insufficient and argued the GDPR did not apply to them. The app had already disappeared from Italian app stores the previous day.

The same week, OpenAI told the FT it had evidence DeepSeek used its proprietary models to train R1 via distillation — a terms-of-service violation — and the White House AI and crypto czar said there was substantial evidence it had happened. On 29 January Anthropic CEO Dario Amodei published an essay arguing DeepSeek's cost figures were on-trend at best rather than a discontinuity, that R1 essentially replicates OpenAI's o1 RL approach, and that cheaper training makes chip controls more important because million-chip clusters cannot be smuggled the way thousands can.

Wiz Research, assessing DeepSeek's external security posture days after R1 went viral, found a ClickHouse database on DeepSeek subdomains reachable from the open internet with no authentication at all.

It allowed full database control and potential privilege escalation inside DeepSeek's environment, and held over a million lines of log streams including plaintext chat history, API keys, backend hostnames and operational metadata. Wiz disclosed responsibly and DeepSeek secured it the same day.

Alibaba announced Qwen2.5-Max, a large-scale MoE pretrained on more than 20 trillion tokens and post-trained with curated SFT and RLHF, available only through the Alibaba Cloud API — no downloadable weights. Qwen claims it outperforms DeepSeek-V3 on Arena-Hard, LiveBench, LiveCodeBench and GPQA-Diamond.

Two days earlier Qwen had open-sourced Qwen2.5-VL in 3B, 7B and 72B sizes, a vision-language model that acts as a visual agent able to direct tools and perform computer and phone use without task-specific fine-tuning, understand videos over an hour with event localization, emit bounding boxes and points, and produce stable JSON structured output. Qwen2.5-VL-7B is claimed to beat GPT-4o-mini on several tasks.

Nvidia closed down about 17% on Monday, shedding close to $593 billion in market value — the largest single-day market-cap loss ever for a US company — while AI-exposed semiconductor, power and infrastructure shares collectively dropped more than $1 trillion as investors repriced the assumption that frontier AI requires ever-larger GPU fleets.

The trigger was DeepSeek's free assistant, built on the openly downloadable R1 model and claimed to be trained for a fraction of incumbent cost. The app hit number one on the US App Store, up from number 31 days earlier, and was top free app in the US and 51 other countries, with downloads doubling from 1M to 2.6M in days. Trump called it a wakeup call; Altman called the model impressive. Nvidia rebounded 8.9% the next day.