open-weights

DeepSeek's peak/off-peak billing takes effect today: V4-Pro output up 4.5x at peak, cache-hit input up as much as 12x. It lands a fortnight after OpenAI cut Luna 80%. The price war was never a race to zero — it is convergence toward the true marginal cost of intelligence, approached from both directions at once.

Qwen3.8-Max's weights went live today under a license that charges companies above $50M in revenue. Kimi K3's asks up to 30% above $20M. Meta's Glimmer is Apache-clean. 'Open weight' just fractured into regimes — this is the map of who pays.

Meta returned to open weights with Muse Glimmer, a 30B model under Apache 2.0, wrapped in a manifesto arguing that distribution is safety. Five days earlier, Meta disclosed its own model had hacked another company. The essay is best read against that timeline.

In twelve days, Chinese labs pushed the capability floor toward zero, Anthropic halved the price of the frontier, and Google cut Flash tokens again. Read together, the fortnight says one thing: the capability premium is over, and margin is the only battleground left. A ledger of who pays.

In roughly fourteen days, four Chinese labs shipped or previewed near-frontier open-weight models — LongCat-2.0, Kimi K3, Qwen 3.8-Max, GLM-5.2. No US lab matched the density. But 'open' is a spectrum being gamed, so this is a map that grades actual openness, not press-release openness.

On July 17, Xi Jinping used his first WAIC keynote to make open-source AI a matter of Chinese foreign policy — a global public good, a new cooperation body, a pitch to the Global South against Washington's gate. The Western reflex is to call it propaganda. The reflex misses what makes it work: the models are real, and they shipped the same fortnight.

Moonshot AI shipped a 2.8-trillion-parameter model it calls the world's first open 3T-class model — while reportedly raising at a $30 billion valuation. The interesting number isn't the parameter count. It's that a startup pitching investors on scarcity gave its best asset away.

In five days, an Ant Group affiliate open-sourced an entire robot brain — perception, depth, world model, video-action — while BAAI shipped a world model that learns robot control without action labels and Mistral entered robotics for the first time. The physical-AI frontier moved to open weights, and almost none of it came from a US lab. A map of the week that reset embodied AI.

While Washington spent June turning frontier model releases into licensed events, a Chinese food-delivery company open-sourced a 1.6-trillion-parameter agentic-coding model under an MIT license — trained start to finish on domestic chips, no Nvidia silicon involved. LongCat-2.0 is the counter-move to the export-control regime, and it's already downloadable worldwide.

On June 16, GLM-5.2 became the leading open-weight model on the Artificial Analysis Intelligence Index v4.1 at **51**, ahead of every Google model and **3.4 months** behind the equally-capable closed model. The smallest gap on record is now **2.2 months** (DeepSeek V4 Pro). The peak was **9.8 months** in December 2024. The reasoning frontier the closed labs owned outright is now a quarter-year head start, priced in the open.

On June 12 a Commerce letter switched off two Anthropic models worldwide. Strip the news and a harder question remains: can you export-control software at all? America tried once before, on encryption, and lost in court. This control fails on the same three layers.

The lab that refused capital is closing a $7.4B first round at up to $59B. Why a battery maker leads it, why Tencent's check is defensive, and why Liang kept 40%.

In a three-week window, MiniMax M3, Qwen3.7-Max, DeepSeek V4, Kimi K2.6 and Nemotron 3 Ultra all claimed the frontier. The third-party leaderboards say the gap is real and small. The marquee numbers are mostly the labs grading their own homework. And the open-weight crown carries a jurisdiction risk no benchmark measures.