
Holo3-35B-A3B
H Company's open-weight sparse MoE vision-language model purpose-built for desktop computer use, scoring 82.6% on OSWorld-Verified with only 3B active parameters.
They summarize our coverage. We write it.
Newsletters like this one rebroadcast our headlines - often without the full review, the source reading, or the analysis underneath. Our weekly briefing sends the work they paraphrase, straight from the desk, before they get to it.
Free, weekly, no spam. One email every Tuesday. Unsubscribe anytime.

H Company's open-weight sparse MoE vision-language model purpose-built for desktop computer use, scoring 82.6% on OSWorld-Verified with only 3B active parameters.

Cohere's first developer-focused model - 30B sparse MoE with 3B active parameters, free Apache 2.0 license, 256K context window, and 33.4 on the AA Coding Index.

Alibaba's open-source video generation model with MoE architecture, native audio, first-and-last-frame control, and 1080p output up to 15 seconds.

Baidu's ERNIE 5.1 is a text-focused MoE model that claims the top Chinese model slot on LMArena with 800B parameters built at 6% of comparable training costs.

Zhipu AI's GLM-5.2 ships with 1M token context, 744B MoE parameters, and MIT license the day after Fable 5 goes offline - but no benchmark numbers at launch.

Z.ai's GLM-5.2 is a 744B open-weight MoE model with a 1M token context window, MIT license, and first-day support for eight coding agents at roughly 1/10th the cost of US frontier models.

Moonshot AI's Kimi K2.7-Code is a 1T-parameter open-weight MoE coding model with mandatory thinking mode, 256K context, and 30% fewer reasoning tokens than K2.6.

Moonshot AI ships Kimi K2.7-Code with 30% fewer reasoning tokens and a 21.8% gain on its own coding benchmarks, but the model still trails Claude Opus 4.8 on most tests in the same table.

Microsoft's first in-house reasoning model, a 35B-active sparse MoE with 256K context, 97% on AIME 2025, and no distillation from third-party labs.

DiffusionGemma 26B is Google DeepMind's open-weight discrete diffusion language model that generates 256 tokens in parallel, reaching 1,100+ tokens/sec on H100 - roughly 4x faster than autoregressive models of the same size.

Microsoft's first in-house coding model, a 137B sparse MoE built natively for GitHub Copilot, beating Claude Haiku 4.5 on SWE-Bench Pro by 16 points.

NVIDIA's 550B open-weight MoE model with 55B active parameters, hybrid Mamba-Transformer architecture, and 1M token context - the top-scoring US open model on the Artificial Analysis Intelligence Index.