Gemma 4 goes Apache 2.0, Qwen3.6-Plus, GLM-5V-Turbo

Early April was an open-weights week for me, with one closed launch worth setting next to it.

Gemma 4 and the license change

Google released Gemma 4 on April 2 in four sizes. E2B and E4B are built for phones and small boards. Then there is a 26B mixture-of-experts model that activates about 3.8B parameters per token, and a 31B dense model. The license is Apache 2.0. Earlier Gemma releases shipped with custom terms, so this switch matters more to me than any chart.

Google says the 31B ranks third among open models on the Arena text leaderboard and the 26B sixth. That is Google's framing. The hardware claim is the useful part: unquantized weights for the 26B and 31B fit on a single 80GB H100, and quantized versions run on consumer GPUs. Context is 128K on the edge models and up to 256K on the bigger two, and training covered more than 140 languages. For a market where tender documents arrive in Arabic and English, that language count is a reason to test it.

I run agents against tender packs and supplier quotes. Those files are the ones I would rather not push through someone else's API. A mid-sized model with a license that lets me ship it inside a product changes what I can tell a client about where their data sits.

Qwen3.6-Plus

Alibaba's Qwen3.6-Plus also arrived on April 2, with a one-million-token window by default and a pitch built around coding agents that work across a whole repository. The scores above are Alibaba's own table, not mine. It runs through Alibaba Cloud Model Studio and Qwen Chat, and Alibaba says it works with third-party coding tools including Claude Code and Cline. To be clear, this one is not an open-weights release.

The smaller items

Z.ai's GLM-V repository logs GLM-5V-Turbo on April 2, a multimodal model aimed at vision-based coding and agent tasks. On Anthropic's platform notes, March 30 shows the batch output cap going to 300k tokens for Opus 4.6 and Sonnet 4.6. Two papers led the Hugging Face list: a survey of latent-space research and a generative world renderer.

What I'd watch next: whether anyone publishes an honest Arabic-document comparison of Gemma 4 against the Qwen line, because the leaderboard numbers will not tell me that.