Though much of the open-source ecosystem is concentrated in China, US open models are starting to pick up steam.
On Tuesday, Google unveiled EmbeddingGemma 2, an open-source model that Google calls its "most capable" yet for on-device work, sitting at a lightweight 740 million parameters and requiring only 270 million parameters for text-only workloads. However, the model outruns its predecessor by going beyond text and can handle code, images, video and audio.
Google noted that the model offers powerful on-device performance for semantic search, routing and retrieval. For instance, when put to use, the model is capable of finding specific clips from voice memos and searching through hours of audio recordings from a text request. It also sports strong multilingual performance and is best-in-class for embedded models under 1 billion parameters.
However, Google isn't the only US company making moves in open models this week. On Monday, New York-based AI startup Reflection unveiled Beam, an open-source model built for coding, reasoning and agentic workloads.
Though far larger than Google's new model at more than 500 billion parameters, Reflection characterized its new model as "highly efficient," offering frontier reasoning at up to four times cheaper when compared to larger open-weight models like GLM-5.2. Additionally, the company said its models are approaching that of larger frontier open models such as Qwen 3.8-Max on coding and agentic tasks. Reflection noted that Beam's advantage compared to frontier open models is "efficiency at inference time."
"These results translate into more intelligence per token, delivering strong model capabilities at lower cost, making Beam a powerful workhorse model for enterprise coding and agentic workloads," the company said in its announcement.
Our Deeper View #
Though Google and Reflection's new offerings target far different workloads, these companies couldn't have picked a better time to push forward into the open model ecosystem. Frontier labs building proprietary models are trying to undercut each other on price while leapfrogging in capability, while AI costs and data sovereignty get more critical for enterprises. And while open models are praised for their cost efficiency, given that so much of open-source innovation, research and development is centralized in China, the argument against them is largely one of security. In feeding the broader US open source ecosystem, the latest models from Google and Reflection offer enterprises more choice in optimizing for cost, efficiency and safety.