We’re excited to announce that @ultravox_dot_ai is now part of Inworld. Ultravox is the platform developers use to build real-time voice agents. We've worked with the team for a while through our TTS partnership, and today members of the team that built it are joining Inworld to keep developing it.

Lots of discussion out there about our next model(!), so I wanted to give an early look as soon as possible. Introducing Gemini 4 Argon! It shows frontier performance in complex workflows, cyber defense and software engineering. Teams are using it extensively at Google, from coding to quantum computing, great feedback. Here’s a look at the benchmarks:

MASSIVE reveal from Google. Its new flagship, Gemini 4 Argon, outscores GPT-6 Astra and Claude Opus 5.5 on most benchmarks. - beats GPT-6 Astra and Claude Opus 5.5 on some super important industry benchmarks. - its widest lead in legal work, 19.6% on Harvey's Legal Agent Benchmark against 6.7% for Anthropic's Claude Fable 5.1. - output limit jumps from 64K to 1M tokens, an industry-leading ceiling, - Only 3 groups have it today. the first is Google's own staff, vetted cyber defenders such as government agencies and security companies and trusted testers giving Google feedback. - Inside Google, Argon agents freed over 300 TiB of data-center memory, with 500 TiB to 1 PiB of total savings estimated, and made a Rust port of the libgav1 video decoder 2.7x faster by replacing 32K lines of SIMD code.

Introducing Gemini 4 Argon – our new frontier model. It’s built for complex workflows across coding, enterprise knowledge work, and cybersecurity defense – rolling out today to a set of trusted testers through our Fairwind Program.

Lots of discussion out there about our next model(!), so I wanted to give an early look as soon as possible. Introducing Gemini 4 Argon! It shows frontier performance in complex workflows, cyber defense and software engineering. Teams are using it extensively at Google, from coding to quantum computing, great feedback. Here’s a look at the benchmarks:

We’re excited to announce that @ultravox_dot_ai is now part of Inworld. Ultravox is the platform developers use to build real-time voice agents. We've worked with the team for a while through our TTS partnership, and today members of the team that built it are joining Inworld to keep developing it.

Introducing Ideogram 4.5, the most precise edit model. With each edit, leading models add artifacts, pixel shifts, and color changes. Ideogram 4.5 eliminates artifact buildup, making multi-turn editing possible. Live in Ideogram, the API, and launch partners. Open weights soon.

We built a new way to train contextual embedding models, which encode each chunk of a document with the whole document in view. pplx-embed-v2-context-9b-preview sets a new state of the art on ConTEB and @turbopuffer's new, privately held context-bench. https://www.perplexity.ai/hub/blog/contextual-embedding-beyond-the-gold-passage

"a spec that is sufficiently detailed to generate code with a reliable degree of quality is roughly the same length and detail as the code itself" ^^ 100% don't believe in that. You should care about the code at the level of abstraction @dexhorthy is describing here. But no way in hell you can't compress it way smaller than the code itself would be (40:1 based on my measurements for a 30k LOC codebase). An LLM holds the priors for pretty much every single convention there is. Along with the cultures from Linus Torvalds to corporate Java. Two things - Understanding the model's priors for your choice of language/framework(s). - You holding those same priors. First screenshot is benchmark results from a few days ago. Second is what the 40:1 compression looks like. YMMW with typescript or python slop.