Inworld AI announced that Meta's reasoning and coding model Muse Spark 1.3 is now live on Inworld Realtime Router, with closed weights, support for a 1M token context, and pricing of $1.25 per 1M input tokens and $4.25 per 1M output tokens. According to its disclosure, in Meta's internal coding tests, this version uses about 25% fewer tokens than Spark 1.2.
This is a big step in our work on speech-to-speech experiences, bringing speech understanding, reasoning and expressive voice generation closer together — and we are just getting started. The full article covers why we came together, what you can get today, and what's next: https://tinyurl.com/inworldultravox
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Inworld AI has released Realtime TTS-2, a new-generation realtime conversational voice model, now fully available in the Inworld API and the Inworld Realtime API.
Why it matters: The original post covers multi-turn audio context, natural-language voice instructions and a first-audio latency of under 200ms, which readers can use to judge whether realtime voice conversation fits their own projects.
Inworld announced price cuts across its entire voice stack, with most developers seeing reductions of more than half at each layer of text-to-speech, speech recognition, LLMs and compute. The new prices are already in effect for all developers. Realtime TTS-2 drops to about $10/1M characters, speech recognition to about $0.10/hour, and Router can connect to 100+ models with no markup on third-party models.
Why it matters: Inworld has lowered prices at every layer of its voice stack and published tiered per-unit rates for TTS, speech recognition and LLMs, which can be used to estimate inference costs for consumer-grade applications.
Inworld teamed up with Stream to build a reference implementation, Crashout Buddy, combining the latest speech model Realtime TTS-2 with Stream's open-source Vision Agents framework, so it can see facial expressions, hear speech and adjust its delivery in real time.
Why it matters: The post shows how to turn visual signals and emotional context into speech delivery instructions, which readers can use to judge whether this realtime voice pipeline can be plugged into their own projects.