趋势解读：Thinking Machines Lab ships its first model and，提升开发者接入体验

Thinking Machines Lab, founded by ex-OpenAI CTO Mira Murati, has released its first AI model that processes audio, video, and text in 200-millisecond chunks, replacing rigid turn-taking with fluid, real-time conversation. The model outperforms OpenAI's GPT-Realtime-2 and Google's Gemini Live on interaction quality and latency benchmarks, pairing a fast interaction model with a background reasoning model. Despite the technical promise, the startup still faces pressure, as several key employees have recently left the company. Thinking Machines Lab has released a research preview of its first AI model, designed to break voice AI out of the traditional question-and-answer pattern. The model processes audio, video, and text in parallel 200-millisecond chunks, and the startup claims it beats OpenAI's GPT-Realtime-2 and Google's Gemini Live on interaction quality. Thinking Machines Lab has published a research preview of what it calls Interaction Models , AI models that handle interaction natively rather than through external scaffolding. The core idea is that interactivity should scale alongside intelligence, not get treated as an afterthought. Today's real-time systems like GPT-Realtime or Gemini Live continuously take in audio, but the actual language model never sees it directly. According to Thinking Machines, a "harness" of separate components sits in front of the model, including things like a voice activity detector that decides when a speaker's turn is over. Only then does the finished utterance get handed to the model, which generates a complete response. While it's talking, its perception freezes, receiving no new information until it finishes or gets interrupted. Ad These components are far less intelligent than the model itself. That means behaviors that define real conversation simply don't work, according to Thinking Machines: proactively jumping in ("interrupt me if I say something wrong"), reacting to visual cues ("tell me when I've written a bug"), or speaking simultaneously, which would be useful for something like live translation. Citing Sutton's "Bitter Lesson," the lab argues that these hand-crafted systems will eventually be outpaced by the advance of general capabilities. Ad DEC_D_Incontent-1 Thinking Machines' Interaction Models replace the harness with a model that processes the audio and video stream directly rather than receiving pre-segmented utterances. The approach resembles full-duplex models like Moshi or Nemotron VoiceChat , which work in a similarly interleaved fashion but are smaller-scale models focused on latency rather than intelligence benchmarks. The real break from existing architectures is what the team calls time-aligned micro-turns. The model continuously processes 200 milliseconds of input and generates 200 milliseconds of output, with both token streams running in an interleaved fashion. Input and output no longer happen sequentially. Instead, they share the same clock cycle. Ad This eliminates artificial turn boundaries, letting the model decide on its own whether to stay silent, interject, or speak alongside the user. Audio and images aren't preprocessed through large, standalone encoders but are fed directly into the transformer with minimal preprocessing. That saves latency, though it could also limit the model's ability to pick up fine visual details like text. The real-time model has another challenge, though. If you need to respond every 200 milliseconds, you can't simultaneously spend minutes reasoning or searching the web. Thinking Machines solves this by pairing the interaction model with a second, asynchronous background model that handles longer tasks like reasoning, tool use, and research. Ad DEC_D_Incontent-2 Both models share the same conversation context. The interaction model delegates tasks while keeping the conversation going, then weaves results from the background model into the conversation as they arrive, at a moment appropriate to what the user is curr