OpenAI’s voice model doesn’t think. That’s the point.

Voice agents have a latency problem that shows up as soon as they have to do real work. Within five days, Google and OpenAI shipped two very different fixes.

On Tuesday, Google launched Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking through the Gemini API and Google AI Studio, just five days after OpenAI released GPT-Live-1. Both let a voice agent keep talking while it works in the background, but they go about it very differently.

Gemini 3.8 Live Extended Thinking keeps reasoning inside the voice model, letting it continue speaking while it executes asynchronous tool calls. OpenAI separates those jobs, using GPT-Live-1 for the real-time conversation while a backend reasoning model handles complex tasks — pushing more orchestration into the application layer.

Google cautions against treating that split as a direct comparison between Gemini and products like ChatGPT or Claude Voice.

“Today’s models are more centered on giving developers/enterprises tools to build voice agents,” a Google spokesperson tells The New Stack. “ChatGPT and Claude voice mode are full products rather than models, so the comparison is not apples-to-apples.”

“ChatGPT and Claude voice mode are full products rather than models, so the comparison is not apples-to-apples.”

Reasoning inside the session

Gemini 3.8 Live Extended Thinking keeps speech, reasoning, and tool execution inside a single stateful session, even while external API calls are still running.

When a function is set to NON_BLOCKING, Gemini can keep talking while it waits for the tool to respond, asking follow-up questions or giving updates along the way. Once the result comes back, Gemini picks up from there.

Developers can set reasoning effort to low, medium, or high per request. Standard Gemini 3.8 Live skips the extended reasoning step to cut latency and token cost.

The same underlying model also powers Gemini Live in the consumer Gemini app. Google calls that its “end-user focused offering more closely comparable to ChatGPT and Claude,” rather than the developer models themselves.

Multimodality carries over to the new audio models as well. “Visual understanding is excellent,” Google tells The New Stack, adding that users can “converse with the model seamlessly about whatever you show it.”

Gemini 3.8 Live Extended Thinking keeps speech, reasoning, and tool execution inside a single stateful session, even while external API calls are still running.

Coordinating two separate layers

With GPT-Live-1, the voice model handles the full-duplex conversation while a backend model such as GPT-6 Astra, a lighter model like Luna, or even a third-party option, handles reasoning and tool execution independently.

Keeping the backend work separate lets the voice layer stay responsive, with OpenAI putting turn-taking latency at around 800 milliseconds.

The tradeoff is that developers must coordinate the two layers themselves, passing context between the voice model and backend reasoner through sideband channels and deciding what the conversation does while background work runs. That orchestration burden falls entirely on the application layer.

Stale work when a user interrupts

Both approaches face the same headache when someone interrupts or changes their mind halfway through a request, leaving background work running that may no longer be needed.

In Gemini, that work stays within the same session, although developers have less visibility into exactly when a tool call stops. OpenAI leaves more of that cleanup to developers, who have to cancel pending jobs and make sure an outdated answer doesn’t find its way back into the conversation.

Google says Gemini has an edge under the messy conditions voice agents encounter outside a demo. The company tells The New Stack that Extended Thinking handles “background noise, heavy accents, and unexpected interruptions better than competing models.”

Per-minute costs diverge sharply

Standard Gemini 3.8 Live carries Gemini Live API rates of $0.005 per minute of audio input and $0.018 per minute of output. Extended Thinking adds reasoning tokens, with additional charges for inputs like live video and documents.

GPT-Live-1 costs $0.05 per voice minute for the front-end voice layer alone. The backend reasoning model, function calls, and external agent runs are all billed separately. As with GPT-6 Astra’s adjustable reasoning settings, developers can dial cost up or down per call, but a voice agent that regularly calls a more powerful reasoning model will see its bill climb fast.

The company also embeds DeepMind’s SynthID watermark in generated audio.

Benchmark numbers, with caveats

Gemini 3.8 Live Extended Thinking scored 82.6 on Artificial Analysis’ Speech-to-Speech Quality Index, with task completion rates of 68.6% on τ-Voice and 35.1% on Sierra’s τ-Voice-banking benchmark.

The company points directly to those results, telling The New Stack that Extended Thinking “holds the #1 spot on the Speech-to-Speech Quality Index and leads on complex task-completion benchmarks.”

GPT-Live-1, paired with GPT-6 Astra at medium reasoning effort, scored 86.2% Pass@1 on Tau3’s spoken customer-service evaluation spanning airline, retail, and telecom domains. On Full Duplex Bench, it beat GPT-Realtime-2.1 by 30 percentage points.

“Extended Thinking holds the #1 spot on the Speech-to-Speech Quality Index and leads on complex task-completion benchmarks.”

Different tests, different stacks

But these results aren’t head-to-head. Google and OpenAI used different tests and setups, and Google also notes that some comparisons put developer models up against finished consumer products.

Claude Voice isn’t part of this developer calculus. Anthropic offers voice in its consumer apps but doesn’t currently offer a real-time speech-to-speech API comparable to Gemini Live or GPT-Live-1. Developers building voice agents around Claude still have to assemble more of the voice stack themselves.

Google keeps speech, reasoning, and tool execution inside one session, cutting down on middleware but tying developers more closely to its runtime. OpenAI requires more orchestration but gives developers more control over the models and tools running behind the voice layer.

The post OpenAI’s voice model doesn’t think. That’s the point. appeared first on The New Stack.