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Grok Voice Think Fast 2.0

SpaceXAI · Released Jul 2026

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Grok Voice Think Fast 2.0 is SpaceXAI's next-generation speech-to-speech model with a parallel reasoning architecture that makes it substantially smarter than conventional voice models with no latency penalty. Its transcription accuracy destroys dedicated STT competitors in noisy environments — the deciding factor for production voice agents. But at $0.08/min, developer/API-only with no consumer-facing assistant, it's a premium specialist — not for cost-sensitive or general-purpose voice use.

Is it right for you?

Good for

  • Parallel reasoning during speech — the model thinks while it speaks, improving intelligence without latency penalty
  • Transcription in noisy environments — 10x advantage over dedicated STT models when background noise is present
  • Voice agent deployment at scale — 25+ languages, 21 voices, no-code Voice Agent Builder for production

Not good for

  • Consumer voice assistant use — model is developer/API-only, no end-user conversational product like ChatGPT Voice
  • Cost-sensitive high-volume telephony — at $0.08/min with $0.01/min telephony surcharge, budget STT-only pipelines are cheaper

How it performs by task

Speech-to-speech conversation

Excellent

SOTA parallel reasoning with 82.9% benchmark score, leading the category

Transcription accuracy

Excellent

1.5-2x better than dedicated STT models, 10x in noisy settings

Multilingual voice agents

Very Good

25+ languages with 21 voices, strong but real-world quality varies by language

Cost-effectiveness for high-volume

Good

$0.08/min premium pricing — dedicated STT+TTS pipelines are cheaper at scale

Pricing

Input

$0.08 / min of audio (speech-to-speech)

Output

Included in speech-to-speech rate

Context

API-only

View full pricing

Benchmarks

BenchmarkScoreSource
Artificial Analysis speech-to-speech82.9% Source
Artificial Analysis speech-to-speech (v1.0)75.7% Source
Conversational Dynamics (Full Duplex Bench)95.1% Source

No verdict changes yet

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Verification log

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How we evaluate