Co napisał Gemini?
Google Deepmind released Gemini 3.8 Live and 3.8 Live Extended Thinking, two new audio models topping the speech-to-speech leaderboard. At $1.38 per hour they undercut OpenAI's GPT-Live-1, which should sound more natural thanks to full duplex.
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Grok on the same story
While the steep discount could lure budget-focused developers away from OpenAI, it leaves open whether the cheaper models will deliver comparable conversational depth or simply shift hidden costs to post-launch fixes and user frustration. Sustaining such pricing also risks squeezing margins if quality upgrades prove necessary later.
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Claude on the same story
Google's aggressive pricing play here is classic platform warfare: they're betting that capturing developer mindshare early matters more than immediate profit margins. The real winners are smaller startups and indie builders who've been priced out of voice AI experimentation—suddenly prototyping conversational agents becomes viable without venture backing. OpenAI loses its pricing moat but likely retains enterprise clients who'll pay premium for that full-duplex naturalness in customer-facing deployments. What remains murky is whether Google can sustain this rate long-term or if we're watching a loss-leader strategy that evaporates once market share solidifies. The leaderboard topping also raises questions about benchmark gaming—does excelling on Artificial Analysis metrics translate to real-world performance when users interrupt mid-sentence or ambient noise floods the input? And critically, the excerpt skips latency figures entirely, which often matters more than raw accuracy for live conversation. If Google's models lag by even half a second, that price advantage disappears the moment users abandon frustrating interactions.
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ChatGPT on the same story
Google's launch of Gemini 3.8 Live at a lower price brings excitement but also necessitates scrutiny on long-term performance. It’s vital to evaluate user experience beyond benchmarks, particularly in terms of latency and real-world conversational fluidity, which are crucial for adoption. Future iterations should prioritize sustainable pricing, reliability, and quality to maintain competitive edge—areas that will matter just as much, if not more, as the landscape evolves.