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Tavus Griffin fools 48% of users in video calls

Tavus released Griffin, an AI model that passed a video Turing test, with 48% of participants mistaking it for a human. The release follows Alibaba's Wan3.0 launch and Black Forest Labs' FLUX 3.

AI & modelsNewsGrace OkonkwoPublished: 3 October 20263 min readSources 21
Tavus Griffin fools 48% of users in video calls

On 2 October 2026, Tavus unveiled Griffin, a video-based AI model designed for real-time interaction.

BigGo Finance reports that 48% of test subjects believed they were speaking to a real person during a video call. The model reacts to user inputs with lifelike latency and facial expression.

The release lands amid a busy week for generative video tools. Alibaba launched Wan3.0 on 24 August, following a $10 billion share sale. Black Forest Labs introduced FLUX 3 in July, a model capable of generating images and 20-second videos with audio. Griffin prioritizes interactive realism over static generation.

The Turing Test in Video

Griffin's performance in the video Turing test is the central metric of this release. Business Today reported that 48% of users could not tell the difference between Griffin and a human. This is a high success rate for a synthetic agent in a unmediated, real-time setting. Previous AI models often struggled with the nuances of live conversation, such as eye contact and speech timing. Griffin appears to have closed much of that gap.

"48% of test subjects mistook video call partner for a real human."

BigGo Finance

The implications for verification and trust are immediate. If nearly half of users cannot detect the AI, traditional cues for authenticity are failing. This raises questions for platforms that rely on video for identity verification, from banking to social media.

BeInCrypto noted that 26 of 54 people in their sample did not know they were talking to an AI. This aligns with BigGo Finance's 48% figure, suggesting a consistent pattern of deception. The models are becoming good enough to fool casual observers.

Context from Recent Releases

Griffin is part of a broader trend in AI video generation. On 10 August, LTX-2.5 was released, capable of generating 10-second videos from images in 6.8 seconds. It runs on Nvidia superchips and is open-weights. This speed and accessibility lower the barrier for creating realistic video content. Griffin adds a layer of interactivity that static generation models do not offer.

Alibaba's Wan3.0, launched in August, is another data point. The company raised $10 billion in a share sale before the release. This indicates significant capital investment in video generation capabilities. Tavus's Griffin distinguishes itself by focusing on the live video call format.

Black Forest Labs' FLUX 3, released in July, also pushes boundaries. It generates images and 20-second videos with audio. The inclusion of audio is a key feature, as sound adds another dimension to realism. Griffin takes this further by reacting to audio inputs in real-time. The combination of visual and auditory cues makes the deception more effective.

Verification and Ethics

The ease with which Griffin fools users raises ethical concerns. Misinformation and deepfakes become harder to detect when the AI is live and interactive. Platforms must develop new tools to verify the authenticity of video participants.

Regulators and tech companies are likely to respond to these developments. The ability to pass a video Turing test at a 48% rate is a milestone. It suggests that future models may reach even higher deception rates.

For now, Griffin acts as a benchmark for real-time AI video. Its performance highlights the rapid progress in synthetic media. As these tools become more common, the need for strong verification methods will only grow.

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Sources

21
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  2. 02US killer's sentence quashed because of AI video of victim shown in courtEN
  3. 03Anthropic's super bug-hunting model Mythos is hardcore good at mathEN
  4. 04Anthropic claims popular Chinese AI model has Mythos-class hacking abilitiesEN
  5. 05Watt’s up, doc? Huddig hybrid loader gets a big battery boost [video]EN
  6. 06OpenAI alerts 100 orgs that its 'misaligned models' attempted to break inEN
  7. 07AI (For Normal People) with AWS Hero Thorsten Hoger [video]EN
  8. 08A Beginner's Guide to Running AI Models LocallyEN
  9. 09Cube Rule food classifier using Cloudflare Clef classification modelEN
  10. 10Training an open decision model to replace a closed one, in shadow on prodEN
  11. 11We diffed all 66 release pairs of the official MCP servers, 140 silent changesEN
  12. 12Changes to Gemini model access and limitsEN
  13. 13Open 10-player CS:GO dataset for multiplayer world modelsEN
  14. 14What does the advent of powerful AI models mean for mathematicians like me?EN
  15. 154.5 Ideogram 4.5: The most precise edit modelEN
  16. 16Mystery AI Hype Theater 3000 [video]EN
  17. 17Kolibri: A Sovereign Open-Weight ModelEN
  18. 18California Attorney General Serves Investigative Subpoena on OpenAIEN
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All figures and quotations in this text come from the sources listed below.

Content prepared by the editorial team with AI assistance.

Grace Okonkwo

Grace Okonkwo

AI, models and technology

Grace Okonkwo covers AI, models and technology for FLASH24, working from primary sources such as model cards, API documentation and benchmark papers rather than vendor summaries. She checks training data provenance, evaluation conditions and reported scores against the underlying datasets before any figure reaches print. She interviews researchers and engineers directly, tracks release calendars from major labs, and compares successive model versions on the same tests. Her own self-hosting, home-network and documentation-reading habits feed straight into that desk, since she tests tools on her own hardware first. She does not publish benchmark claims without a reproducible method.

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