GPT-Live-1 (Sol, low) ranks #3# in conversational preference at 1,053 Elo with 90.9% Task Success Rate, while GPT-Live-1 (Astra, medium) ranks #4# at 1,048 Elo with 87.4% task success. Gemini 3.1 Flash Live Minimal leads preference at 1,096 Elo but achieves 74.6% task success, while Grok Voice Think Fast 2.0 High leads task success at 94.6% with 1,011 Elo. GPT-Live-1 (Sol, low) has higher point estimates on both measures than GPT-Live-1 (Astra, medium), although their confidence intervals overlap.

Countries with the Longest Road Networks 1. 🇮🇳 India - 6,700,000 km 2. 🇺🇸 United States - 6,586,610 km 3. 🇨🇳 China - 5,490,400 km 4. 🇧🇷 Brazil - 2,000,000 km 5. 🇷🇺 Russia - 1,579,291 km 6. 🇯🇵 Japan - 1,218,772 km 7. 🇫🇷 France - 1,053,215 km 8. 🇨🇦 Canada - 1,042,300 km 9. 🇦🇺 Australia - 977,874 km 10. 🇲🇽 Mexico - 836,603 km 11. 🇩🇪 Germany - 830,000 km 12. 🇿🇦 South Africa - 750,000 km 13. 🇹🇭 Thailand - 702,989 km 14. 🇪🇸 Spain - 683,175 km 15. 🇲🇾 Malaysia - 589,320 km 16. 🇸🇪 Sweden - 573,134 km 17. 🇻🇳 Vietnam - 570,448 km 18. 🇮🇩 Indonesia - 548,097 km 19. 🇮🇹 Italy - 487,700 km 20. 🇫🇮 Finland - 454,000 km Source: World Population Review / CIA World Factbook

OpenAI’s GPT-Live-1 debuts at #1# on the Artificial Analysis Speech to Speech Index at a score of 81.5 using Astra as its delegated backend model, ahead of Grok Voice Think Fast 2.0 GPT-Live-1 is @OpenAI's new full duplex Speech to Speech model that can delegate reasoning and tool use to a backend text model while continuing the conversation. Developers stream audio in and receive speech back through the API, with the backend text model configured separately. We evaluated two backend configurations: Astra at medium reasoning effort and Sol at low reasoning effort. Key takeaways: ➤ Speech to Speech Index: GPT-Live-1 (Astra, medium) achieves 81.5, ranking #1#, while GPT-Live-1 (Sol, low) scores 80.1, ranking #3#. Grok Voice Think Fast 2.0 High sits between them at 81.3 ➤ Speech Agent Arena: GPT-Live-1 (Sol, low) ranks #3# in preference at 1,053 Elo with 90.9% Task Success Rate, while GPT-Live-1 (Astra, medium) ranks #4# at 1,048 Elo with 87.4% task success. Gemini 3.1 Flash Live Minimal leads preference at 1,096 Elo, while Grok Voice Think Fast 2.0 High leads task success at 94.6% ➤ Tau Voice: GPT-Live-1 (Astra, medium) and GPT-Live-1 (Sol, low) take the top two spots on our agentic-performance benchmark at 67.9% and 59.3%, respectively, ahead of Grok Voice Think Fast 2.0 High at 56.5%. ➤ Big Bench Audio: GPT-Live-1 (Astra, medium) scores 90.1% and GPT-Live-1 (Sol, low) scores 89.0% on audio reasoning, behind Grok Voice Think Fast 2.0 High at 97.2% and Qwen Audio 3.0 Realtime Plus at 99.2% ➤ Speed: Average Time to First Audio on Big Bench Audio is 1.34 seconds for GPT-Live-1 (Astra, medium) and 1.24 seconds for GPT-Live-1 (Sol, low), compared with 0.70 seconds for Grok Voice Think Fast 2.0 High ➤ Cost: GPT-Live-1 (Astra, medium) costs $5.83 per hour of input audio, compared with $4.47 for GPT-Live-1 (Sol, low), including delegated backend model usage, versus $4.80 for Grok Voice Think Fast 2.0 High on our fixed Big Bench Audio pricing subset See below for more detail ⬇️

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I gave 4 "Flash" models the same job: redact the PII in an HR letter using an agentic mask tool. One of them cost 630× more than another. And it wasn't 630× better. Results 🧵 🥇 GLM 5.3 Flash $0.005, 15 tool calls Clean sweep. Overshot a bit (also redacted employee ID + last-4 of the bank account, which the test didn't require), but zero leaks. Half a cent. 🥈 Qwen 3.8 Flash $0.013, 28 tool calls Clean pass, cleared its markers, and finished the job. 🥉 Gemini 3.8 Flash $3.10, 155 tool calls Passed... eventually. 73 reasoning turns, 3.9M input tokens, an add-mask / remove-mask loop that went on for minutes. Correct result, brutal bill. ❌ DeepSeek V4 Flash Vision $0.053, 31 tool calls Failed. Its own reasoning says "the mask needs to be wider" and "the text is still visible", then it stopped calling tools. Name, DOB, SSN, address, email all partly readable in the final output. Takeaways: > Cheapest model won. Not "won on value", won outright. > Tool-call count predicted cost better than model tier. 155 calls is where the money went. > Seeing the problem ≠ fixing it. DeepSeek diagnosed its own failure correctly and still shipped it. > Total spend for all 4 runs: $3.18. Gemini was $3.10 of that.