Gemini 3.5 Pro pushed back again, didn't hit internal perf targets.
Fable 5 and GPT-5.6 are just too far ahead. Next Gemini's gonna have a rough time catching up.
Gemini 3.5 Pro pushed back again, didn't hit internal perf
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No Mac. No code. App Store anyway.
The old way: buy a Mac, learn Xcode, pay Apple $99/year, and drown in certs and provisioning profiles. Baidu Miaoda skips all of it. Describe your idea, it spits out a native iOS app, handles cloud builds, signs everything, and ships straight to App Store Connect. All automatic. No Xcode. No certs. No Mac required. First platform to go end-to-end: natural language → native iOS app → App Store. Nobody else does this.
Kimi-K3 is wild.
2.8T params, 896 MoE experts, only 16 active at a time. Native 1M context + multimodal. AA index 57, beats Opus 4.8 and GPT-5.5. #1 on Arena WebDev, 1527 on long agent tasks (2nd). Pricing is brutal. ¥2/M input (90%+ cache hit), ¥100/M output. Opensourcing before July 27. Strongest open model right now. I used K3 to generate a playable guitar. Final result stunned me. Cost me ¥5 in API calls but I hit errors the whole time. Honest take: too pricey right now, compute is tight.
Meituan shipped LongCat-2.5
. 1.6T params, ~48B active, native multimodal, aimed at long-horizon agents, trained start to finish on Chinese hardware. Images get quantized into multi-level discrete tokens, and that's where Visual Coding and 3D pick up. On the RL side, failed trajectories get turned back into verifiable training tasks. W4A4 inference, 43% faster prefill. Meituan's own evals put it between Claude Opus 4.8 and Opus 5.
SenseTime open-sourced SenseNova Loop-9B-Preview.
It stacks layers instead of parameters. The decoder has 32 layers, but layers 4 through 27 run twice with shared weights, so a forward pass does 56 layers of work while the checkpoint stays at 9B. Their own eval numbers: 71.38 on BIRD-Dev and 13.72 on AutomationBench, both above Qwen3.5-27B. Terminal Bench is still behind.
