OpenAI Launched GPT-5.3-Codex | NVIDIA AI releases C-RADIOv4 vision backbone

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GPT-5.3-Codex is OpenAI’s new agentic coding model that merges GPT-5.2-Codex’s frontier coding skills with GPT-5.2’s reasoning and professional knowledge, while running 25% faster in Codex. It sets new highs on SWE-Bench Pro (56.8% at xhigh) and Terminal-Bench 2.0 (77.3%), and scores 64.7% on OSWorld-Verified and 70.9% wins or ties on GDPval, often using fewer tokens. In the Codex app, it can drive long-horizon workflows, such as autonomously building complex web games and generating realistic work products like slide decks and spreadsheets. Early versions were used to debug its own training and deployment, making it instrumental in its creation. Classified as a “High capability” cyber model, it comes with strict safeguards and is available to paid ChatGPT users across the Codex app, CLI, IDE extensions, and web.....… Read the full analysis/article here.

NVIDIA AI releases C-RADIOv4, an agglomerative vision backbone that distills SigLIP2-g-384, DINOv3-7B, and SAM3 into a single ViT-style encoder for classification, retrieval, dense prediction, and segmentation. The model uses stochastic multi resolution training over 128–1152 px, FeatSharp upsampling, and shift equivariant dense and MESA losses to suppress teacher artifacts such as border and window noise. An angular dispersion aware summary loss balances SigLIP2 and DINOv3 contributions so vision language alignment is not dominated by self supervised features. C-RADIOv4-H reaches about 83.09 % ImageNet zero shot accuracy, strong ADE20k and VOC scores, and state of the art NAVI and SPair results within the RADIO family. The backbone can directly replace the SAM3 Perception Encoder, supports ViTDet style windowed attention for faster high resolution inference, and is released under the NVIDIA Open Model License....… Read the full analysis/article here.

Anthropic’s Claude Opus 4.6 is a new Opus-class frontier model optimized for long-context, agentic workloads rather than single-turn chat. It offers a 1M-token context window in beta, up to 128k output tokens, and explicit controls for reasoning depth via /effort and adaptive thinking, letting teams tune latency and cost. Benchmarks show state-of-the-art results on GDPval-AA, Terminal-Bench 2.0, Humanity’s Last Exam, BrowseComp, and MRCR v2 1M, with large gains over Claude Opus 4.5 in long-context retrieval and tool-augmented reasoning. Deep integration with Claude Code, Excel, and PowerPoint targets real engineering and analyst workflows, while expanded safety and cybersecurity evaluations, including 6 new probes, aim to keep misalignment low and support defensive security use cases....… Read the full analysis/article here.

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