Nace AI is expanding its decision-model technology with Drex Max, a multimodal model designed to evaluate text and images and return structured decisions. The release adds visual input to the company’s Drex decision-model approach, opening up new possibilities for developers building computer-use agents and automated workflows. Unlike a conventional chatbot that generates a sentence or […]
Artificial intelligence has become remarkably capable at writing, coding, summarising documents, and answering questions. Yet understanding what is happening inside an image or video remains a difficult challenge, particularly when a task requires careful observation rather than a simple description. Elorian, a Palo Alto-based AI startup, believes that part of the problem lies in how […]
Nemotron 3.5 Super VL could be one of NVIDIA’s next developments in multimodal artificial intelligence, with a potential announcement expected at GTC Berlin in October 2026. Recent reporting from TestingCatalog points to the model as a possible addition to NVIDIA’s growing Nemotron family. Meanwhile, NVIDIA’s own technical documentation already contains a model entry for Nemotron […]
DeepSeek released V4.1 Flash on Hugging Face on 10 September 2026: a 552B mixture-of-experts model under the MIT licence that activates 8B parameters to read and 16B to write, stores its global KV cache in 890 bytes per token, and replaces V4 Pro for every API caller from 04:00 UTC on 14 September. Its own model card shows it leading V4 Pro on all 12 agentic rows and trailing it on 12 of 16 base-model rows, including a 12.9-point gap on SimpleQA-Verified. This breakdown covers the causal encoder-decoder architecture, the KV cache arithmetic, the new price sheet with a worked agent bill, the 511 GB checkpoint and 614 GB serving floor, the mismatched reasoning-effort aliases, and a pre-cutover checklist.
Z.ai spent six days serving an anonymous model called Ox Alpha on OpenRouter, took nearly 20% of the platform’s weekly token share, and only then revealed it was GLM-5.3-Flash — a 320B mixture-of-experts model with 18B active parameters, a one-million-token context window and MIT-licensed weights. This piece works through the hybrid attention architecture, what the benchmark table supports and what it does not, what the API actually costs once the launch promotion ends, how credible the domestic-silicon claim is, and what any of it changes for a business choosing a model this quarter.
Google released Gemini Omni 1.1 Flash on 27 August 2026, the production version of its generative video model, and the headline change is output above 720p. The model now accepts resolution values of 360p, 720p, 1080p and 4K, extends an existing clip in ten-second increments to a cumulative forty seconds, and interpolates footage between a start frame and an end frame you supply. It is paid-tier only, billed on output tokens at a rate Google states as 5,792 tokens per second of 720p video — roughly $0.10 per second — with a 360p draft tier at a third of that cost and up to 60% faster. This breakdown covers exactly what shipped, why the 1080p and 4K figures are documented as upscales rather than native renders, how the token billing compares against Veo 3.1, which surfaces the model is live on today, and the limitations list that blocks editing uploaded video for users in the UK, the EEA and Switzerland.
Alibaba open-weighted Qwen3.8-Flash-Next on 26 August 2026: a multimodal mixture-of-experts model with 125 billion parameters, a separate 51-billion-parameter N-gram embedding table, and just 6 billion parameters activated per token. This breakdown covers the four rebuilt subsystems — Gated DeltaNet paired with Qwen Sparse Attention at block granularity, a Gated Residual stream widened to four gated branches, the N-gram table that offloads to host RAM, and the Muon plus AdamW training recipe with batch-size warmup removed — alongside the 48-layer stack of 512 experts that fires eleven per token, the published benchmark table showing 62.5 on SWE-bench Pro against 53.4 for Claude Opus 4.6 and 84.5 on AndroidWorld against 62.0, the single loss on Humanity’s Last Exam at 35.9 against 40.0, the unverifiable one-ninth training cost claim, the 262,144-token native context extended to a million with YaRN, hosted pricing of $0.16 and $0.47 per million tokens against $2.00 and $6.00 for Qwen3.8-Max, the real hardware bill from a 172.78 GiB FP8 checkpoint down to a 111 GB four-bit GGUF, the qwen-community-1.0 licence that is not Apache 2.0, and a buyer’s checklist for treating a preview checkpoint as a production dependency.
Qwen3.5 Omni combines audio, video, image, and text handling with speech output inside Alibaba Cloud’s OpenAI-compatible workflow.
Kimi K2.6 is Moonshot AI’s latest multimodal model for long-horizon coding, tool use, research, and agent workflows across the API, Kimi Code, and the Kimi app ecosystem.
A source-backed explainer on HappyHorse-1.0 covering leaderboard performance, API availability, pricing, native audio-video generation, multi-shot workflows, and the unresolved open-source story.
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