#Other

AIArtificial Analysis shares the second part of an AA-Video-T2V v2.0 prompt describing a four-shot documentary-style handheld video of a glass cabin in falling snow. The shots follow a caretaker sweeping snow from the deck, empty snow-covered windows, an empty interior, and the same caretaker stamping snow off his boots at the door, with hard cuts between shots.

AIArtificial Analysis shares an AA-Video-T2V v2.0 text-to-video prompt, a 10-second product-page scene of a presenter revealing a volcano-shaped mist diffuser. The prompt specifies a shift from warm daylight to dim evening lamplight, fixed close-up camera angles, and consistent presenter and product appearance across shots.

AIGrok Imagine Video 1.5 Lite ranks #17 on AA-Video-T2V v2.0, two places above Google's Veo 3.1. At 1080p with audio, it costs $0.14 per second versus $0.40 per second for Veo 3.1. Compared with Grok Imagine Video 1.5, Lite is 44% cheaper at 1080p but ranks six places lower.

AISpaceXAI's Grok 4.7 scored 90.2% on ARC-AGI-1 at $0.64 per task, higher than Grok 4.6, according to ARC Prize. It reached 61.4% on ARC-AGI-2 at $2.01 per task and 1.8% on ARC-AGI-3 under the standard harness ($2.7k), or 10.0% with the provider adapter harness ($4.8k), both lower than Grok 4.6 on those two benchmarks.

AIElevenLabs' Eleven v4 and Eleven v4 Turbo rank #1 and #2 on the Artificial Analysis text-to-speech leaderboard. Per Artificial Analysis, Eleven v4 Turbo leads the Provider Voice arena at an Elo of 1,334, costs $40 per 1M characters, and generates 96 characters per second.

AIIndexTeam released Index-Nailong-9B-FP4, an official NVFP4 (W4A4) quantization of the Index-Nailong-9B multilingual translation model, which covers 150 languages. In a validation on an NVIDIA A100 against the BF16 checkpoint, perplexity rose 3.10% (2.4339 to 2.5094), and zh-en and en-zh outputs were semantically equivalent. Full FP4 compute acceleration requires an NVIDIA Blackwell GPU, while older GPUs get memory savings only; the FP8 build is recommended for Hopper and Ampere.

AIAnt Ling asked developers what they will build with Ling-3.1-flash and invited them to share demos and feedback in its official Discord server. The post gives no specifications, benchmarks, pricing, or release details.

AIAnt Ling reports that Ling-3.1-flash scored 65.35 on HealthBench Professional, a healthcare evaluation rather than clinical certification. The model was trained with feedback from AQ and Haodf.com (Good Doctor Online) healthcare scenarios.

AIModel page:

AIZ.ai says GLM-5.3 is available now through GLM Coding Plan and ZCode. API access and open weights will follow in stages after rigorous safety evaluations.

AIThinking Machines has introduced Inkling as the first model in a family. It also shares details of Inkling-Small, a lighter-weight model trained on a similar recipe, with full weights promised later.

AITri Dao announced Mamba-3, which he described as the most powerful linear sequence model to date, as hybrid architectures increasingly rely on strong linear models. The post cites Qwen, Kimi-Linear, and NVIDIA's Nemotron-3 Super as examples of this trend. According to co-author Albert Gu, Mamba-3 shows noticeable performance gains over Mamba-2 and Gated DeltaNet at all sizes while maintaining speed.

AIMistral announces Voxtral 2 with two transcription models: Voxtral Realtime, released under an Apache 2 license with latency configurable to sub-200 ms, and Voxtral Mini Transcribe 2, which adds speaker diarization, word-level timestamps, and context biasing. The models support 13 languages and are available through the Mistral API, which the post describes as one of the most cost-effective transcription APIs on the market. The attached chart shows word error rates on FLEURS across Italian, Spanish, English, German, Portuguese, French, Russian, Dutch, and Chinese at several latency settings.