World Wire

Liquid AI has released d1-ultra , its most capable decision model to date. The Liquid AI d1-ultra decision model reads text, images, or both. It answers typed questions with a probability for each possible answer. It does this in one forward pass and generates no output tokens. For developers, that means classification, routing and scoring calls without paying for generated text. It is available now on the Liquid API at $0.042 per million input tokens. TL;DR Size: Parameters and context length not disclosed. Built on a larger open-weight backbone than d1. Runs on: Hosted Liquid API only. No downloadable weights or quantized variants announced. Performance: Leads all 8 categories in Liquid’s 112-evaluation suite, per Liquid. Best: 65 on the Artificial Analysis Decision Index, ranked #1 (Liquid-reported). Worst: No public per-benchmark breakdown or parameter count yet; claims are vendor-reported. What is a decision model? A decision model ans...

Meta Superintelligence Labs has released Muse Spark Open , the open-weight version of its Muse Spark model. Muse Spark Open is an 816B-parameter Mixture-of-Experts model that activates 42B parameters per token. It takes text, image, video and audio input, returns text, and holds a 1M-token context window. Meta team claims it to be the most capable US open-weight model to date. For developers, the real news is deployment: the NVFP4 build runs on 2 Apple M5 Ultra Mac Studios. TL;DR Size: 816B total / 42B active parameters (256 experts, 8 active per token); 1M-token context. Runs on: 2 Mac Studios (M5 Ultra, 512 GB each) via MLX; 4 Blackwell Ultra GPUs or 8 H200s with NVFP4; about 300 GB setups via Unsloth 2-bit GGUF. Performance: Strong on agentic tool use; mid-pack among large open models on coding. Best: MCP-Atlas 89.2, the top score in Meta’s comparison table. Worst: Terminal-Bench 2.1 76.4, behind Kimi K3 (85.0), GLM 5.3 (83.9) and Qwen3.8 (82.0)....

TypeSafe AI has released Jev 1.14 , a new version of its System One decision model. It is live now under the jev-preview alias. It adds 3 experimental ‘danger zone’ features: images, a box type and longer context. For developers, Jev 1.14 pushes a typed decision model toward vision tasks like inspection and localization. TypeSafe shares a note that none of the 3 features are ready for production yet. TL;DR: Jev 1.14 Size: Parameters not disclosed. Context is 64k tokens per request and 32k per question by default, or 262k with an opt-in header. Runs on: TypeSafe’s hosted API only ( POST /v1/systemone ). No open weights. Hardware and quantized variants not disclosed. Performance: Small gains everywhere, largest in multilingual support (TypeSafe’s claim, no public scores). Best: In TypeSafe’s Box test, 4 edge-check questions caught 30 of 36 cut-off crops. Worst: Images and Box are not production-ready, and Box has no “not...

SpaceXAI just released Grok Voice 3.0 . Grok Voice 3.0 is a SpaceXAI speech-to-speech model that listens, reasons, calls tools and speaks. It now ranks first on the Artificial Analysis Speech-to-Speech Index with a score of 86.9. For developers, the endpoint, events and $0.08 per minute price are unchanged from Grok Voice 2.0. TL;DR Runs on: SpaceXAI’s hosted Voice Agent API only. No open weights, no local or quantized variants. Performance: Highest Index score of any model Artificial Analysis currently ranks. Best: 86.9 on the AA Speech-to-Speech Index, ranked #1. Worst: 71.6% on τ-Voice, behind GPT-Live-1 (Astra, medium) at 74.5%. Bottom line: Best: top composite score at 0.81 s time to first audio. Worst: not the leader on agentic task resolution or raw speech reasoning. What is Grok Voice 3.0? Grok Voice 3.0 is SpaceXAI’s flagship realtime voice model, exposed as grok-voice-3.0 on the /v1/realtime WebSocket endpoint . T...