El nuevo modelo MoE de 594B de Thinking Machines mejora significativamente el rendimiento y la eficiencia de la IA, ofreciendo un gran avance frente a su predecesor para los desarrolladores que buscan capacidades avanzadas y personalizables.
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Thinking Machines Lab has released Inkling 2 Small, the first model of its second Inkling generation. The Inkling 2 Small model is a sparse Mixture-of-Experts (MoE) with 594B total and 17B active parameters. It accepts text and images, and supports variable reasoning effort. On the Artificial Analysis Intelligence Index, it scores 31. Its predecessor, Inkling Small, scores 13 on the same index. The model is live on Tinker for fine-tuning and inference. TL;DR Size: 594B total, 17B active MoE; 1M-token context. Runs on: Tinker API only for now. Self-hosting hardware is not disclosed. Weights support MXFP8 and NVFP4. Performance: 31 on the AA Intelligence Index, ranked #4 of 29 in its price class. Best: 86.3% on SWE-Bench Pro v2 (Hard), second only to Kimi K3 in the lab’s table. Worst: 13.1% on Terminal-Bench 4.0, versus 32.8% for GLM-5.3-Flash. What is Inkling 2 Small? Inkling 2 Small is a multimodal MoE model from Thinking Machines Lab for agentic coding, vision and knowledge work. It takes text and image input and returns text. It is important to note that it does not support audio as of now. Weights are closed at launch. The Thinking Machines Lab plans an Apache 2.0 release next week, after external safety testing and red-teaming. This follows its safe path to open weights policy. How is Inkling 2 Small built? It includes a 48-layer decoder-only transformer. Each token routes to 8 of 384 experts. Attention mixes local and global layers. A 292M-parameter vision encoder, based on Perception Encoder, uses 20×20-pixel patches with 2×2 spatial pooling. Image patches are interleaved with text tokens. Pretraining ran in 4 phases: H1: 8.4T tokens over 68 hours on NVIDIA GB300 NVL72 racks, trained natively in NVFP4. H2: 3.8T tokens of newer, higher-quality data at 512K context. H3: about 320B tokens of reasoning and agent trajectories at 1M context. H4: about 34B tokens of the highest-quality agent trajectories. A single joint RL run followed. It took 1,075
Una ayuda automática para ordenar, basada en fuentes, palabras clave y entidades, en una escala de 1–12. Alta: 9–12; media: 6–8; baja: 1–5. No mide urgencia, verifica noticias ni representa respaldo editorial.
ToyBlocks ofrece un entorno estandarizado y controlado para entrenar y evaluar algoritmos de aprendizaje de robots, permitiendo comparaciones de rendimiento consistentes y reproducibles.
Agregar el inglés egipcio y nigeriano al Open ASR Leaderboard amplía los puntos de referencia de reconocimiento de voz para incluir dialectos diversos y subrepresentados, mejorando el rendimiento global de los modelos.