Muse Spark Open
❖ Muse Spark Open is an 816B total/42B active parameter open weights model with a context window of 1M tokens. Built for coding and agentic work, Muse Spark Open is natively multimodal. This open weights version has additional safety post-training to make it more robust against misuse as a standalone model without system guardrails. We recommend deploying Muse Spark Open as part of a broader AI system, with additional guardrails suited to the specific use case.
Nemotron 3.5
❖ NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 is the quantized release of NVIDIA Nemotron 3.5 Super VL, a vision-language model for reasoning, coding, tool use, document understanding, and video understanding. It uses NVFP4 quantization to reduce memory requirements, with quantization-aware distillation from the BF16 model.
DeepBuilder
❖ DeepBuilder is an end-to-end autoresearch agent framework for productive, stable, and goal-aligned research over hours or days. The current implementation centers on a recoverable executable workspace: agents inspect task data, write and run code, evaluate candidate artifacts, preserve validated stages, reconsider research direction, and resume interrupted work without discarding established evidence.
MiniMax M3.1
❖ As a next-generation Flash model, MiniMax M3.1 Flash delivers a generational leap in agentic tasks, coding, Computer Use and cybersecurity, while making online inference nearly three times faster and reducing usage costs by 70%. Space Bunny 🐰 on OpenRouter was an early version of MiniMax M3.1 Flash.
Ling-3.1
❖ We’re introducing Ling-3.1-flash, an upgraded native hybrid reasoning model designed for complex, real-world tasks. With 560B total parameters, 25B active parameters per token, and a 1M-token context window, Ling-3.1-flash builds upon Ling-3.0-flash with improvements in architecture and performance on real-world tasks.
MiMo-V2.6
❖ MiMo-V2.6-Pro-RL is the flagship checkpoint of the MiMo-V2.6 series. The series is built to scale reinforcement learning toward self-improvement — scaling RL compute, environment diversity, and grader compute together, so the model keeps expanding its capability frontier through exploration and feedback.
Step-5
❖ Step-5-Preview is our flagship foundation model for real-world agentic work — a 600B-parameter sparse Mixture-of-Experts model with 27B active parameters, a 1M-token context window, and native support for text, image, and video inputs. It targets professional domains such as AI coding, software engineering, professional knowledge work, and financial analysis.
LongCat DeepResearch
❖ LongCat DeepResearch organizes open-ended research around an executable ResearchSpec. Multiple planners explore sources and refine a shared agenda; independent Researchers develop citation-bearing sections; direct assembly and coordinated editing turn those sections into a complete report without asking one final writer to regenerate everything.