Chatbot Arena +
❖ This leaderboard is based on the following benchmarks. Arena + — an agent-driven battle platform for large language models (LLMs), with Bradley-Terry Elo ratings computed from LLM-as-a-judge votes. MII — Model Intelligence Index, combining Arena Elo and Agent Intelligence (scored on real-world, multi-step workflows and coding tasks). ARC-AGI-2 — an Artificial General Intelligence benchmark that measures fluid intelligence.
Agentic Inference with NVFP4 KV Cache
❖ The KV cache is a fundamental building block of the modern LLM inference system. The context from multiple conversation rounds in agent sessions is cached as keys and values (KV) in GPU memory, allowing the model to reuse them during subsequent decoding steps. When generating each new token, the new query token attends to the relevant past KV tokens from the KV cache. As context windows grow, storing and reading KV cache puts increasing pressure on GPU memory capacity and bandwidth.
TorchNeuron
❖ TorchNeuron is an open-source PyTorch extension that provides new native backend integration for AWS Trainium. The implementation includes support for eager mode for rapid iteration and experimentation, torch.compile for just-in-time compilation, and standard distributed processing APIs. TorchNeuron enables ecosystem compatibility and supports custom kernel development through the Neuron Kernel Interface (NKI) for performance optimization and research applications.
PyTorch on TPU
❖ PyTorch on Cloud TPU lets you train, fine-tune, and serve open-source models on Cloud TPU hardware using standard PyTorch semantics, distributed APIs, and the ecosystem tools you already use, including Hugging Face, vLLM, SGLang, llm-d, TorchTitan, TorchRec, and Ray. This ecosystem provides libraries, frameworks, and tools for every stage of the machine learning lifecycle, from core runtime execution and custom kernels to production inference, large-scale training, and cluster orchestration.
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.