The most capable, efficient, and innovative open-weight models defining the frontier AI ecosystem in late 2026.
The reigning #1 open-weight model on global intelligence benchmarks. Unified asynchronous RL scaling across coding, visual workflows, and cybersecurity, bolstered by Groupwise Agentic Grading for recursive self-improvement.
The world's first open 3T-class model. 2.8 trillion parameters with extreme MoE sparsity, capable of sustaining multi-hour autonomous engineering sessions, GPU kernel optimization, CAD design, and interactive scientific research.
Pushing the limits of KV cache compression. Its Causal Encoder-Decoder (CED) architecture activates only 8B parameters during prefill, reducing persistent KV memory to 1/8th of standard baselines for lightning-fast long-context inference.
The reigning open-source champion for coding and agentic terminal execution. Achieves open SOTA on Terminal Bench 3.0 and CyberGym, with massive gains in automated vulnerability discovery, exploitation defense, and system auditing.
Multimodal reasoning powerhouse featuring native vision and video understanding with dynamic thinking control (`xhigh`, `medium`, `low`). Backed by support for 119+ languages and an unencumbered Apache 2.0 open license.
Meta's multimodal MoE flagship. Llama 4 Scout offers an unprecedented 10M token context window for entire multi-repository analysis, while Maverick delivers frontier reasoning and native image understanding.
The dense 128B parameter powerhouse. Features toggleable instant versus deep reasoning modes, multimodal vision processing, and enterprise-grade native function calling with strict JSON schema guarantees.
Frontier hybrid architecture combining Mamba-2 state space operators with LatentMoE and Multi-Token Prediction (MTP). Delivers blazing linear inference throughput across 1M context tokens for mission-critical enterprise RAG.
An omnimodal MoE titan with native understanding of hour-long video, high-resolution imagery, audio, and text. Delivers top SWE-bench Verified coding scores and robust XML tool invocation for autonomous multi-agent scaffolds.
OpenAI's milestone entry into open weights. The 120B parameter MoE (with only 5.1B active parameters per token) is engineered specifically for agentic workflows, native tool calling, and efficient deployment on single-GPU hardware.