NVIDIA Alpamayo 2 Super is an open 34-billion-parameter reasoning vision-language-action (VLA) model for autonomous driving. It pairs the 32B Cosmos 3 Super Reasoner with a roughly 2B diffusion-based Action Expert and is post-trained with reinforcement learning. From a single pass over 360-degree video from up to seven cameras plus egomotion history, it emits a planned trajectory, a Chain-of-Causation trace explaining the decision, a meta-action such as yield, lane change or stop, reasoning auto-labels, and visual question answering with 2D grounding. It ranks first on the LingoQA driving-reasoning benchmark among nearly 40 models with a Lingo-Judge score of 79.2, beating Gemini 2.5 Pro by 15.1 points and GPT-4o by 23.2 points. Weights are on Hugging Face under the Linux Foundation's OpenMDW-1.1 licence with source code under Apache 2.0, permitting fine-tuning, derivative models and commercial redistribution. NVIDIA positions it as a cloud-scale teacher model to be distilled for in-vehicle inference rather than run directly in the car.
Parameters
34B
Context Window
License
OpenMDW-1.1
Release Date
2026-08-04
Benchmark Performance
AA Intelligence Index
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LMArena Elo
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HLE
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ARC-AGI-2
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SWE-bench Verified
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GPQA Diamond
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MMLU-Pro
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LiveCodeBench
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AIME 2025
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MATH-500
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API Pricing
API pricing for this model is not yet available
Strengths
- •Ranks first on LingoQA among nearly 40 models (Lingo-Judge 79.2)
- •Emits trajectory, causal explanation and meta-action in a single pass, making decisions inspectable
- •OpenMDW-1.1 licence permits commercial use, derivatives and redistribution
Weaknesses
- •Peak memory of about 72GB on a single H100 80GB; distillation is required for in-vehicle real-time inference
- •Purpose-built for driving, not a general-purpose assistant
- •Many reported benchmarks come from NVIDIA's own internal evaluations
Use Cases
- •Trajectory generation and decision explanation for robotaxi development
- •Reasoning auto-labelling of raw fleet driving footage
- •Teacher model for distilling compact in-vehicle policies