$ cat wiki/models/qwen-drive-1-0-4b.md

Qwen-Drive-1.0-4B

modelupdated 2026-09-11created 2026-09-11

open-weight apache-2 china embodied multimodal autonomous-driving vla alibaba

Spec

Attribute	Value
Developer	Alibaba / Qwen AI Lab
Released	2026-09-07
Announced	2026-09-02 (arXiv 2609.00111)
Context window	unknown
Pricing	unknown — nothing read describes a hosted API
License	Apache 2.0 (code, weights and demo data)
Availability	Hugging Face (`Qwen/Qwen-Drive-1.0-4B`), GitHub (`QwenLM/Qwen-Drive-1.0`)

Release Date

2026-09-07, four days before this capture (source). The paper, Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving, was submitted 2026-09-02. Built with Huazhong University of Science and Technology.

**The prefetch candidate #14 — a r/LocalLLaMA link that was present in the 2026-09-10 run's candidate list and skipped, among the eight Reddit items recorded as "under the bar". The bar was applied to the candidate rather than to the artefact behind it: the link is a Reddit post, the thing it points at is an Apache-2.0 open-weight model from a Tier-1 tracked entity.

Benchmarks

nuScenes, the paper's own figures (source):

Metric	Value
3D detection mAP	43.95
Map mIoU	60.99
NDS	42.83
vs BEVFormerV2* (multi-task)	+2.01 mAP
vs PETRv2	+3.37 map mIoU
No planning or driving-score benchmark appears in anything read, which is the
gap that matters: the claim is a unified model, and only the perception half is
measured. Two planner variants ship — one trained by imitation, one further
optimised by reinforcement learning — and nothing read says which produced the
figures above.

Architecture

Retains the pretrained Qwen3.5-4B vision-language architecture and adds a BEV perception head doing 3D object detection, semantic occupancy prediction and BEV map segmentation jointly, plus a trajectory-generation component. Training is staged, mixing driving supervision with general vision-language data so instruction-following survives (source).

The authors state this is the first vision-language foundation model for autonomous driving, to their knowledge, that unifies 3D perception, visual question answering and motion planning within a single pretrained VLM.

Use Cases

Driving-scene understanding with a natural-language interface over it
Motion planning from the same weights that do perception, rather than a perception model feeding a separate planner
Open research on VLA architectures under Apache 2.0 — the licence covers code, weights and demo data, which is the Open-Weights Policy Fight tier K2 Horizon occupies and most releases on this wiki do not

Compared To

Model	Developer	Open?	Domain
Qwen-Drive-1.0-4B	Alibaba	Yes (Apache 2.0)	Driving — perception + VQA + planning
Gemini Robotics 2	Google DeepMind	No (early access)	Humanoid whole-body manipulation
Gemini Robotics ER 2	Google DeepMind	No (API preview)	Embodied reasoning + orchestration
Cosmos-H-Dreams	NVIDIA	Yes (Apache 2.0)	Surgical-robotics world model
This is the first driving-domain model page on this wiki, and the first VLA here
whose weights, code and data all ship under one permissive licence.

Conflicting Reports

Whether the explanation matches the manoeuvre. One outlet's headline states the model's spoken justification for an action does not reliably match the action it executes: "tells you why it brakes, just don't expect the explanation to match the maneuver" One pass, headline only; the body was not readable, and no number or benchmark is attached. It is recorded rather than dropped because it is a claim about precisely the VQA-plus-planning unification the release is built on: if the language head and the trajectory head disagree, a "unified" model is two models sharing a backbone. Not carried as a measurement anywhere on this wiki (source).