Google DeepMind Releases Gemini Robotics 2.5: 30% to 100% Higher Success Across 200 Tasks, a 96% Parts-Kitting Rate for Apptronik Apollo 2 on Mercedes-Benz Lines, and an Open-Source ASIMOV-Jailbreak Benchmark
On November 10, Google DeepMind released Gemini Robotics 2.5, which it calls its most capable vision-language-action (VLA) model so far. Across more than 200 evaluated tasks, DeepMind reports a 30% to 100% higher success rate than earlier baselines depending on the task type, with tasks finished 20% to 30% faster. In a proof of concept with Apptronik and Mercedes-Benz, the Apollo 2 humanoid sorted parts on car production lines in Hungary and Germany with a 96% success rate. The model is rolling out only to robotics partners for now, so developers cannot call it directly yet.
Key Takeaways
- Task breadth: evaluated on 200+ tasks (tying a bow, serving food with tongs, pipetting), with a 30%–100% higher success rate than earlier baselines and 20%–30% faster completion (company figures, official blog)
- Factory test: Apptronik's Apollo 2 kitted parts at Mercedes-Benz plants in Kecskemet, Hungary, and Sindelfingen, Germany, with a 96% success rate (a proof of concept, not a production rollout)
- Safety: inference-time safety guidance raised safe task completion by 33% around obstacles and 43% near people; the new open-source ASIMOV-Jailbreak benchmark shows 99.5% jailbreak resistance for Gemini 4 Argon versus 82% for the earlier Gemini Robotics ER 2 baseline
- Faster training: Gemini 4 Argon builds digital twins and thousands of simulated practice runs from a single camera view and analyzes failure videos, which DeepMind says sped up months of training work
- Availability: rolling out to robotics partners only, with no public API yet; it runs on Apollo 2 (five-finger, 22-degree-of-freedom SharpaWave hand) and the two-arm Franka Duo platform
Linked Models & Agents:
⚡ gemini-robotics-2.5⚡ Gemini 4 Argon⚡ Gemini Robotics Er 2🌐 google-deepmind🌐 apptronik🌐 mercedes-benz
