Gemini Robotics-ER 1.5
Google DeepMind, US · September 2025
- Parameters
- not published
- No parameter count published.
- GPU memory
- not published
- weights at bf16, computed
- Inference latency
- not published
- per action step
- Weights
- closed
- closed, available through the Gemini API
What it is
Gemini Robotics-ER 1.5 is the embodied reasoning model of the 1.5 family, built on the Gemini 2.5 generation, and the first Gemini Robotics model that ordinary developers could call through the API. Its strongest claim is aggregate rather than per benchmark: an ER Score of 59.6 over 15 academic embodied reasoning benchmarks against 51.1 for GPT-5, with an overall average of 62.8 against 60.6. Looked at individually the picture is mixed, since GPT-5 leads on ERQA and the older ER 1.0 leads on Point-Bench. Its reasoning depends heavily on the thinking budget, and disabling thinking costs roughly six aggregate points. It has since been replaced in the API by ER 1.6 and ER 2.
Architecture
- Backbone
- Built on the Gemini 2.5 generation. A vision-language model for embodied reasoning with a configurable thinking budget, not a motor-control model.
- Action head
- None. It outputs text, points, plans, progress estimates and tool calls, and acts as the orchestrator for a VLA such as Gemini Robotics 1.5.
- Parameters
- No parameter count published.
- Pretraining data
- Gemini 2.5 training data plus additional embodied reasoning datasets. No corpus size published.
- Embodiments
- Embodiment agnostic. Evaluated as the orchestrator for ALOHA, bi-arm Franka and the Apollo humanoid.
What hardware it needs
This is the section most people came for, so it is worth being precise about which numbers are measured and which are arithmetic.
The authors publish no memory or latency figure for this model. Everything above is computed from the parameter count.
Reported results
Grouped by what the evaluation actually asked. Values from different suites are not comparable with each other, so each one keeps its suite and split.
Simulation
No results in this category are published for this model.
Real world
No results in this category are published for this model.
Fine tuned tasks
No results in this category are published for this model.
Embodied reasoning
Offline benchmarks that test understanding rather than control. No robot moves in these, so a high score says nothing about grasping.
- Point-Bench pointing71.6%accuracyTable 19. GPT-5 scores 43.6. Gemini Robotics-ER 1.0 scores 75.7, so ER 1.5 is slightly below its predecessor on this split.source
- 15 academic embodied reasoning benchmarks Overall Average62.8 pointsaggregate scoreTable 19. GPT-5 60.6, Gemini 2.5 Pro 59.3. Baselines were accessed between 1 and 20 September 2025 with default thinking budgets and no tool use.source
- 15 academic embodied reasoning benchmarks ER Score aggregate59.6 pointsaggregate scoreTable 19. GPT-5 51.1, Gemini 2.5 Pro 51.7, Gemini Robotics-ER 1.0 52.6. This is the split where ER 1.5 leads clearly.source
- Where2Place free-space pointing59%
- ERQA 400 multiple-choice embodied reasoning questions, thinking enabled54.8%accuracyTable 19. GPT-5 scores 59.0 and Gemini 2.5 Pro 56.0 on the same split, so ER 1.5 does not lead on ERQA itself. Without thinking it drops to 47.0.source
- RefSpatial spatial referring48.5%
On real hardware
Used as the high-level orchestrator in an agentic system with Gemini Robotics 1.5 as the low-level controller, evaluated on ALOHA, bi-arm Franka and the Apollo humanoid. The report shows the agent with ER 1.5 as orchestrator beating the same agent driven by off-the-shelf Gemini 2.5 Flash across all tested cases.
Fine tuning it yourself
No fine-tuning. The model was served as gemini-robotics-er-1.5-preview through the Gemini API and Google AI Studio, with behavior controlled by prompting and the thinking_config option, which sets or disables the thinking budget.
Where it helps, where it does not
Strengths
- Leads the 15-benchmark aggregate (ER Score 59.6 versus 51.1 for GPT-5)
- Was the first Gemini Robotics model made broadly available to developers through the Gemini API
- Configurable thinking budget lets you trade latency against reasoning quality
- Native tool calling, including Google Search, and semantic safety reasoning
Limits
- Does not lead every benchmark. GPT-5 beats it on ERQA (59.0 versus 54.8) and ER 1.0 beats it on Point-Bench (75.7 versus 71.6)
- Thinking is load bearing. Turning it off drops the aggregate from 62.8 to 56.7
- Produces no motor commands, so it needs a separate VLA to act
- Closed weights
- No longer listed in the current Gemini API model lineup, which now offers ER 2 and the deprecating ER 1.6
Sources
Everything on this page was taken from these documents. Where they disagree with what you read here, they win.
- https://arxiv.org/abs/2510.03342
- https://deepmind.google/blog/gemini-robotics-15-brings-ai-agents-into-the-physical-world/
- https://developers.googleblog.com/en/building-the-next-generation-of-physical-agents-with-gemini-robotics-er-15/
Entry last checked 2026-08-11.
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