The BANG research model generates sequences of separated object parts using a pretrained geometry model, an exploded-view adapter and temporal attention.
Input billingNot collectedHyper3D-associated research publication
Output billingNot collectedNo inferred price
Specifications
Provider-documented facts. “Not collected” means we have not verified that field yet, not that the feature is unsupported.
Context / input limit
Not collected
Maximum input tokens
Not collected
Maximum output
Not collected
Reasoning
Not collected
Tool calling
Not collected
Structured output
Not collected
Streaming
Not collected
Batch processing
Not collected
Fine-tuning
Not collected
Knowledge cutoff
Not collected
Release date
Not collected
Publication or observed application version does not establish an original weight release date.
Parameters
Not collected
Architecture
Base DiT: 24 layers, width 2560, 20 heads. Exploded-view adapter: four layers, width 512. Temporal attention is trained after freezing the base and adapter.Source for Architecture (opens in a new tab)
Languages
Not collected
Context caching
Not collected
Usage metering
Not collected
No price or currently callable endpoint for this exact research edition verified. Current Rodin API rates are not inherited.
Data retention
Not collected
No hosting, retention or enterprise contract verified for this research edition.
Endpoint pricing has not been collected. Check the provider for applicable rates.
Data & enterprise terms
Research access is not assigned the commercial Rodin API retention policy.
Caching
Caching can reuse earlier input processing. Availability, lifetime and billing depend on the endpoint. A cache hit still generates a new response.
01 / INITIAL INPUTProcess & retain
The system processes eligible input and retains reusable state.
02 / LATER REQUESTCheck available state
Reuse depends on matching input, session state and the cache policy.
03 / CACHE HITReuse & generate
Reusing state can reduce repeated work. Any charges follow the billing rules below.
Hyper3D-associated research publication
Not verified
No model-specific prompt-cache contract or price established.
How it works
Not collected
No model-specific prompt-cache contract or price established.
Minimum reusable input
Not collected
No model-specific prompt-cache contract or price established.
Lifetime & refresh
Not collected
No model-specific prompt-cache contract or price established.
How to enable it
Not collected
No model-specific prompt-cache contract or price established.
Usage fields
Not collected
No model-specific prompt-cache contract or price established.
Billing rules
Not collected
No model-specific prompt-cache contract or price established.
Limits & data boundaries
Not collected
No model-specific prompt-cache contract or price established.
No verified cache rate for this endpoint. An empty rate is not a zero charge.
Caching reviewed 14 Oct 2026
What the measurements say
No benchmark observations have been added for this model. We do not turn a vendor description into a performance score.
Latency and throughput depend on the host, request size, reasoning settings and load. There is no measured speed or global usage figure in this record.
The record behind the model
Why it is included
Qualified unreleased-weight classification as reviewed: the official project page does not provide a released weight download, and reviewed provider repository/model listings do not establish one. The paper documents trained models. This does not assert a permanent proprietary license or rule out future releases; public research publication and integration code are not weight releases.
