Model overview
NV-Reason-CT is a 3D vision-language model (VLM) for CT image analysis. It combines a native 3D vision encoder (3D ViT) with a language model and is designed for radiology report generation, general question answering, and multi-step reasoning across chest and abdominal CT volumes. Computed tomography encodes clinically important anatomy across hundreds of slices, yet most vision–language systems either operate on 2D images or compress volumetric features before language decoding. The 3D vision encoder converts a 384×384×384-mm input volume into a 24×24×24 grid of 13,824 visual tokens. All tokens and their corresponding 3D positions are passed to the language model without spatial downsampling, while 3D MRoPE preserves their spatial relationships within the LLM. Input volumes are automatically cropped to the chest or abdomen and resampled to 2-mm isotropic resolution before processing. The model was trained end-to-end using Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) on a curated dataset of approximately 550,000 structured QA examples from 70,111 unique CT volume inputs. The training corpus integrates standardized reports, abnormality-focused QA, multi-turn interactions, and radiologist-authored reasoning collected through recorded and transcribed expert CT interpretations. These expert annotations also guide the generation of report-grounded synthetic reasoning data for SFT, while GRPO uses verifiable rewards over chest and abdominal abnormality sets.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Do open weights permit free commercial use?
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Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：openmdw-1.1。模型介绍与第三方评测均以原来源的具体版本和方法为准。