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Decision 3.0 is an open model family associated with vLLM Semantic Router that scores structured decisions instead of producing a free-form answer. You give a model text or JSON, optionally with images or video, and ask a defined choice, yes/no, or score question; it returns probabilities for the possible answers. The official cards for d3 and d3-flash state that both are licensed under Apache-2.0.
How Decision 3.0 works
A generative language model typically responds with text. Decision 3.0 is presented as a decision-scoring approach: provide a question with a defined answer format, and the model returns a probability for each answer rather than composing an open-ended response. That makes the output more structured for tasks such as choosing among categories, answering a binary question, or assigning a score.
The verified d3 and d3-flash cards list text or JSON inputs, with image and video inputs also supported. Their stated answer formats are choice, yes/no, and score. The cards do not establish that every checkpoint in the broader family has identical capabilities.
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| Model | Parameters listed on model card | Base model | Inputs and decision formats | License |
|---|---|---|---|---|
| d3 | 26.09B, including a 0.46B vision encoder | Qwen/Qwen3.8-27B | Text or JSON, images and videos; choice, yes/no, and score | Apache-2.0 |
| d3-flash | 8.39B, including a 0.46B vision encoder | Qwen/Qwen3.5-9B | Text or JSON, images and videos; choice, yes/no, and score | Apache-2.0 |
These figures and model details are stated on the respective vLLM Semantic Router cards in 2026. The cards describe d3 as a 27B-class multimodal foundation decision model and d3-flash as a 9B-class variant. The listed parameter totals, rather than the rounded class labels, are the more precise figures to use when comparing these two checkpoints.
#1 Best Overall
![Arduino® UNO™ Q 4GB [ABX00173]- Hybrid Board, Qualcomm Dragonwing QRB2210 microprocessor (MPU) & STM32U585 Microcontroller(MCU), AI Vision, Voice, IoT, Robotics, Linux Debian OS, Wi-Fi 5, USB-C](https://m.media-amazon.com/images/I/51cl4xygwGL._SL160_.jpg)
- Dual-Brain Hybrid Power: Combines the Qualcomm Dragonwing QRB2210 MPU (Quad-core Arm Cortex-A53 @ 2.0 GHz CPU, Adreno GPU, AI acceleration) and the real-time, low-power STM32U585 MCU for advanced applications like object recognition, voice commands, and motion detection.
- AI & Linux Capabilities: Unlocks AI-powered vision and sound solutions; runs Linux Debian OS for coding in Python and supports the Arduino ecosystem with libraries and Sketches; quick start with Arduino App Lab.
- Advanced Features: Equipped with 4 GB LPDDR4 RAM, 32 GB eMMC built-in storage, ideal for single-board computer (SBC) mode, running multiple simultaneous high-level processes, more complex AI or ML models, extensive logs. Dual-band Wi-Fi 5 (2.4/5 GHz), Bluetooth 5.1, and high-speed headers for vision, audio, and display peripherals.
- Seamless Expansion & Connectivity: Features the classic UNO form factor for shields compatibility, an 8x13 LED matrix, and a Qwiic connector for easy expansion with Modulino nodes; power and connect via the USB-C connector.
- Intended Use & Development: The perfect platform for prototyping robotics or IoT projects, empowering innovators with a unified development experience to mix Arduino Sketches, Python scripts, and containerized AI models in a single interface.
What the benchmark and latency claims mean
An announcement excerpt attributed to Xunzhuo Liu reports that the family spans 0.6B to 27B parameters, that the 27B model ranked first on cited text and vision boards, and that the models answered 77.4% of Perception Test video questions correctly. These are project-reported evaluation claims from 2026, not independently reproduced results in the available evidence. The reviewed materials do not provide a complete evaluation protocol or independent audit for those figures. Announcement excerpt
The same excerpt reports median text latency of 6.7 ms for the 0.6B model and 55 ms for the 27B model on AMD Instinct MI325X. Those measurements are attributed to the announcement and should not be treated as general latency expectations: the evidence does not establish a reproducible test setup or show how results change with other hardware, request sizes, or serving configurations.
Rank #2
- Dual-Brain Hybrid Power: Combines the Qualcomm Dragonwing QRB2210 MPU (Quad-core Arm Cortex-A53 @ 2.0 GHz CPU, Adreno GPU, AI acceleration) and the real-time, low-power STM32U585 MCU for advanced applications like object recognition, voice commands, and motion detection.
- AI & Linux Capabilities: Unlocks AI-powered vision and sound solutions; runs Linux Debian OS for coding in Python and supports the Arduino ecosystem with libraries and Sketches; quick start with Arduino App Lab.
- Advanced Features: Equipped with 2 GB LPDDR4 RAM, 16 GB eMMC built-in storage, ideal to develop in PC-connected mode, running the OS, Python scripts, and basic network services (SSH) without a demanding GUI or heavy multitasking; great for lightweight AI and memory-optimized TinyML applications, needing local storage for basic OS and core libraries. Dual-band Wi-Fi 5 (2.4/5 GHz), Bluetooth 5.1, and high-speed headers for vision, audio, and display peripherals.
- Seamless Expansion & Connectivity: Features the classic UNO form factor for shields compatibility, an 8x13 LED matrix, and a Qwiic connector for easy expansion with Modulino nodes; power and connect via the USB-C connector.
- Intended Use & Development: The perfect platform for prototyping robotics or IoT projects, empowering innovators with a unified development experience to mix Arduino Sketches, Python scripts, and containerized AI models in a single interface.
A secondary overview discusses six checkpoints, with five of the six said to support image and video inputs. It notes that the performance results it covers come from vLLM-SR model cards and were not independently reproduced in that article. It is useful context, not independent validation. OrcaRouter’s overview
How it relates to vLLM Semantic Router
vLLM Semantic Router documentation describes routing decisions and distinguishes router system models from its Mixture of Models product. This explains the broader project context, but it is not a version-specific deployment guide for every Decision 3.0 checkpoint. Check the documentation for the exact router release and model you plan to run rather than assuming a particular production integration path. vLLM Semantic Router documentation
Rank #3
- Single core ARM Cortex-A7 32-bit core, integrated with NEON and FPU
- Built in Micro's self-developed 4th generation NPU, with high computational accuracy and support for mixed quantization of int4, int8, and int16. Among them, int8 has a computing power of 0.5 TOPS and int4 has a computing power of up to 1.0 TOPS
- Built in self-developed 3rd generation ISP3.2, supports 4 million pixels, and supports various image enhancement and correction algorithms such as HDR, WDR, and multi-level denoisin
- It has powerful encoding performance, supports intelligent encoding, adapts to save bit rates according to the scene, and saves more than 50% of the bit rate compared to conventional CBR mode, making the captured images high-definition, smaller in size, and doubling the storage space
- The design with built-in RISC-V MCU supports low-power fast startup, 250ms fast capture, and simultaneous loading of AI model library, enabling facial recognition to be completed within 1 second
What to check before using a checkpoint
Match the task to the output
Decision 3.0’s stated use is most direct when the task can be expressed as a defined choice, yes/no answer, or score. If an application needs a narrative explanation or an unrestricted response, the model cards do not establish that this family is intended to provide it.
Review the individual model card and dependencies
The two verified cards state Apache-2.0 licensing. Review the card for the precise checkpoint you select, along with the licenses and terms for its base model and other dependencies, before deciding whether your intended use is covered.
Rank #4
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- 【STABLE WIRELESS & CLEAR AUDIO】Integrated 2.4GHz Wi‑Fi + Bluetooth 5 (LE); dedicated audio decoding module for natural, responsive voice interaction.
- 【USER‑FRIENDLY VISUAL & PLUG‑AND‑PLAY】2” TFT‑SPI color screen shows real‑time chat; modular design, no extra wiring, ready to use after setup.
- 【FULL LEARNING SUPPORT】45 programmable GPIOs, rich interfaces, online web tutorials, free technical support for beginners & developers.
Do not infer inference requirements from training hardware
The d3 card says it was trained on AMD Instinct MI325X GPUs. That is a statement about training, not a minimum or recommended inference configuration. The reviewed cards do not establish inference memory requirements, recommended hardware, serving throughput, or a complete production setup. The Transformers loading example on the d3-flash card is a starting point for loading the model, not a tested hardware recommendation.
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Before choosing a checkpoint for a real workload, test the relevant input types and decision formats on the hardware and serving stack you intend to use. The announcement figures do not establish how a particular deployment will perform, and the available evidence does not provide independent cross-model results.
Quick Recap
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