Decision 3.0 is a family of open models from vLLM Semantic Router designed to score structured decisions, rather than write free-form answers. Give a model text or JSON and a defined question—such as choosing among options, answering yes or no, or assigning a score—and it returns probabilities for the possible answers. The verified d3 and d3-flash model cards also list image and video inputs and Apache-2.0 licensing.
What does a decision model do?
A general-purpose language model is typically asked to generate text. Decision 3.0 is aimed at a narrower task: evaluate a question with predefined answer options and score those options. Its output is a probability for each answer, not a conversational explanation.
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That distinction makes the family relevant to systems that need a structured choice—for example, selecting a category or producing a yes/no judgment—rather than a paragraph. The model cards list three supported answer formats: choice, yes/no, and score. The exact input and question format should be taken from the individual checkpoint’s documentation.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →The project’s Semantic Router documentation provides context for routing decisions and distinguishes router system models from its Mixture of Models product. It does not, by itself, document a version-specific production integration path for every Decision 3.0 checkpoint.
#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 do the verified d3 models support?
The two primary model cards establish different model sizes and base checkpoints. Both describe multimodal models that accept text or JSON and can also take images and videos.
| Checkpoint | Parameters listed by the model card | Base model listed | Inputs and decision formats | License |
|---|---|---|---|---|
| d3 | 26.09B, including a 0.46B vision encoder (vLLM Semantic Router model card, 2026) | Qwen/Qwen3.8-27B | Text or JSON, with images and videos; choice, yes/no, and score | Apache-2.0 |
| d3-flash | 8.39B, including a 0.46B vision encoder (vLLM Semantic Router model card, 2026) | Qwen/Qwen3.5-9B | Text or JSON, with images and videos; choice, yes/no, and score | Apache-2.0 |
The cards describe the outputs as probabilities and say the models do not generate text. Apache-2.0 is the license stated for these two checkpoints; check the individual model card and its dependencies for the terms that apply to a particular use.
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.
What performance figures have been announced?
An announcement excerpt attributed to Xunzhuo Liu reports that the family ranges from 0.6B to 27B parameters and that the 27B model ranked first on cited text and vision boards. It also reports that the models answered 77.4% of Perception Test video questions correctly. These are project-reported evaluation claims, not independently reproduced results established by the cited material. The excerpt does not provide a complete evaluation protocol, so the figures should not be treated as a general performance guarantee.
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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 are announcement-reported figures for that hardware and those model sizes—not a general latency expectation for other servers or workloads. The excerpt is available at Xunzhuo Liu’s LinkedIn page; the attribution and results are not independently verified in the evidence presented there.
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
A secondary overview of Decision 3.0 describes six checkpoints spanning 0.6B to 27B and says five of the six support images and video. It cautions that the performance results it discusses come from vLLM-SR’s own model cards and were not independently reproduced in that article. The reviewed primary cards substantiate details for d3 and d3-flash, but do not provide a complete primary-source comparison of all six checkpoints.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is known about deployment?
The d3 model card says the model was trained on AMD Instinct MI325X GPUs. That is a training detail, not evidence that this GPU is required or recommended for inference. The reviewed cards do not establish minimum inference memory, recommended hardware, serving throughput, or a complete production configuration.
Rank #4
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- 【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.
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The d3-flash card includes a Transformers loading example. It is a documented starting point, not a tested hardware recommendation or a complete production deployment guide. Before choosing a checkpoint or server, look for inference-specific memory requirements and benchmarks under conditions similar to your workload.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteHow should you choose between d3 and d3-flash?
Start with the individual model card and the task you need to score. For these two verified checkpoints, the documented distinctions are size and base model; both list the same input modalities, decision formats, and license. The available evidence does not establish that one is faster, more accurate, or a better fit for a particular production task.
Quick Recap
- Confirm that the checkpoint supports your input type and decision format.
- Check its base model, parameter count, license, and dependencies.
- Look for inference requirements and benchmark results measured on hardware and workloads comparable to yours.
- Verify how the checkpoint integrates with the specific Semantic Router release you plan to use; general router documentation does not settle every version-specific integration detail.
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