Decision 3.0 is a family of multimodal decision models for answering constrained questions about a request, rather than generating ordinary free-form text. In vLLM Semantic Router, an application supplies context and one or more questions—such as which category fits, whether a capability is needed, or how an item should be scored—and receives structured answers with probabilities. The family ranges from sub-billion-parameter models to a 27B-class model, and its documented inputs can include images and video as well as text.
What is Decision 3.0?
Decision 3.0 is a family of foundation models associated with vLLM Semantic Router. Its purpose is to make a decision from information supplied by an application or router. Instead of asking it to write an essay and then extracting an answer from that prose, the caller defines the question and the response type in advance.
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For example, an application might ask which of several labels best describes a user request, whether the request requires a particular capability, or how strongly an item matches a criterion. The model returns a constrained result: a selected option, a yes-or-no answer, or a score, depending on the question type. For choice questions, the documented response includes a probability for every candidate option. The vLLM Semantic Router model cards describe the design this way: “It answers them all in one call and returns a probability for every answer, without generating text.”
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How does Decision 3.0 work in vLLM Semantic Router?
A typical call contains two conceptual parts: the request state and a set of typed questions. The state is the context to evaluate. Each question specifies its type and instructions; a choice question also supplies the candidate labels from which the model must choose. An application can send several questions about the same state together, rather than making separate calls for each decision. The model cards say every question in a call can see the media supplied with that request.
- Prepare the state. Provide the text or structured request context that the application wants evaluated. For a multimodal task, include supported image or video input in the request.
- Define each decision. State the question, its instructions, and the answer type. For a choice question, give the possible labels; for other questions, request the relevant yes-or-no answer or score.
- Send the questions together when they share context. The documented design allows multiple questions to be answered in one call, with an answer and probabilities for each choice question.
- Use the structured result in the application. A router or downstream component can use the selected label, Boolean-style answer, or score according to its own logic. The model’s output does not replace application policy or validation.
The d3-nano model card describes a JSON request containing a state and typed questions, but it does not reproduce a complete current API schema. Treat the two-part description above as the interaction model, not as a copy-and-paste payload: field names, accepted media representation, and response structure should be checked in the documentation for the version being deployed.
Can Decision 3.0 read images and videos?
Yes. The model cards describe text, image, and video inputs. Images and video are supplied with the request state, and questions can be asked about the provided media just as they can about text context. The cards specify video input read at two frames per second. That is a documented sampling description, not a promise that every fleeting detail in a video will be captured or that all clips are handled identically.
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Multimodal support can make a decision possible when the relevant evidence is visual—for instance, evaluating a screenshot or asking about video content. It does not make every model equally capable on every visual task. Model size, question design, media complexity, and deployment conditions all affect practical suitability; the published material does not establish a universal threshold for image or video quality.
Which Decision 3.0 model size should you use?
The documented family offers six sizes, from d3-edge to d3. The names used in the router guide are rounded; model-card parameter totals are more precise for most variants and include the vision encoder. Parameter count helps describe model scale, but does not by itself tell you the memory required, the hardware that will run it, or the latency you will see.
| Variant | Router-guide size | Model-card parameter count | Vision encoder | What is established |
|---|---|---|---|---|
| d3-edge | 0.6B | 0.59B total | 0.10B | Described by the router guide as a pruned d3-lite |
| d3-lite | 0.8B | 0.85B total | 0.10B | Small variant; no benchmark or latency figure is given in the cited material |
| d3-nano | 2B | 2.21B total | 0.33B | Model card documents an Engine-mode example |
| d3-mini | 4B | 4.54B total | 0.33B | Public Decision Index and specified latency results are reported |
| d3-flash | 9B | 8.39B total | 0.46B | Public Decision Index result is reported |
| d3 | 27B | 26.09B total | 0.46B | Largest listed variant; the cited material supplies no matching latency figure |
Counts and family labels above are claims in the vLLM Semantic Router model cards and guide; the router guide rounds the family names to 0.6B, 0.8B, 2B, 4B, 9B, and 27B, while the cards provide the totals shown. They are not minimum-memory requirements. The available documentation does not provide a complete, independently verified compatibility matrix or a minimum memory figure for every size.
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A practical selection approach
- Start with the decision, not the largest score. Write the questions and labels the application actually needs. The router guide advises using the smallest model accurate enough for those questions.
- Include the input type. If the task uses screenshots or video, evaluate that workload rather than assuming text performance predicts visual performance.
- Test the exact decision set. A model’s general benchmark result does not prove it will handle your labels, instructions, or edge cases accurately.
- Check deployment constraints separately. Confirm that the chosen model and serving mode are available in the release, and verify hardware and memory needs in the applicable documentation. The supplied materials do not settle those requirements for every size.
- Compare operational results under your conditions. Published latency results below use one specific GPU and one request at a time; they cannot establish performance under another device, batch size, concurrency level, or serving stack.
What do the published evaluations and latency figures show?
The 2026 model cards report results on the Jev Decision Index 0.3 public suite for three variants. The scores can help readers understand what the cards claim about those model versions, but they are vendor-reported results, not independent validation. The benchmark version and released-weight context matter: the cards say they used the official 0.3 kit on released weights.
| Variant | Jev Decision Index 0.3 public suite | Reported evaluation detail |
|---|---|---|
| d3-edge | 28.82 | Model card says all 140,178 public requests were answered with none unsupported |
| d3-mini | 54.90 | Model card compares this with Decision 2.0’s 4B model at 44.21 |
| d3-flash | 57.79 | Model card says the official 0.3 kit was run on released weights and all 140,178 public requests were answered |
The public-request count and unsupported-answer statement are reported for d3-edge and d3-flash as described in their cards; they should not be silently generalized to every variant or other test setup. The d3-mini card also reports a Perception Test multiple-choice video question-answering score of 70.3, but explicitly labels that an internal evaluation. It is not a public, independent benchmark result.
The d3-edge and d3-mini model cards also report median latency measured with one AMD Instinct MI325X GPU and one request at a time. These are card-reported results under that setup, not expected times on other hardware or at higher concurrency.
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| Variant | Text request median | Image request median | 10-second video median | Reported setup |
|---|---|---|---|---|
| d3-edge | 6.7 ms | 41.9 ms | 308.3 ms | One AMD Instinct MI325X GPU; one request at a time |
| d3-mini | 17.5 ms | 96.2 ms | 371.5 ms | One AMD Instinct MI325X GPU; one request at a time |
These figures illustrate why request type matters: the listed video results are materially slower than text results in those specific tests. They do not isolate the cause of the difference or establish a general performance ratio. Nor should they be used to infer how d3-lite, d3-nano, d3-flash, or d3 will perform: the cited material gives no directly comparable latency table for those models.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How is Decision 3.0 served by vLLM Semantic Router?
The router documentation lists Decision 3.0 variants as built-in models and describes Engine mode. One documented command example is vllm-sr serve vllm-sr/d3-lite --engine --platform rocm. This shows the form of a serving command for d3-lite with the ROCm platform option; it is not enough by itself to establish all prerequisites, hardware support, or a complete installation procedure.
The d3-nano model card describes a native Engine mode example that serves a local vllm-sr/d3-nano model behind the System One API on the operator’s machine, then sends a JSON request containing state and typed questions. Image or video requests include media in the request fields. The card’s example is specific to d3-nano and should not be assumed to document identical steps for every family member.
Release status needs particular care. The d3-nano card says Engine mode and Decision 3.0 were in the development channel until a stable vLLM Semantic Router release included them. The current router documentation cited for the family is under a /docs/next/ path, which is a next-version documentation path. The reviewed documentation does not establish whether every size is included in the current stable release. Before deploying, check the release notes and version-specific instructions for the exact model and engine you plan to use.
What do the Apple Silicon demonstrations establish?
In a vLLM Semantic Router team post published October 11, 2026, the team described converting d3-lite, d3-nano, and d3-mini to Core ML for Apple Silicon and building text, screenshot, and live-video demonstrations. This is a separate conversion implementation, not a requirement for the standard vLLM Semantic Router Engine serving path.
The post reports bounded demonstrations using d3-mini. On real pull requests, it matched maintainers’ workgroup label on 134 of 168 items and the change-type tag on 153 of 182. In a browser book-shopping demo, it achieved all 17 stated goals when it read individual book cards and compared them in code; choosing from the whole page reached 9 of 17. These figures describe those particular examples, not general-purpose accuracy across repositories, websites, or user goals.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThe post describes the conversion as running the model backbone and vision tower on Core ML, with supporting token and position preparation and a small readout on the host. It also says that local decisions in that demo setup had no per-call network round trip. That observation applies to the local demonstration configuration; remote inference and other deployments may have different networking behavior.
What Decision 3.0 does not tell you
A parameter count is not a hardware compatibility table, and a vendor benchmark is not a guarantee for an application’s own questions. The available documentation does not establish exact supported hardware and minimum memory by model size, nor comparable independently verified performance across different devices. The model cards’ benchmark and latency figures should therefore be read with their named test, hardware, and request conditions attached.
Likewise, the constrained output format does not mean an application can skip testing. The application chooses the questions, candidate labels, and instructions; it must still decide how to handle uncertain results, validate outputs where appropriate, and test representative inputs before relying on decisions in a production workflow. A probability attached to each answer is useful output, but the cited material does not prescribe a universal confidence threshold or policy for acting on it.
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