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Decision 3.0 is a family of multimodal models built to return structured decisions—such as a selected label, a yes-or-no answer, or a score—instead of ordinary free-form text. In vLLM Semantic Router, an application supplies request context and one or more typed questions; the model answers them together and returns probabilities for the available answers. The family ranges from sub-billion-parameter models to a roughly 27-billion-parameter model, but the best fit depends on the task, media inputs, runtime, and release you can use.
What is Decision 3.0?
Decision 3.0 is a family of decision-focused foundation models associated with vLLM Semantic Router. Rather than asking a model to write a paragraph and then trying to extract a decision from its prose, the application defines the decision it needs and provides the available answer choices or scoring instructions. The model returns a structured result, including a probability for each answer in a choice question.
This changes the role of the model in an application. A conventional language-model call is often used to generate an explanation, a draft, or a conversation. A Decision 3.0 call is intended to resolve a bounded question selected by the application or its operator: for example, which category best fits a request, whether a request requires a capability, or how an item should be scored. Those examples describe the kinds of tasks the interface supports; they are not claims that the model will be accurate for every application-specific label set.
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The model cards describe the family as answering multiple questions in one call and returning probabilities without generating text. That makes Decision 3.0 a potential fit for classification and routing steps where an application needs a machine-readable outcome. It does not mean that the model itself decides which questions to ask: the calling application or router defines them.
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How does Decision 3.0 work in vLLM Semantic Router?
A request has two main parts: the state to evaluate and a set of questions about that state. The state provides the context—such as text or structured request information—and can also include images or video. Each question specifies a type and instructions; a choice question also provides candidate labels. Multiple questions about the same state can be submitted together, and the model returns an answer for each question with probabilities for the available options.
What goes into a decision request?
- State: the text or structured context the model should assess. Where relevant, the request may also provide image or video input.
- Typed questions: one or more prompts describing the decision to make. The application chooses the question and its instructions.
- Candidate answers: for a choice question, the available labels define the alternatives among which the model should decide.
For example, an application might provide an incoming support request as the state and ask both which of several categories applies and whether the request needs a particular capability. These are illustrative questions, not an official benchmark or a recommended production taxonomy. Good labels and instructions matter: a model cannot reliably distinguish categories that the application has defined ambiguously or that are missing from the candidate list.
Because the answers are constrained, the result is easier for downstream code to consume than an unconstrained paragraph. The probabilities can help an application inspect the model’s relative confidence across the listed choices. They should not be treated automatically as calibrated real-world odds or as a substitute for testing the decision against the application’s own requirements.
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Can it read images and videos?
Yes. The documented family accepts media along with request context, and each question can see the media supplied with the request. The model cards describe video input being read at two frames per second. That detail matters: a video decision is not equivalent to frame-by-frame review at every moment, and the available documentation does not establish that every event or detail in a clip will be captured.
Media support allows applications to ask structured questions about a screenshot, image, or clip—for example, whether a visual condition is present or which of several descriptions fits. The actual request fields and supported runtime behavior should be checked for the model and release being used. The documented examples show media included in request fields, but they do not establish a universal hardware compatibility or memory requirement for every model size.
Which Decision 3.0 model size should you use?
The family offers a range of sizes, from d3-edge at about 0.6 billion parameters to d3 at about 27 billion. The vLLM Semantic Router guide uses rounded model names and parameter counts; the model cards report more precise totals. These figures describe model size, not a guarantee of memory fit, response time, or accuracy on a particular workload.
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| Model | Model-card parameter count | Router guide naming | What the available evidence says |
|---|---|---|---|
| d3-edge | 0.59B total, including a 0.10B vision encoder | 0.6B | The router guide describes it as a pruned d3-lite. The card reports a Decision Index result and single-GPU latency figures detailed below. |
| d3-lite | 0.85B total, including a 0.10B vision encoder | Not listed in the router guide’s rounded family series in the reviewed description | A smaller option with a documented Engine-mode serving example. The reviewed material does not provide comparable benchmark and latency figures for this model. |
| d3-nano | 2.21B total, including a 0.33B vision encoder | 2B | The model card documents a local Engine-mode example using the System One API. |
| d3-mini | 4.54B total, including a 0.33B vision encoder | 4B | The card reports Decision Index and latency results, plus an internal video evaluation. |
| d3-flash | 8.39B total, including a 0.46B vision encoder | 9B | The card reports a Decision Index result on the public suite. |
| d3 | 26.09B total, including a 0.46B vision encoder | 27B | The reviewed material identifies the model and size but does not provide comparable benchmark or latency figures for it. |
Use the smallest model that meets the accuracy needs of the decisions you actually ask it to make, as the router guide advises. Start by defining representative questions, answer choices, and media cases. Then measure the candidate model in the intended environment. Parameter count alone cannot tell you whether the model will fit available memory or meet a latency target, and the reviewed documentation does not provide a complete, independently verified hardware-compatibility matrix.
A larger public benchmark score is not automatically a better deployment choice. A smaller model may be sufficient for a narrow, well-defined decision; a larger one may be worth evaluating for harder tasks. The relevant comparison is how each candidate performs on your own labeled examples, under your concurrency and media conditions, with the runtime and hardware you plan to use.
What do the published evaluation and latency figures show?
The following figures are vendor-reported model-card results from 2026. Jev Decision Index 0.3 figures refer to the official 0.3 kit on released weights. Latency figures are medians reported for one AMD Instinct MI325X GPU with one request at a time. They are useful reference points for that setup, not independent validation or promises for other hardware, batch sizes, software versions, or concurrency levels.
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| Model | Jev Decision Index 0.3 | Median text latency | Median image latency | Median latency for a 10-second video |
|---|---|---|---|---|
| d3-edge | 28.82; the card reports all 140,178 public requests answered, with none unsupported | 6.7 ms | 41.9 ms | 308.3 ms |
| d3-mini | 54.90; the card compares this with 44.21 for Decision 2.0’s 4B model | 17.5 ms | 96.2 ms | 371.5 ms |
| d3-flash | 57.79; the card reports that all 140,178 public requests were answered | Not stated in the reviewed model-card figures | Not stated in the reviewed model-card figures | Not stated in the reviewed model-card figures |
The d3-mini card also reports a score of 70.3 on the Perception Test multiple-choice video question-answering task, but labels it an internal evaluation. It should not be read as a public, independent benchmark result. Across the reported figures, benchmark scores and latency measure different things: a score on a decision suite does not predict the response time of a particular deployment, and a fast result on one GPU does not establish accuracy for your labels.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How is Decision 3.0 served through the router?
The vLLM Semantic Router guide lists Decision 3.0 variants as built-in models and documents Engine-mode serving. One example is:
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What does the d3-nano example demonstrate?
The d3-nano card describes starting a local vllm-sr/d3-nano model in Engine mode and sending a JSON request with a state and typed questions through the System One API. Image and video requests include media in the request fields. This documents a concrete integration pattern, but it is not evidence that all variants expose identical interfaces or that all releases support the same request fields.
Before production use, confirm that the exact release you have installed includes the desired model and serving mode, then validate the request schema and media handling for that version. Test with the intended input types, question wording, and candidate labels. Finally, measure performance on the target hardware and under expected request concurrency; the published one-request-at-a-time figures are not a capacity plan.
What can the local Core ML demonstrations tell you?
In a team post published October 11, 2026, the vLLM Semantic Router team described converting d3-lite, d3-nano, and d3-mini to Core ML for Apple Silicon and building text, screenshot, and live-video demonstrations. The post reports bounded results: d3-mini matched maintainers’ workgroup label on 134 of 168 real pull requests and the change-type tag on 153 of 182. In a separate book-shopping browser demo, it achieved all 17 stated goals when it read individual book cards and compared them in code; choosing from the whole page achieved 9 of 17.
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The same post describes a particular Core ML conversion in which the model backbone and vision tower run on Core ML, while token and position preparation and a small readout run on the host. That is an implementation detail of the conversion, not a requirement for the standard vLLM Semantic Router serving path. The post says those local demo decisions did not require a per-call network round trip; that observation applies to the described local setup, not to remote inference or other deployments.
What should you verify before adopting it?
- Release inclusion: confirm that your chosen stable or development release includes the model size and Engine mode you intend to use.
- Task quality: test the actual questions, label definitions, and representative cases, including ambiguous examples and cases where none of the listed choices fits.
- Media behavior: verify that the chosen model and runtime accept the image or video fields you need, and account for the documented two-frames-per-second video sampling.
- Hardware and memory: measure resource use on your target system. The available material does not establish a full per-size compatibility or minimum-memory matrix.
- Latency under load: benchmark with your expected concurrency and request mix rather than carrying over the model cards’ one-request-at-a-time measurements.
- Decision handling: decide how downstream code should use probabilities, including what to do when the top choices are close or no choice is sufficiently reliable. The model’s returned probabilities do not, by themselves, define your application’s acceptance policy.
Decision 3.0 is most useful to evaluate when an application needs explicit, repeatable decisions from text or media and can define the questions and outputs in advance. Its structured interface, model-size range, and media support make it more than a prose-generation call, but the deployment still depends on release availability, task-specific validation, and measured performance in the environment where it will run.
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