Liquid AI 发布决策模型 d1，单次调用返回校准概率且零输出 token

Liquid AI has released d1 , a decision model built for structured choices instead of text generation. You give it context and a set of typed questions. It returns calibrated probabilities across a fixed set of outcomes in a single call, with zero generated tokens. The target is the work many teams still send to general LLMs: classification, ticket routing, scoring, moderation, reranking and LLM-as-judge checks.

Is it deployable? Yes, today, as a hosted API. d1 runs on the Liquid API under the model name d1:free . Liquid’s model library lists it as API only and not trainable, so there are no GGUF, MLX or ONNX weights to self-host.

A decision model evaluates a situation and returns a typed answer from options you define before the call. It does not write text. In every response, usage.output_tokens is 0. Liquid AI’s migration guide gives a simple rule: if the answer is one of N known options, use a decision model. If the model must compose a new string, keep your LLM.

You can mix all 3 types in one request. The model evaluates every question against the same state in one round trip.

Each request has 3 parts: the model, the state (plain text or a JSON object) and the questions. Calls go to POST https://api.liquid.ai/decisions/v1/systemone . Keys come from console.liquid.ai and start with liquid_ . The clients are TypeSafe AI’s typesafe-sdk for Python and @typesafe-ai/sdk for TypeScript.

from typesafe_sdk import TypeSafeClient, Noul

client = TypeSafeClient(api_key=os.environ["LIQUID_API_KEY"], base_url="https://api.liquid.ai") result = client.system_one( model="d1:free", state="I have been waiting over three weeks for my order...", questions={"is_complaint": Noul( instructions="Is this message a complaint from the customer?")}, ) print(result.answers["is_complaint"].noul) # 0.999

The migration guide lists the concrete differences against an LLM with structured output:

Probabilities make thresholds practical. Liquid’s moderation example blocks above 0.8, allows below 0.2 and sends the middle band to human review. Its routing example falls back to the most capable model tier when router confidence drops below 0.5. Liquid also says repeated evaluations of the same input are more consistent, which reduces verdict flips.

Keep an LLM for summarization, drafting, multi-turn chat, code generation and complex multi-step reasoning.

Liquid’s road-decider cookbook is a pixel-art survival racer. d1 uses a Choice question to pick left, center or right on every decision tick, about 2 to 5 times per second depending on game speed. The app is vanilla JavaScript on Node.js 18+, with a Vite proxy that keeps the API key server-side. A “Jev vs d1” mode races d1 against TypeSafe’s typesafe/jev-1.13 through OpenRouter. The most useful lesson is about state design. Per-lane summaries with distance to the first obstacle produced more confident decisions than a raw grid of the road.

d1 enters a small but fast-moving category of non-generative decision models. Here is how it compares on published features.

Data checked September 29, 2026 against each vendor’s primary page.