d1
Quadrant scores
See the full quadrantScored with the public quadrant rubric (maturity × capability; bubble = adoption). Revise as evidence lands.
- Maturity4.9/10
Self-serve hosted API from day one (console.liquid.ai, free d1:free tier, pricing to come) with public docs and a migration guide. It works with TypeSafe's official SDKs through base_url. No live third-party integration yet (OpenRouter 'soon') and no SLA.
- Capability6.5/10
Full Noul / Choice / Score in one call, zero generated tokens. Calibration is claimed but no method or ECE is published, and there are no latency figures. The only benchmark is Liquid's internal run of HF's Decision Index 0.2.1; d1 is not on the official index.
- Adoption23/100
Big launch for the category (~1.8k likes / ~680k views on X within a day, press pickup), backed by an established lab. Day-one product: no ecosystem integrations or production cases reported yet.
Vendor claims
- First model to outperform Jev on Hugging Face's Decision Index: 58.9 vs 57.9 (Liquid's internal reproduction of Decision Index 0.2.1)[Vendor claim — not independently verified]
- Area scores vs Jev 1.13: Arts 45.5 vs 37.7, Language 67.6 vs 62.0, Retrieval 60.7 vs 55.4, Tools 74.1 vs 75.1, Knowledge 43.3 vs 51.3[Vendor claim — not independently verified]
- Wins on multilingual evals, more robust against prompt injection, handles longer inputs more effectively[Vendor claim — not independently verified]
- Returns calibrated probabilities with zero generated tokens (usage.output_tokens is always 0)[Vendor claim — not independently verified]
d1 is the first decision model from Liquid AI, the company behind the LFM model family. Liquid announced it on 29 September 2026 (15:35 BRT) as “the first model to outperform Jev on @huggingface’s Decision Index”, with a hosted API at console.liquid.ai and OpenRouter listed as “soon”.
It follows the System One contract that Jev introduced: you send state plus named typed questions, and get back typed answers with probabilities. Liquid’s docs say d1 answers “in a single call with zero generated tokens”, and every example response shows usage.output_tokens: 0.
Specs
| Attribute | Value |
|---|---|
| Company | Liquid AI |
| Launch | 29 Sep 2026 (X announcement, 15:35 BRT) |
| Status | Public self-serve API; model id d1:free in the docs. Liquid does not call it GA |
| Endpoint | POST https://api.liquid.ai/decisions/v1/systemone |
| Auth | Bearer key from console.liquid.ai (keys start with liquid_) |
| Question types | noul (yes/no probability), choice (distribution over named options), score (probability-weighted position on an ordered rubric) |
| SDKs | TypeSafe’s own clients: pip install typesafe-sdk / npm install @typesafe-ai/sdk with base_url="https://api.liquid.ai" |
| Pricing | Free tier for now; press reports pricing “will be announced at a later date” |
| Weights | Not published. Nothing named d1 in Liquid’s Hugging Face org (checked 30 Sep 2026) |
| Parameters / base model | Not disclosed |
| Latency | No figures published (the 0–0ms frontmatter value means “no data”) |
TypeSafe-compatible by design
The request and response shapes match TypeSafe’s /v1/systemone, and Liquid’s quickstart uses TypeSafe’s official Python and TypeScript SDKs pointed at https://api.liquid.ai. Code written for Jev should move over by changing the base URL, key and model id. Liquid’s migration guide goes the other way too, showing how to replace LLM classification, routing, moderation and scoring calls with d1. A road-decider demo lives in Liquid’s cookbook repo.
The Decision Index claim
The launch chart is labelled “Internal reproduction of Decision Index 0.2.1 by Hugging Face”. It compares d1 with Jev 1.13:
| Area | d1 | Jev 1.13 | Diff | Benchmarks |
|---|---|---|---|---|
| Arts | 45.5 | 37.7 | +7.8 | 7 |
| Language | 67.6 | 62.0 | +5.6 | 10 |
| Retrieval | 60.7 | 55.4 | +5.3 | 6 |
| Tools | 74.1 | 75.1 | −1.0 | 5 |
| Knowledge | 43.3 | 51.3 | −8.0 | 10 |
| Index | 58.9 | 57.9 | +1.0 | 5 areas |
UnverifiedClaim. Liquid ran these numbers itself. The official Decision Index Space (v0.2.1 data, generated 28 Sep 2026) does not list d1. What we could check: the Jev column matches the official Jev 1.13.0 row (balanced skill 57.91; Knowledge 51.4, Language 62.0, Retrieval 55.4, Tools 75.1, Arts 37.7). So Liquid used the published Jev baseline and ran d1 on its own. The Index row is the index’s balanced skill score, not a plain average of the five areas.
Read together, the chart says d1 is ahead on arts and language tasks, level on tools, and clearly behind on knowledge and reasoning. The overall lead is one point. The claims about multilingual robustness, prompt injection and long inputs come without their own numbers.
Fit / anti-fit
Fit when you need: a hosted System One endpoint that is not TypeSafe, an easy A/B against Jev (same SDK), or multilingual text classification where Liquid claims an edge.
Anti-fit when you need: local or on-prem inference (no weights yet; Liquid replied “for sure” when asked about local runs), knowledge-heavy judgments (its weakest area on its own chart), published latency or pricing guarantees, or independently verified accuracy.
Limits
- Day-one product: no public SLA, no pricing yet, and the only model id seen is
d1:free. - Parameters, architecture, base model and training method are not disclosed. We don’t know whether d1 is built on LFM.
- “Calibrated probabilities” is a vendor claim with no published ECE or method.
- Every benchmark number above is Liquid’s own run (UnverifiedClaim).
Why it is in the catalog
Liquid presents d1 as its own trained decision model, not as a general LLM behind a decision wrapper. It has a public, documented typed API that returns probabilities. That passes the catalog criteria the same way hosted Jev does. Compare the OpenAI Decisions API, which OpenAI describes as a way of using GPT-6 Luna and which has no public docs; it stays under runtimes.