Add Liquid AI d1 (0.742)

Scored 2026-09-30 through api.liquid.ai (d1:free) with the same client code as the Jev row: 400 cases, 2,000 decisions, zero errors. Accuracy 0.742, KL 0.475, Brier 0.155, ECE 0.124, p50 525 ms.

README.md

@@ -68,13 +68,14 @@ they have never seen these workflows or their question schemas.
 | # | Model | Kind | Accuracy ↑ | KL from gold ↓ | Brier ↓ | ECE ↓ | p50 latency | Price / 1M input |
 |---|---|---|---|---|---|---|---|---|
 | 1 | **[meraGPT Decider 1](https://meragpt.com/models/state-decider-1?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=typed-decisions)** (`sd-1`) | general, zero-shot | **0.768** | **0.096** | **0.052** | 0.180 | **526 ms** | **$0.03** |
-| 2 | TypeSafe Jev 1.13.0 | general, zero-shot | 0.727 | 1.442 | 0.148 | 0.144 | 710 ms | $0.042 |
-| 3 | [Featherless Simple Jev](https://simple-jev.featherless.ai) (`Qwen3.6-35B-A3B-classifier`) | general, zero-shot | 0.716 | 0.488 | 0.176 | – | – | free demo |
-| 4 | ModernBERT-base (149M) | specialist, fitted per workflow | 0.646 | 0.223 | 0.119 | 0.179 | 349 ms† | – |
-| 5 | MiniLM-L6 (22M) | specialist, fitted per workflow | 0.587 | 0.262 | 0.143 | 0.108 | 22 ms† | – |
-| 6 | [Jeff-Gemma4-E2B](https://huggingface.co/mstrasser/Jeff-Gemma4-E2B) | general, zero-shot, open weights | 0.561 | 0.403 | 0.219 | 0.188 | 2,272 ms† | open weights |
-| 7 | [Jeff-Qwen3.5-2B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-2B) | general, zero-shot, open weights | 0.511 | 0.460 | 0.237 | 0.203 | 1,346 ms† | open weights |
-| 8 | [Jeff-Qwen3.5-0.8B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-0.8B) | general, zero-shot, open weights | 0.483 | 0.679 | 0.313 | 0.251 | 662 ms† | open weights |
+| 2 | [Liquid AI d1](https://docs.liquid.ai/lfm/models/decision-models) (`d1:free`) | general, zero-shot | 0.742 | 0.475 | 0.155 | 0.124 | 525 ms | free tier |
+| 3 | TypeSafe Jev 1.13.0 | general, zero-shot | 0.727 | 1.442 | 0.148 | 0.144 | 710 ms | $0.042 |
+| 4 | [Featherless Simple Jev](https://simple-jev.featherless.ai) (`Qwen3.6-35B-A3B-classifier`) | general, zero-shot | 0.716 | 0.488 | 0.176 | – | – | free demo |
+| 5 | ModernBERT-base (149M) | specialist, fitted per workflow | 0.646 | 0.223 | 0.119 | 0.179 | 349 ms† | – |
+| 6 | MiniLM-L6 (22M) | specialist, fitted per workflow | 0.587 | 0.262 | 0.143 | 0.108 | 22 ms† | – |
+| 7 | [Jeff-Gemma4-E2B](https://huggingface.co/mstrasser/Jeff-Gemma4-E2B) | general, zero-shot, open weights | 0.561 | 0.403 | 0.219 | 0.188 | 2,272 ms† | open weights |
+| 8 | [Jeff-Qwen3.5-2B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-2B) | general, zero-shot, open weights | 0.511 | 0.460 | 0.237 | 0.203 | 1,346 ms† | open weights |
+| 9 | [Jeff-Qwen3.5-0.8B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-0.8B) | general, zero-shot, open weights | 0.483 | 0.679 | 0.313 | 0.251 | 662 ms† | open weights |
 | – | Prior (ignores the input) | reference | 0.470 | 0.347 | 0.189 | 0.088 | – | – |
 | – | Uniform (same probability on every option) | reference | 0.308 | 0.444 | 0.238 | 0.169 | – | – |
 
@@ -94,6 +95,12 @@ discussion with the numbers and the mode (specialist or general) it used.
 
 ### Notes on the rows
 
+- **Liquid AI d1** was measured on 2026-09-30 through Liquid's API
+  (`https://api.liquid.ai/v1/systemone`, `model: d1:free`), with the same client
+  code as the Jev row: all 2,000 decisions, zero errors. It ties Decider 1 on
+  `noul` (0.840) and `choice` (0.732 vs 0.733) and trails on `score` (0.677 vs
+  0.739), and it beats Jev on accuracy and KL. Liquid lists no per-token price
+  yet, so the row shows the free tier.
 - **Jev 1.13.0** was measured on 2026-09-18 through TypeSafe's API (`jev-latest`,
   which reported `jev-1.13.0`): all 2,000 decisions, zero errors, $0.016 in total.
   Its accuracy is near the 0.735 ceiling, but it puts nearly all its probability