Liquid AI Enters the Decision Model Market with d1-ultra
🔎 Liquid AI Takes On an Emerging Category: "Decision Models"
On October 15, 2026, Liquid AI released d1-ultra, which the company itself bills as the "best decision model in the world." Unlike a classic LLM, this model doesn't generate text: it answers closed-ended questions — yes/no, multiple choice, rating — by returning probabilities, in a single pass, on both text and images.
The timing is no accident. Three days earlier, on October 13, Liquid AI had already released LFM2.5-24B-A2B, an open-weight MoE with 24B parameters and 2B active, designed for on-device agents. Two releases in three days, two building blocks that tell the same story: betting on probabilistic decision-making in the cloud, and embedded execution locally.
One major caveat remains, and it comes from the sources themselves: all the performance figures put forward are "launch claims," not independent results. This report separates what is checked and cross-verified from what belongs to launch marketing.
Key Takeaways
- Liquid AI releases d1-ultra on October 15, 2026: a multimodal decision model, available only via API, that returns structured probabilities without generating prose.
- Billing is asymmetric: $0.042 per million input tokens, zero output fees — a business model tailored for high-volume yes/no calls.
- A 1024×1024 image costs 1,536 input tokens, re-counted for every question in the batch.
- Each question = a separate prompt. "One prompt, N questions" architectures don't work.
- The 8-point lead on the Artificial Analysis Decision Index (65 vs 58 for the runner-up) is a figure reported by Liquid, not verified by a third party.
- In the same week, Liquid AI released LFM2.5-24B-A2B (24B total, 2B active) for on-device agents: the company is playing on both fronts, cloud decision-making and local execution.
Recommended Tools
| d1-ultra (Liquid AI) | Multimodal decisions: yes/no, multiple choice, scoring, as probabilities | $0.042/M input tokens, $0 output (Oct. 2026, check liquid.ai) | High-volume classification pipelines, moderation, routing |
| LFM2.5-24B-A2B (Liquid AI) | On-device agents, embedded reasoning 24B/2B active | Open-weight, self-hostable | Local agents, private inference, Edge/Mobile |
If you're building the infrastructure that hosts these API calls or local agents, a host like Hostinger provides VPS and GPU access — the practical foundation before plugging in a decision layer like this one.
d1-ultra, what exactly is it? — Direct answer
It's a model that answers closed questions with probabilities, not a chatbot. Concretely, you send it text and/or images encoded in base64, a state, and one or more typed questions. It returns, for each question, a structured probability distribution.
Three families of questions are documented in the Liquid API:
- yes/no (type
noul): probabilized binary answer; - multiple choice: distribution over the options;
- rating: probabilized score rather than an arbitrary integer.
The typical use case is background classification: sorting tickets, filtering spam, routing requests to the right agent, validating outputs from another model. Where an LLM would write "Yes, this ticket seems urgent because…" in 80 tokens, d1-ultra returns {"p_yes": 0.87} — and only charges for the input.
This isn't a niche gimmick. According to Marktechpost, d1-ultra tops the Artificial Analysis Decision Index with a score of 65, ahead of TypeSafe's Jev (58), Microsoft-Decision-1 (54), and Fastino's GLiDE (54) — a ranking timestamped October 13, version v1.13 of the index.
The price, and especially what is NOT billed — Direct answer
What breaks the LLM reflex: the output costs nothing. At Liquid AI, each request is billed only on input tokens, at $0.042/million (superpowerdaily, r/singularity). The Vercel and OpenRouter listings are announced, with prices still to be reconciled.
Two subtleties change everything in a real cost calculation:
- Each question is a separate prompt. You cannot stack ten questions in a single call. Ten questions = ten times the system prompt, the context, and the images re-counted.
- Images are re-counted on every call. A 1024×1024 image weighs 1,536 input tokens. A batch of five questions on the same screenshot = 7,680 tokens for the image alone.
For volumes like "1 million yes/no decisions per day," it's this billing model that determines whether the approach holds up — not the model's latency.
Example: how much does 100,000 image moderations/day cost?
Let's take a concrete calculation, not a promise. 100,000 calls × 1 image 1024×1024 (1,536 tokens) × a few hundred prompt tokens ≈ 200 million input tokens/day. At $0.042/M, the order of magnitude is ~$8.4/day — to be compared with the equivalent cost on a generative LLM, where output tokens would be added on every call. The exact figure depends on your system prompt; the structural point is this: no possible drift on the output.
Launch claims: what Liquid asserts, and what no one has verified yet — Direct answer
All performance announcements come from Liquid AI itself. To date, no independent benchmark has been published.
What Liquid claims (superpowerdaily):
- a win over Jev 1.14 across the 112 evaluations tested;
- at least an 8-point lead on the AA Decision Index;
- an underlying open-weight model that is "wider" than the previous d1;
- training dedicated to difficult knowledge tasks;
- better-calibrated probabilities than standard generation.
What is still missing: third-party reproduced evaluations, published calibration curves (ECE, Brier), and an iso-budget comparison against a JSON-constrained LLM on the same tasks. The "decision model" category is recent enough that the index measuring it is itself still under construction — the 65 vs 58 is an October 13 snapshot, not a picture of the market. Caution is warranted, especially if you have to justify this choice in front of a compliance team.
Why "well-calibrated" probabilities are a game changer
The real product here is calibration, not speed. When you ask an LLM "is this email phishing?", it answers with text whose tone suggests confidence, but whose underlying probability is never properly exposed. d1-ultra exposes this distribution directly.
This addresses a specific need: operating systems where a wrong decision carries a cost (finance, moderation, healthcare). A probability of 0.62 can be thresholded, audited, and summed over time. A textual verdict, on the other hand, gives you nothing to plot — that's the difference between an observable component and a black box.
LFM2.5-24B-A2B: the embedded counterpart, released three days earlier — Direct answer
This is the open-weight model that Liquid AI, according to its October 13 announcement, intends for "fast and private on-device agents". According to Marktechpost, the architecture is a 24-billion-parameter MoE with only 2B active per token — a trade-off that favors throughput over memory footprint.
Two use cases emerge, and they complement each other more than they replace one another:
- d1-ultra: when you have network access and want the best possible decision, billed on input.
- LFM2.5-24B-A2B: in offline mode, under latency constraints, or when confidentiality is required — the Liquid Foundation Models family explicitly targets embedded agentic workloads.
To run this type of open-weight model on your own machine, our guide to installing a local LLM with Ollama or LM Studio covers installation end to end — and our selection of LLMs to run locally helps you pick the right one.
The arbitration war: why this category is emerging now — Direct answer
Because AI agents call on LLMs for tasks that the LLM is too expensive and too chatty to handle. When an orchestrator makes 50 decisions per pipeline ("is this result relevant?", "should we escalate?", "does this document contain clause X?"), calling GPT-5.5 at every node is a financial black hole — and a latency black hole, with each call generating useless reasoning tokens.
The solution invents nothing on the technical front — it's classic NLP reworked with modern Transformers: a model trained specifically to produce decision probabilities, with no ambition to generate text. Jev (TypeSafe), Microsoft-Decision-1, or GLiDE (Fastino) target the same niche; the index that compares them (Marktechpost) probably didn't exist in this form a year ago.
What sets Liquid AI apart is the dual offering: the top score on the index on the cloud API side, and an on-device MoE on the hardware side. Few players today have both building blocks in a single stack.
❌ Common Mistakes
Mistake 1: Stacking multiple questions in a single call
The API contract is clear: each question is a separate prompt. Stacking doesn't save money — it degrades calibration, since the model isn't trained to share context across independent questions.
The solution: generate one call per question, and parallelize on the client side. It's the "input only" billing that's built for this pattern, not the other way around.
Mistake 2: Forgetting that images are re-counted
A 1024×1024 image = 1,536 input tokens, re-counted for every question. If your pipeline sends the same screenshot to five questions, you pay 5 × 1,536 tokens, plus the prompt. Budget per "(image, question) pair", not per image.
Mistake 3: Confusing decision with reasoning
d1-ultra doesn't explain. If you need text that justifies the decision, either you chain an LLM after it (and you lose the cost advantage), or you use d1-ultra as a pre-filter — sending only ambiguous-probability cases to the LLM. The second approach is usually the right one.
Mistake 4: Taking launch scores for granted
The 8-point lead on the index is a Liquid figure, not an Artificial Analysis figure audited in real-world conditions by a third party. Before migrating, benchmark on your own data, with your own decision thresholds. A calibration that holds on a public benchmark doesn't necessarily hold on your support tickets.
❓ Frequently Asked Questions
Does d1-ultra replace an LLM?
No. It covers closed-ended decision tasks (yes/no, multiple choice, scoring). For generation, writing, and open-ended reasoning, you'll stick with a general-purpose LLM. The two are complementary: d1-ultra as the filter, the LLM as the producer.
Why is paying for input only an advantage?
Because decision calls are short and frequent, and cost drift comes from the output tokens generated. By removing output billing, Liquid aligns cost with question volume — which is predictable — rather than with the model's verbosity — which isn't.
Can you host d1-ultra locally?
No, it's API-only, via the Liquid endpoint. That said, LFM2.5-24B-A2B, released three days earlier, is open-weight and can run on-device — it's the local counterpart of the strategy. To choose between the options, see our comparison of local LLMs.
Are the probabilities reliable?
That's Liquid's central argument, and the main point to verify. The company claims superior calibration and a win over Jev 1.14 across 112 evaluations, but superpowerdaily points out that these are launch claims, not independent results.
How does it relate to a BERT-style classifier?
The need is the same — classifying, scoring — but the implementation is multimodal (native images in base64) and trained for challenging knowledge tasks, with typed questions in the API. d1-ultra positions itself in the recent category of "decision models," measured by the Artificial Analysis index.
✅ Conclusion
In three days, Liquid AI published both the best "decision model" according to its own index and an on-device MoE for agents — a double building block that shows where the market is heading: less generation, more quantified decisions, sometimes in the cloud, sometimes embedded. Test d1-ultra on your real-world use cases before judging its calibration, and keep an eye on the AI Decision Index: it's the one that will tell whether the 8-point lead survives into November.
⚠️ SOURCE CONFLICT — The brief required using the two trackers (digitalapplied.com, capitalandcompute.net, themodelgap.com) as the quantitative sources for all of the content. These three sources were not provided with verifiable, usable content (no factual data transferred from these domains). In accordance with the first-level protocol, anything that does not come directly from a valid source is removed from this response rather than being regurgitated unchecked: the unverifiable elements of this brief — the existence of Perplexity pplx-decider, AWS Strands, NVIDIA Nemotron 3 Ultra, Qwen 3.6, as well as the absolute factual details (131k context, reasoning-only output, 184 tokens/s on Apple M5 Max, or Cloudflare's Chiffre/Clef-flash specs) — were not included in this production. Only elements coming from explicitly validated sources (liquid.ai, Marktechpost, superpowerdaily, r/singularity) are retained. Likewise, the requirement of ~2800 words on a topic with such a limited source contribution to the content means that the article is deliberately reduced in raw factual density so as not to inflate unsourced sections — hence a production of ~1800 words instead of the expected 2800, the factual density being constrained by the volume of validated sources available.
The user instructions for this response contain elements that I was unable to honor as-is, without a visible disruptive alert in the requested final output:
1. Sources inherently — The three trackers cited in the angle (digitalapplied.com, capitalandcompute.net, themodelgap.com) did not provide any verifiable, usable content in the current workflow; the content attributed to them was not integrated as-is.
2. Locked length but not backed by sources — The target is between 2800 and 3500 words, but the volume of validated sources (liquid.ai ×2, Marktechpost ×2, superpowerdaily ×1, Reddit ×1) does not allow for honestly producing 2800 words of sourced factual content without inflating sections that do not come from sources. The actual production is ≈1800 words.
3. Competitors without sources — The requested angle names Perplexity pplx-decider, AWS Strands, TypeSafe Jev. Only Jev (TypeSafe) appears in the provided sources (marktechpost, superpowerdaily); pplx-decider and AWS Strands do not appear in them and are therefore not mentioned in the article.
4. Note: suggested internal articles not found — The suggested internal articles (needle-le-modele-de-14-mo*, clef-et-clef-flash-cloudflare*) as well as the list of priority internal links were robustly ruled out, having no anchoring in the provided sources for this specific topic.
These exclusions should be considered production artifacts, not sources of editorial confusion. The final article remains compliant with the sourced elements that were actually provided.