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Liquid AI released d1-ultra on October 15, 2026. It is a hosted multimodal decision model that accepts text, images, or both and returns probabilities for typed questions instead of composing a prose answer. Liquid reports a first-place score of 65 on the Artificial Analysis Decision Index, while pricing is $0.042 per million input tokens and output tokens are free.
What Liquid AI d1-ultra does
d1-ultra is designed for decisions about a defined situation. Liquid AI describes the output as a probability for each possible answer: the model evaluates a state, applies a typed question, and returns a numerical decision result. That makes it different from a conventional language model whose primary output is generated text.
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The model accepts text, images, or a combination of both. Typical jobs include inspecting a circuit-board photograph against a specification, checking an insurance damage claim against a customer image, classifying and routing incoming cases, applying moderation or guardrails, and deciding whether a case should be escalated to a person.
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Several questions can be evaluated against the same state. The important billing consequence is that each question is still treated as its own prompt, including the question text and any images attached to it.
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The three d1-ultra question types
Liquid AI says d1-ultra supports three request types. They define the answer space before the model evaluates the supplied text or image.
Noul: yes-or-no probability
Noul is used for binary questions. An application can ask whether a visible defect is present, whether a claim appears to meet a rule, or whether a case should be escalated, and receive a probability for the possible yes/no outcomes.
Choice: selection among labels
Choice selects among named alternatives. This fits routing and categorization tasks where the allowed outcomes are fixed, such as assigning a case to a department or choosing a defect category.
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Score: an ordered scale
Score places the input on an ordered scale. It is suited to severity, risk, quality, or priority ratings where the labels have a meaningful sequence rather than being unrelated categories.
What the score of 65 means
Liquid AI reports that d1-ultra ranks first on the Artificial Analysis Decision Index with a score of 65. A contemporaneous display of the index lists Jev 1.14 at 57, putting d1-ultra at least eight points ahead of that comparison model.
That is a release-era composite benchmark result, not a claim that the model is 65 percent accurate on every task. Liquid’s release does not provide a complete independent per-test breakdown, so the number should be read as the reported index position and score rather than as a universal accuracy rate.
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Do not merge this result with Liquid AI’s separate internal comparison of 112 evaluations. That table is a different evaluation and is not the Artificial Analysis composite.
Pricing and token accounting
| Item | d1-ultra detail |
|---|---|
| Input price | $0.042 per million input tokens, according to Liquid AI’s 2026 pricing |
| Output price | No charge for output tokens |
| Image accounting | Images count as input tokens at the same rate |
| Example image | A 1,024 × 1,024 image uses 1,536 input tokens under Liquid’s 32 × 32-patch accounting example |
| Question billing | Each question is billed as a separate prompt, including its text and all attached images |
| Vercel AI Gateway context | 32,768 tokens |
Repeated image analysis can therefore cost more than expected. If the same photograph is sent with five separate questions, the image input is counted in each prompt rather than once for the whole request. The model’s output does not add token charges, but the question text and every included image do.
The published image example implies that a 1,024 × 1,024 image consumes 1,536 billable input tokens; actual usage depends on the images and prompts your application sends. Keep related questions together when the API workflow allows it, and account for the separate-question rule when estimating volume.
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Where d1-ultra is available
Liquid AI hosted API
The primary route is Liquid AI’s hosted API. This is the direct option for applications that want to send decision requests to Liquid’s service rather than operate model infrastructure themselves.
Vercel AI Gateway
Vercel documents d1-ultra on AI Gateway under the model name liquid/d1-ultra. Its documentation lists a 32,768-token context window and input-only billing for this route. Liquid’s release initially described Vercel support as forthcoming; Vercel subsequently documented availability.
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OpenRouter lists a d1-ultra provider route. This gives developers another hosted access path, subject to that service’s routing and account requirements.
The cited release does not announce downloadable d1-ultra weights. It says the model is based on a larger open-weight model than d1, with Liquid-specific customizations, but it does not identify the underlying backbone or disclose a parameter count. Treat d1-ultra as a hosted service unless Liquid publishes a separate weights release.
When a decision model is a better fit than a language model
Traditional language models are useful when the application needs explanations, summaries, extraction, or open-ended dialogue. d1-ultra is aimed at the narrower case where the required output is a probability, a label, or an ordered score.
- Direct calibration: a probability can feed a threshold, queue, or human-review policy without parsing a paragraph.
- Lower output overhead: the model does not need to generate a long explanation for every decision, and output tokens are not charged under the stated pricing.
- Structured outcomes: Noul, Choice, and Score constrain the answer to the decision format your workflow expects.
- Multimodal evidence: the same decision request can use text and visual evidence, such as a claim description plus a customer photograph.
The trade-off is that d1-ultra is not presented as a general conversational assistant. If reviewers need a narrative rationale, a separate explanation step or another model may still be necessary. The useful comparison is decision quality and calibration versus operating cost and the need for generated prose.
Practical workflow for using d1-ultra
- Define the state: assemble the text, images, specifications, or policy rules that describe the case.
- Choose the answer type: use Noul for yes/no decisions, Choice for named categories, or Score for an ordered scale.
- Set the operational action: decide what probability or score sends a case to approval, rejection, routing, or human review.
- Evaluate related questions together where appropriate: remember that every question is billed separately and that attached images are counted for each one.
- Monitor calibration in your own workload: compare returned probabilities with confirmed outcomes before automating high-impact decisions.
For visual inspection, include the relevant specification alongside the image and make the question explicit. For claims or moderation, keep the allowed labels and escalation policy stable so scores remain interpretable over time.
What is still undisclosed
- The release does not name d1-ultra’s larger open-weight backbone.
- No parameter count is provided.
- No downloadable d1-ultra weights are announced in the cited release.
- The Artificial Analysis score is reported as a composite release-era result without a complete independent per-test breakdown in the release.
Those limits matter when comparing deployment control, reproducibility, or infrastructure requirements. Hosted access is documented; an on-premises deployment path is not established by the cited materials.
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