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A score of 65 on the Artificial Analysis (AA) Decision Index v1.0 is a weighted composite benchmark result, not a statement that a model gets 65% of questions right. In the October 2026 context, the score indicates how a decision model performed across knowledge, hallucination avoidance, reasoning, tool calling and safety classification under AA’s published protocol.
The number is useful for comparing models tested on the same index version and date. It cannot by itself predict accuracy, probability calibration, cost, speed or safety in your own application.
Quick answers to the four questions readers ask most
Is 65 a percentage?
No. It is a composite index score. The number is produced by combining several benchmark results with fixed weights; it is not a percentage of questions answered correctly.
Does 65 mean 65% accuracy?
No. The Decision Index mixes two knowledge-related measures with separate reasoning, tool-calling and safety evaluations. A model could have uneven results across those areas and still receive an overall score of 65.
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What benchmarks make up the AA Decision Index?
Decision Index v1.0 uses AA-Omniscience-Decision accuracy, AA-Omniscience-Decision non-hallucination, HLE-MultiChoice, When2Call and WildGuardTest.
Can I use this score to choose a model for my task?
Use it as a screening comparison, then check the component scores, probability calibration, cost, latency and performance on your own labeled examples and escalation rules.
What the Artificial Analysis Decision Index measures
Artificial Analysis defines decision models as systems that answer structured questions in a single call and return probabilities for the available options. Decision Index v1.0 turns five evaluation components into one composite figure. The weights total 100%.
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|---|---|---|
| AA-Omniscience-Decision accuracy | 12.5% | Correct answers on 500 private questions, each offering four answers plus an “I don’t know” option. |
| AA-Omniscience-Decision non-hallucination | 12.5% | Whether the model avoids asserting unsupported answers, including appropriate use of the “I don’t know” choice. |
| HLE-MultiChoice | 25% | Reasoning performance on a 513-question multiple-choice evaluation. |
| When2Call | 25% | Tool-calling decisions: deciding when a tool should be used and how that decision is expressed. |
| WildGuardTest | 25% | Safety classification performance. |
Knowledge and hallucination are separate signals
The AA-Omniscience-Decision portion contributes two different 12.5% slices. Accuracy asks whether the selected answer is correct. Non-hallucination asks whether the model avoids making an unsupported claim. Keeping those measures separate matters because a system can be cautious and abstain when it lacks enough information, or it can answer confidently while being wrong. The test’s four-answer format and explicit “I don’t know” option make that distinction visible.
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Reasoning, tools and safety each carry a quarter
HLE-MultiChoice, When2Call and WildGuardTest each account for 25% of the total. That means a strong knowledge result cannot dominate the index: reasoning, tool-use judgment and safety classification together represent three quarters of the score. The composite therefore describes a broad decision profile rather than a single capability such as factual recall.
Why 65 is not a 65% accuracy result
The index is a weighted combination of component results. In simplified form, the calculation gives 12.5% weight to AA-Omniscience-Decision accuracy, 12.5% to its non-hallucination measure, and 25% each to HLE-MultiChoice, When2Call and WildGuardTest. The resulting figure is reported on the Decision Index scale used by Artificial Analysis.
Because the component weights differ from one another and evaluate different behaviors, the final number cannot be read as the share of items answered correctly. It also cannot be converted into a probability that the model will succeed on your next request. A model’s probability output is a separate object from its leaderboard score: the index may reward a model for choosing the right option, avoiding unsupported claims, selecting a tool appropriately and classifying safety cases, while your application may emphasize only one of those behaviors.
There is no valid calculation that turns 65 into “65 out of every 100 tasks.” To estimate task accuracy, you need a test set that represents your inputs, labels and acceptance criteria.
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What “65” means in the October 2026 release context
Liquid AI announced its d1-ultra model on October 15, 2026. The company’s release said that d1-ultra “also outperforms all other decision models Artificial Analysis Decision Index by at least 8 points.” In the context of that announcement, a reported score of 65 should be written as 65 on Artificial Analysis Decision Index v1.0 (October 2026 context).
That wording matters. A leaderboard comparison is tied to the models, index version, test protocol and date available when the comparison was made. A later model release, a revised benchmark, a changed scoring method or an updated leaderboard can alter the ranking. The “at least eight points” statement is a vendor release claim associated with the contemporaneous leaderboard; it is not a permanent guarantee that d1-ultra will always lead every future Decision Index version.
What the score can tell you
- Relative standing under one protocol: When models have been tested on the same Decision Index version and date, the score offers a common screening scale.
- Broad decision capability: The index deliberately combines factual decision-making, abstention behavior, reasoning, tool-use judgment and safety classification.
- Where to investigate next: A composite result should prompt you to inspect the five component results rather than treating the headline number as a complete model profile.
What the score does not prove
- It does not establish 65% accuracy on general questions or on your organization’s data.
- It does not establish that the model’s stated probabilities are calibrated for your domain. Calibration means that predictions expressed with a given probability should be correct at roughly that frequency on comparable cases.
- It does not prove lower cost or lower latency for every deployment. Infrastructure, request size, batching and service terms can change the result.
- It does not replace testing with your own examples, labels, confidence thresholds and human-escalation policy.
- It does not remain current when the leaderboard, model version or Decision Index protocol changes.
How to compare a model with a score of 65
1. Match the index version and date
Compare scores only when the models were evaluated with the same Decision Index release. Record the version as v1.0 and the date of the leaderboard snapshot. A score from a later protocol may not be directly comparable even if the number looks similar.
2. Examine every component
Look for the separate results for AA-Omniscience-Decision accuracy, non-hallucination, HLE-MultiChoice, When2Call and WildGuardTest. Two models can share an overall score while having different strengths: one may be better at reasoning, while another may be more cautious about unsupported answers or more reliable in safety classification. The composite alone cannot reveal that trade-off.
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3. Check probability calibration
Decision models return probabilities for available options, so evaluate whether those probabilities track observed outcomes in your workload. Group predictions by confidence, compare each group with its actual success rate and decide how much uncertainty requires review. A high index score does not guarantee useful probabilities for medical, financial, legal, operational or other specialized decisions.
4. Compare cost and latency for your request pattern
Use the same input sizes, image mix, concurrency and output requirements when measuring services. A benchmark’s median time per task is not a promise for your network or queue. Likewise, a per-token price does not equal the cost of a complete decision if prompts, retries, tool calls or image inputs differ.
5. Validate on representative cases
Build a held-out set that includes ordinary cases, ambiguous cases, out-of-distribution inputs and examples where the wrong choice is expensive. Measure correctness, abstention, calibration, safety errors, tool-use decisions and escalation frequency. Set acceptance thresholds before looking at the final model comparison so that a single attractive leaderboard number does not decide the deployment.
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d1-ultra details relevant to interpreting its score
Liquid AI says d1-ultra accepts text, images or both. It supports three typed question forms: yes/no, choice and score. The service returns probabilities without generating output tokens. Those design details make it a decision-oriented interface rather than a conventional text-generation endpoint, but they do not change what the AA score represents.
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The company lists API pricing of $0.042 per million input tokens. That is a service detail from the 2026 release and can change; verify current terms before budgeting. The price also covers input tokens, so a production estimate should account for prompt length, repeated context, image handling and any additional calls in the workflow.
Cost and speed figures from the benchmark period
Artificial Analysis reported that decision-model costs at launch ranged from $17 to $199 per million tasks, with median times from 0.06 to 0.5 seconds per task. Those figures describe the benchmark comparison period in 2026. They are useful for showing the spread between services at that time, but they should not be treated as current prices or latency guarantees for every model, region or workload.
For a purchasing decision, calculate your own cost per completed decision rather than copying a headline rate. Include unsuccessful calls, retries, tool invocations, moderation checks, storage and human review. Measure latency at the percentile your users experience, not only the median, when slow outliers affect a queue or an interactive product.
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- Define the decision: List the allowed options, the conditions for “I don’t know” or abstention, and the action taken at each confidence threshold.
- Create a labeled sample: Include enough examples from each important class, plus difficult and ambiguous cases. Keep a private holdout set for final evaluation.
- Run the same protocol: Use identical instructions, option wording, tool availability and preprocessing for each candidate. Record the model version and evaluation date.
- Score more than correctness: Track answer accuracy, unsupported assertions, abstentions, probability calibration, tool-call decisions and safety classifications separately.
- Model error costs: Weight false positives, false negatives and unnecessary escalations according to the harm or expense they create in your process.
- Set a deployment gate: Require the candidate to meet your task-specific thresholds, then pilot with monitoring and a human fallback before expanding volume.
Common interpretation mistakes
- Calling the score a confidence level: The 65 belongs to the benchmark index, while the probabilities returned for an individual decision belong to that decision.
- Averaging unlike leaderboards: Scores from different index versions or protocols may measure different things.
- Ignoring abstention behavior: A model that knows when not to answer can be preferable to one that guesses, especially when errors are costly.
- Choosing on price alone: Lower token cost can be offset by longer prompts, retries, extra tool calls or more human review.
- Assuming benchmark breadth equals domain fit: Broad evaluations cannot substitute for examples drawn from your own data and policies.
Bottom-line interpretation
Read 65 as a comparative, versioned composite result: it summarizes performance across five weighted evaluations in Artificial Analysis Decision Index v1.0, with the October 2026 d1-ultra claim tied to the leaderboard available at that time. It is a useful starting point for narrowing candidates, but deployment decisions require component-level analysis and validation against your own accuracy, calibration, safety, cost and latency requirements.
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