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A score of 65 on the Artificial Analysis Decision Index is a weighted composite benchmark result. It summarizes performance across knowledge, hallucination control, reasoning, tool calling, and safety classification under a specific index protocol; it is not a statement that a model gets 65 out of 100 questions right.
What the score measures
Artificial Analysis created the Decision Index for “decision models,” a category of systems that answer structured questions in one call and return a probability for each available option. The index is intended to evaluate more than factual recall. It combines whether a model knows an answer, avoids inventing one, reasons through difficult choices, decides when to use a tool, and classifies potentially unsafe content.
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Decision Index v1.0 uses four evaluation areas. The knowledge-and-hallucination area is split into two separately weighted results, so the index has five weighted components in total.
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|---|---|---|---|
| AA-Omniscience-Decision accuracy | 12.5% | Whether the model selects the correct option | 500 private questions, each with four answers plus an “I don’t know” option |
| AA-Omniscience-Decision non-hallucination | 12.5% | Whether the model avoids asserting an answer when it should not | Uses the same AA-Omniscience-Decision evaluation |
| HLE-MultiChoice | 25% | Reasoning on difficult multiple-choice questions | 513 questions |
| When2Call | 25% | Tool-calling decisions: when a model should invoke a tool and when it should answer without one | Tool-use decision benchmark |
| WildGuardTest | 25% | Safety classification | Classifies safety-related content |
The component percentages add to the full index: 25% for AA-Omniscience-Decision accuracy and non-hallucination together, plus 25% each for reasoning, tool calling, and safety classification. A model’s displayed index result is therefore a weighted combination of these benchmark outcomes, rather than a single accuracy test.
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Is 65 a percentage?
No. The 65 is an index score, expressed in the leaderboard’s points, not a percentage of questions answered correctly. Artificial Analysis does not describe the number as a universal probability, confidence level, or pass rate. Treating “65” as “65%” changes the meaning of the measure and can lead to bad deployment decisions.
Does 65 mean 65% accuracy?
No. Accuracy is only one part of the index, and it contributes 12.5% of the total. Another 12.5% comes from non-hallucination performance on the same knowledge evaluation. The remaining 75% comes from reasoning, tool-calling, and safety-classification tests. A model could have a strong overall result while being notably weaker on one of those dimensions, or have excellent factual accuracy but poor tool-use decisions.
The score also does not tell you how often the probabilities returned by a model are calibrated. A model can choose the right option yet be overconfident, or assign useful probabilities even when its top choice is occasionally wrong. Calibration must be measured separately on the task you care about.
Why the index is useful—and where it stops being useful
A composite score is useful for screening models tested under the same Decision Index version and leaderboard date. If one model scores higher than another under an identical protocol, that is evidence of stronger aggregate benchmark performance. It is not proof that the higher-scoring model will be better for every application.
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The weighting also gives the index a particular meaning. Three quarters of the score come from reasoning, tool calling, and safety classification. An organization that only needs extraction from a narrow, well-known document set may care more about domain accuracy and calibration than about the exact balance chosen by the index. Conversely, a system that routes support tickets, decides whether to call external services, or filters risky requests may find the tool and safety components especially relevant.
Because the index blends unlike tasks, inspect component results whenever they are available. Two models with similar composite scores can have very different profiles: one may be better at reasoning, while another is safer or more disciplined about tool invocation. The single number hides that trade-off.
What benchmarks make up the AA Decision Index?
AA-Omniscience-Decision: knowledge and hallucination
AA-Omniscience-Decision uses 500 private questions. Each question offers four answers and an “I don’t know” choice. Artificial Analysis reports two separate outcomes from this evaluation: answer accuracy and non-hallucination rate. Each contributes 12.5% to Decision Index v1.0.
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HLE-MultiChoice: reasoning
HLE-MultiChoice contributes 25% and contains 513 questions. Its role in the index is to test reasoning on challenging multiple-choice problems rather than measuring only factual recall. A model that performs well on straightforward knowledge questions may still lose points here if it cannot connect several steps of evidence or eliminate plausible distractors.
When2Call: tool-use decisions
When2Call contributes 25%. It evaluates whether the model makes the right decision about tool use: calling a tool when external information or an operation is needed, and refraining when a tool call is unnecessary or inappropriate. This is different from testing whether a tool can technically be invoked. It tests the judgment surrounding the invocation.
WildGuardTest: safety classification
WildGuardTest contributes the remaining 25% and evaluates safety classification. This component captures how a model handles content that may require refusal, safer redirection, or a permitted response. It should not be read as a complete security audit for a specific product, policy, or jurisdiction; it is one standardized classification evaluation within the index.
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In the October 2026 release context, Liquid AI announced its d1-ultra model on October 15, 2026, and said it “also outperforms all other decision models Artificial Analysis Decision Index by at least 8 points.” When discussing that statement, the precise description is a score of 65 on Artificial Analysis Decision Index v1.0 in the October 2026 context. The claim belongs to that contemporaneous leaderboard and protocol; it should not be treated as a permanent ranking after models, versions, or benchmark rules change.
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Liquid AI describes d1-ultra as accepting text, images, or both; supporting three typed question forms—yes/no, choice, and score; and returning probabilities without generating output tokens. The company lists API pricing of $0.042 per million input tokens. That is a service price reported for the release and can change, so it should be checked before budgeting a deployment.
The release claim and the benchmark score answer different questions. The score describes aggregate results under Artificial Analysis’s tests. The product details describe an interface and pricing model. Neither establishes how d1-ultra will perform on your labels, images, policies, latency target, or escalation rules.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare two decision models responsibly
- Match the index version and date. Compare scores from the same Decision Index version and the same leaderboard period. A score from a later protocol may not be directly comparable with 65 on Artificial Analysis Decision Index v1.0 in the October 2026 context.
- Read the component profile. Check knowledge accuracy, non-hallucination, reasoning, tool calling, and safety classification separately. Look for a weakness that could be costly in your workflow, even if the composite scores are close.
- Test calibration. Collect representative examples with known outcomes. Group predictions by confidence and compare stated probabilities with observed frequencies. Reliability diagrams, calibration error, and proper scoring rules can reveal whether a model’s probabilities are usable for thresholds or escalation.
- Measure task-level accuracy. Use your own labels, language, document formats, image types, and edge cases. Include abstention cases if your process allows the model to say that the available evidence is insufficient.
- Measure end-to-end latency. Record the time from request submission to a usable decision, including image transfer, tool calls, retries, and your own orchestration. A benchmark’s median time does not predict every production path.
- Calculate the cost of a decision. Separate input-token charges, output or tool charges, retries, and human review. Compare cost per completed task, not only a provider’s token price or a benchmark’s task estimate.
- Set an escalation policy. Decide what probability range triggers review, what categories require mandatory human approval, and what happens when the model abstains or a tool fails. A composite leaderboard score cannot choose those policies for you.
Cost and latency figures supplied with the benchmark comparison
Artificial Analysis reported that, at launch, decision-model costs ranged from $17 to $199 per million tasks, with median times from 0.06 to 0.5 seconds per task. These are benchmark-comparison figures for that period, not current prices or guaranteed production latency for every model. They also use a different unit from d1-ultra’s listed $0.042 per million input tokens, so the numbers cannot be compared directly without knowing token counts, output behavior, tool use, and retry rates.
| Figure | What it describes | Qualification |
|---|---|---|
| $17–$199 per million tasks | Decision-model cost range reported by Artificial Analysis | Launch-period benchmark comparison; not a current universal price |
| 0.06–0.5 seconds per task | Median time range reported by Artificial Analysis | Launch-period benchmark comparison; production latency varies by workload and integration |
| $0.042 per million input tokens | API price listed by Liquid AI for d1-ultra | Release-period service detail that may change; token pricing is not the same as cost per completed task |
What a score of 65 does not prove
- It does not prove 65% accuracy on your questions.
- It does not establish that the model’s probabilities are calibrated for your domain.
- It does not prove lower cost than another model after prompts, tools, retries, and human review are included.
- It does not prove lower latency for your network, payload sizes, or workflow.
- It does not replace testing on your own examples and measuring the consequences of false positives, false negatives, unsafe classifications, and unnecessary tool calls.
- It does not remain current when the leaderboard, model version, or index protocol changes.
Can I use this score to choose a model for my task?
Use it as a screening comparison, not as the final selection rule. Start with the composite score to identify candidates, then examine component results, calibration, cost, latency, and task-specific validation. A model with a lower aggregate score may be the better operational choice if it is more reliable on your domain, abstains appropriately, meets your safety requirements, or costs less at your actual volume.
For a serious evaluation, create a held-out set that reflects production traffic, define the acceptable error and escalation costs before testing, and record both top-choice outcomes and returned probabilities. Re-run the evaluation when the model version, prompt, tool schema, or index protocol changes. That process turns a leaderboard number into evidence about your system rather than a substitute for system testing.
Four direct answers
Is 65 a percentage?
No. It is a composite Artificial Analysis Decision Index score.
Does 65 mean 65% accuracy?
No. Accuracy is one 12.5% component of Decision Index v1.0; the rest covers non-hallucination, reasoning, tool calling, and safety classification.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsWhat benchmarks make up the AA Decision Index?
Decision Index v1.0 combines 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?
Yes, as an initial screening signal. Confirm the choice with component analysis, calibration checks, cost and latency measurements, and validation on your own data.
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