The Artificial Analysis Decision Index benchmarks decision models, a new category of models that answer structured decisions in a single call with a probability for each option. Version 1.0 combines four evaluations across classification, tool calling, knowledge, hallucination, and reasoning.
This is the original launch article and shows Artificial Analysis Decision Index results as of launch. For live results across the latest models, see the Artificial Analysis Decision Index leaderboard.
Introducing the Artificial Analysis Decision Index
Decision models are an emerging class of models that output structured answers or classifications with associated probabilities. Unlike language models designed for complex reasoning and long-horizon tasks, decision models focus on making structured decisions faster and at lower cost.
With the launch of the Artificial Analysis Decision Index v1.0, we aim to help developers and companies understand the capability, cost, and speed trade-offs of decision models, both relative to other decision models and to language models.
State
“Help! My payouts have been failing for 3 days and my rent is due tomorrow. This is unacceptable.”
Questions
ChoiceRoute to team
Yes/NoUrgent?
ScoreHow upset?
Decision model
Every question in one call, with a probability for every option
Route to team
BillingTechnicalAccount
Urgent?
YesNo
How upset?
CalmFrustratedAngry
Language model
Generates a structured output
.
Parsed answer
Route to team BillingUrgent? YesHow upset? Angry
Decision models choose from a fixed set of options, with a probability for each, in one call. That makes them cheap and fast, and lets us measure their calibration.
What the Decision Index measures
At launch, the Artificial Analysis Decision Index combines four evaluations covering classification, tool calling, knowledge, hallucination, and reasoning:
500 private questions adapted from our knowledge and hallucination benchmark AA-Omniscience. Each offers five options: four answers, one of them correct, and "I don't know".
25% of the index: accuracy and non-hallucination rate 12.5% each
NVIDIA's tool-calling evaluation. Given a conversation and a set of tools, the model chooses whether to answer directly, call a tool, ask for more information, or say it cannot answer.
Ai2's safety classification test set. The model decides whether a user's request is harmful, whether an assistant's reply is a refusal, and whether that reply is harmful.
25% of the index
We also run every evaluation on LLMs. An LLM answers with a structured output that names the option it chose, so it gives no probability for each option.
Decision model results
Jev 1.13 leads the Decision Index with 58, ahead of GLiDE and Microsoft-Decision-1, both on 54. Jev 1.13 outperforms other decision models on the reasoning benchmark HLE-MultiChoice, scoring 21 where other models score 15 or less. On the knowledge and hallucination benchmark AA-Omniscience-Decision, Jev 1.13 has an accuracy score of 35, below most decision models and the top score of 41 by Liquid AI's d1, but has the second highest non-hallucination rate of 35 where d1 scores only 2. On the tool calling benchmark When2Call, Jev 1.13 scores 81 in fourth place, with Celeris-1 Decision and Fastino Labs' GLiDE both leading at 84. It scores 94 on the classification benchmark WildGuardTest, equal with Perplexity's Decider v1.1 27B and one point behind GLiDE at 95.
Decision IndexScore by BenchmarkAA-Omniscience-Decision AccuracyAA-Omniscience-Decision Non-Hallucination RateHLE-MultiChoiceWhen2CallWildGuardTest
Artificial Analysis Decision Index
Weighted average of AA-Omniscience-Decision, HLE-MultiChoice, When2Call, and WildGuardTest · Higher is better
9 of 11 models
Include language models
Cost and time efficiency of decision models
Decision models differ widely in cost and speed. Costs run from $17 to $199 per one million tasks, and median times from 0.06 to 0.5 seconds per task.
Decision Index vs. Cost per 1M TasksDecision Index vs. Time per Task
Artificial Analysis Decision Index vs. Cost per 1M Tasks
Artificial Analysis Decision Index score vs. average cost per 1 million tasks (USD) · Lower is better
Average cost per task at current token prices, multiplied by 1 million; for the index, the cost of one pass through all its evaluations divided by their combined tasks, multiplied by 1 million. Decision models are billed for input tokens only, and language models for input and output tokens.
Decider v1.1 27B has the lowest Cost per 1M Tasks at $17, with Microsoft-Decision-1 ($31) and Jev 1.13 ($42) also on the Capability vs. Cost Pareto frontier. GLiDE scores 54, level with Microsoft-Decision-1, but costs $119, about three times as much as Jev 1.13. Celeris-1 Decision has the fastest Time per Task at 0.06 seconds, more than twice as fast as Jev 1.13 at 0.13 seconds.
Time per TaskCost per 1M Tasks
Artificial Analysis Decision Index: Time per Task
Median time per task over every task across all Artificial Analysis Decision Index evaluations · Lower is better
9 of 11 models
Include language models
Decision Time per Task
Median latency per task, in milliseconds; for the index, the median across the tasks of all its evaluations.
Decision models are overconfident
Decision models give a probability for each option, so we can check whether the probability a model assigned to its chosen answer matches how often it is right. Overall, across almost all probability levels, accuracy falls below that probability for all decision models. This is driven by relatively poor calibration on the knowledge and reasoning tasks in HLE-MultiChoice and AA-Omniscience-Decision. In comparison, most decision models are relatively well-calibrated on When2Call (tool calling) and WildGuardTest (classification).
Index CalibrationAA-Omniscience-DecisionHLE-MultiChoiceWhen2CallWildGuardTest
Artificial Analysis Decision Index Calibration
Accuracy of answers against the probability the model gave its chosen answer, grouped into tenths of that probability, each benchmark weighted equally
9 of 11 models
Jev 1.13GLiDEMicrosoft-Decision-1Decider v1.1 27BMercury DecideCeleris-1 Decisiond1ClefGPT-6 Luna (Decisions)Perfect calibration
Calibration for language models coming soon.
How decision models compare with LLMs
Decision models offer significant speed and cost advantages over language models, but trail on knowledge and reasoning. Claude Haiku 5.5 (Low) scores 61 on the Decision Index, 3 points ahead of Jev 1.13, but takes 1.4 seconds per task against 0.13 seconds (11 times slower) and costs $184 per one million tasks against $42 (4.4 times as much). GPT-6 Luna (Low) scores 55, below Jev 1.13, at 1.5 seconds per task and $131 per one million tasks.
Language models lead on knowledge: on AA-Omniscience-Decision accuracy, Claude Haiku 5.5 (Low) scores 52 and GPT-6 Luna (Low) 66, against 28 to 41 for decision models. On HLE-MultiChoice, they score 24 and 22, against 21 for Jev 1.13 and 15 or less for every other decision model.
On classification and tool calling, leading decision models perform comparably. On WildGuardTest, GLiDE scores 95 and Decider v1.1 27B and Jev 1.13 score 94, against 93 for Claude Haiku 5.5 (Low) and 89 for GPT-6 Luna (Low). On When2Call, Celeris-1 Decision and GLiDE score 84, against 77 for GPT-6 Luna (Low) and 73 for Claude Haiku 5.5 (Low).
Decision IndexScore by BenchmarkAA-Omniscience-Decision AccuracyAA-Omniscience-Decision Non-Hallucination RateHLE-MultiChoiceWhen2CallWildGuardTest
Artificial Analysis Decision Index
Weighted average of AA-Omniscience-Decision, HLE-MultiChoice, When2Call, and WildGuardTest · Higher is better
11 of 26 models
Include language models
Decision model
Language model
Decision Index vs. Cost per 1M TasksDecision Index vs. Time per Task
Artificial Analysis Decision Index vs. Cost per 1M Tasks
Artificial Analysis Decision Index score vs. average cost per 1 million tasks (USD) · Lower is better
Average cost per task at current token prices, multiplied by 1 million; for the index, the cost of one pass through all its evaluations divided by their combined tasks, multiplied by 1 million. Decision models are billed for input tokens only, and language models for input and output tokens.
Knowing when not to answer
Every AA-Omniscience-Decision question offers "I don't know", and the non-hallucination rate is the share of questions a model did not answer correctly where it chose that option. Microsoft-Decision-1 leads decision models at 40%, followed by Jev 1.13 at 35% and Mercury Decide at 32%. No other decision model reaches 17%.
Non-Hallucination RateAccuracy
AA-Omniscience-Decision: Non-Hallucination Rate Share of questions not answered correctly where the model chose "I don't know" · Higher is better