Artificial Analysis Decision Index v1.0

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.

At launch, the Artificial Analysis Decision Index combines four evaluations covering classification, tool calling, knowledge, hallucination, and reasoning:

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.

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 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.

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.

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).

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).

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%.