Decision Models Get Scorecard: Speed and Cost Take Center Stage

What the Decision Index measures

It tracks three dimensions at once. The first is accuracy, meaning how often a model gets the answer right. The second is speed, meaning how quickly the answer arrives. The third is cost, meaning how much each answer costs to produce.

The numbers behind the hype

Median latency for these models is approximately 148 ms. Cost runs around $0.11 per 1,000 requests. On comparable tasks, decision models can be 60 to 400 times cheaper than LLMs while maintaining high accuracy. The key qualifier is “comparable.” These are bounded tasks where the possible answers are known in advance.

Why an index for this matters

Artificial Analysis built its reputation by putting AI models side by side on shared tests. Its Intelligence Index became a common reference for comparing large language models. The benchmark results support an argument the category’s builders have been making. Decision models aren't meant to replace LLMs. They are meant to take over the repetitive, bounded calls that LLMs are overqualified and overpriced for.

What this means for AI buyers and builders

For companies running AI at scale, the main takeaway is about routing. If a task has a fixed set of answers, sending it to a full LLM may mean paying a premium for capability you never use. The Decision Index findings point toward a split architecture. Cheap, fast decision models could handle the high-volume yes-or-no calls. Larger LLMs could be reserved for the work that needs knowledge or reasoning.