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How much does an AI agent cost to run Spend Analysis?

Token cost benchmark for an autonomous Spend Analysis agent, across 26 models. Prices as of 26 Jul 2026.

An agent for Spend Analysis on the clean path costs about $0.0298 to $2.06 per outcome depending on the model, around 26x the cost of a single chat message. At 10,000 outcomes a month that is roughly $298 to $20,620.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
GPT-4o mini$0.15$0.60$0.0298$298
Llama 4 Maverick$0.27$0.85$0.0521$521
Gemini 2.5 Flash$0.30$2.50$0.0691$691
GPT-4.1 mini$0.40$1.60$0.0796$796
DeepSeek V4$0.43$0.87$0.0803$803
Mistral Large 3$0.50$1.50$0.0959$959
Qwen3.5 397B$0.60$3.60$0.128$1,280
Kimi K2.6$0.95$4.00$0.190$1,905
Claude Haiku 4.5$1.00$5.00$0.206$2,062
Grok 4.3$1.25$2.50$0.231$2,308
Qwen3.7 Max$1.25$3.75$0.240$2,398
GLM-5.2$1.40$4.40$0.270$2,700
Gemini 2.5 Pro$1.25$10.00$0.285$2,847
Mistral Medium 3.5$1.50$7.50$0.309$3,093
Gemini 3.5 Flash$1.50$9.00$0.320$3,201
GPT-4.1$2.00$8.00$0.398$3,980
Claude Sonnet 5$2.00$10.00$0.412$4,124
GPT-4o$2.50$10.00$0.497$4,975
GPT-5.4$2.50$15.00$0.533$5,335
GPT-5.6 Terra$2.50$15.00$0.533$5,335
Claude Sonnet 4.6$3.00$15.00$0.619$6,186
Kimi K3$3.00$15.00$0.619$6,186
Claude Opus 4.8$5.00$25.00$1.03$10,310
GPT-5.5$5.00$30.00$1.07$10,670
GPT-5.6 Sol$5.00$30.00$1.07$10,670
Claude Fable 5$10.00$50.00$2.06$20,620

*At 10,000 outcomes per month. Cheapest model highlighted.

What this agent does

The clean-path steps this benchmark prices:

  1. Aggregate Spend
  2. Data complete?
  3. Classify & Normalize
  4. Detect Savings & Maverick
  5. Opportunities found?
  6. Material opportunity?
  7. Confidence high?
  8. Publish Recommendations

What drives the cost

This path runs 8 steps: 4 tool calls and 4 decision points. Tool steps make two model calls each, and the agent re-reads its growing context on every call. That compounding is why one Spend Analysis outcome costs about 26x a single chat message ($0.619 on Claude Sonnet 4.6), not the price of one message.

Why these numbers matter.

How this benchmark is calculated

These figures are modeled estimates, not metered bills. We price a generic, representative Spend Analysis workflow across 26 models using the same cost engine as the live estimator, at each model’s published list price (checked 26 Jul 2026), under documented default assumptions for planning loops, tool calls, memory retrieval, sub-agents and context size. Your own process will differ, so use these as starting points, tune the assumptions in the estimator, and validate against your real usage. Illustrative estimates, not financial advice.

Frequently asked questions

How much does an AI agent cost to run Spend Analysis?

On the clean path with default assumptions, an agent for Spend Analysis costs about $0.0298 to $2.06 per outcome depending on the model, or roughly $298 to $20,620 per month at 10,000 outcomes. The cheapest model here is GPT-4o mini at $0.0298; the most expensive is Claude Fable 5 at $2.06.

Why does an AI agent cost more than a single chatbot message?

An agent does not make one model call. It plans, calls tools, retrieves context and re-reads its growing working context on every step. For Spend Analysis that adds up to about 26x the cost of a single chat message.

Which model is cheapest for Spend Analysis?

Across the 26 models benchmarked, GPT-4o mini is cheapest at $0.0298 per outcome and Claude Fable 5 is the most expensive at $2.06. A cheaper model is not always the right choice, but it sets the floor for this workflow.

How can I reduce the cost of an agent for Spend Analysis?

The biggest levers are prompt caching on the base context, fewer planning loops, smaller tool results, less retrieval, and choosing a cheaper model where quality allows. You can test each lever in the live estimator.

What is this Spend Analysis benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative Spend Analysis workflow across 26 models with the same cost engine as the live estimator, at each model's published list price (checked 26 Jul 2026), under documented default assumptions for planning loops, tool calls, memory retrieval, sub-agents and context size. Your own process will differ, so treat these as starting points, tune them in the estimator, and validate against your own usage.

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