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How much does an AI agent cost to run Records Retrieval?

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

An agent for Records Retrieval on the clean path costs about $0.0197 to $1.38 per outcome depending on the model, around 18x the cost of a single chat message. At 10,000 outcomes a month that is roughly $197 to $13,750.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
GPT-4o mini$0.15$0.60$0.0197$197
Llama 4 Maverick$0.27$0.85$0.0341$341
Gemini 2.5 Flash$0.30$2.50$0.0473$472
DeepSeek V4$0.43$0.87$0.0520$520
GPT-4.1 mini$0.40$1.60$0.0526$526
Mistral Large 3$0.50$1.50$0.0627$627
Qwen3.5 397B$0.60$3.60$0.0861$861
Kimi K2.6$0.95$4.00$0.126$1,261
Claude Haiku 4.5$1.00$5.00$0.137$1,375
Grok 4.3$1.25$2.50$0.149$1,494
Qwen3.7 Max$1.25$3.75$0.157$1,569
GLM-5.2$1.40$4.40$0.177$1,769
Gemini 2.5 Pro$1.25$10.00$0.194$1,944
Mistral Medium 3.5$1.50$7.50$0.206$2,062
Gemini 3.5 Flash$1.50$9.00$0.215$2,152
GPT-4.1$2.00$8.00$0.263$2,630
Claude Sonnet 5$2.00$10.00$0.275$2,750
GPT-4o$2.50$10.00$0.329$3,288
GPT-5.4$2.50$15.00$0.359$3,588
GPT-5.6 Terra$2.50$15.00$0.359$3,588
Claude Sonnet 4.6$3.00$15.00$0.412$4,125
Kimi K3$3.00$15.00$0.412$4,125
Claude Opus 4.8$5.00$25.00$0.688$6,875
GPT-5.5$5.00$30.00$0.718$7,175
GPT-5.6 Sol$5.00$30.00$0.718$7,175
Claude Fable 5$10.00$50.00$1.38$13,750

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

What this agent does

The clean-path steps this benchmark prices:

  1. Validate Request
  2. Authorisation valid?
  3. Locate Records
  4. Records found?
  5. Sensitive records?
  6. Match confident?
  7. Compile & Redact
  8. Deliver Securely

What drives the cost

This path runs 8 steps: 2 tool calls, 2 reasoning steps 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 Records Retrieval outcome costs about 18x a single chat message ($0.412 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 Records Retrieval 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 Records Retrieval?

On the clean path with default assumptions, an agent for Records Retrieval costs about $0.0197 to $1.38 per outcome depending on the model, or roughly $197 to $13,750 per month at 10,000 outcomes. The cheapest model here is GPT-4o mini at $0.0197; the most expensive is Claude Fable 5 at $1.38.

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 Records Retrieval that adds up to about 18x the cost of a single chat message.

Which model is cheapest for Records Retrieval?

Across the 26 models benchmarked, GPT-4o mini is cheapest at $0.0197 per outcome and Claude Fable 5 is the most expensive at $1.38. 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 Records Retrieval?

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 Records Retrieval benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative Records Retrieval 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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