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Procurement

How much does an AI agent cost to run Contract Renewals?

Token cost benchmark for an autonomous Contract Renewals agent, across 24 models. Prices as of 25 Oct 2026.

An agent for Contract Renewals on the clean path costs about $0.0106 to $1.06 per outcome depending on the model, around 14x the cost of a single chat message. At 10,000 outcomes a month that is roughly $106 to $10,620.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
Claude Haiku 5.5$0.10$0.50$0.0106$106
GPT-6 Luna$0.10$0.50$0.0106$106
Llama 4 Maverick$0.25$0.87$0.0247$247
DeepSeek V4.1 Flash$0.30$1.20$0.0304$304
Gemini 2.5 Flash$0.30$2.50$0.0367$367
Mistral Large 3$0.50$1.50$0.0483$483
Gemini 3.8 Flash$0.75$3.75$0.0796$796
Claude Haiku 4.5$1.00$5.00$0.106$1,062
DeepSeek V4 Pro$1.32$3.96$0.128$1,275
Mistral Large 4$1.36$4.18$0.132$1,319
Gemini 2.5 Pro$1.25$10.00$0.151$1,508
Grok 4.7$2.00$6.00$0.193$1,932
Claude Sonnet 5.5$2.00$10.00$0.212$2,124
Claude Sonnet 5$2.00$10.00$0.212$2,124
GPT-6.1 Sol$2.00$10.00$0.212$2,124
Gemini 4 Argon$2.00$10.00$0.212$2,124
Gemini 3.1 Pro$2.00$12.00$0.222$2,220
Claude Sonnet 4.6$3.00$15.00$0.319$3,186
Claude Opus 5.5$4.00$20.00$0.425$4,248
Claude Opus 5$5.00$25.00$0.531$5,310
Claude Opus 4.8$5.00$25.00$0.531$5,310
Claude Fable 5.1$10.00$50.00$1.06$10,620
Claude Fable 5$10.00$50.00$1.06$10,620
GPT-6 Astra$10.00$50.00$1.06$10,620

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

What this agent does

The clean-path steps this benchmark prices:

  1. Assess Usage & Spend
  2. Still needed?
  3. Renegotiate?
  4. Material value?
  5. Confidence high?
  6. Execute Renewal

What drives the cost

This path runs 6 steps: 2 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 Contract Renewals outcome costs about 14x a single chat message ($0.319 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 Contract Renewals workflow across 24 models using the same cost engine as the live estimator, at each model’s published list price (checked 25 Oct 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 Contract Renewals?

On the clean path with default assumptions, an agent for Contract Renewals costs about $0.0106 to $1.06 per outcome depending on the model, or roughly $106 to $10,620 per month at 10,000 outcomes. The cheapest model here is Claude Haiku 5.5 at $0.0106; the most expensive is GPT-6 Astra at $1.06.

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 Contract Renewals that adds up to about 14x the cost of a single chat message.

Which model is cheapest for Contract Renewals?

Across the 24 models benchmarked, Claude Haiku 5.5 is cheapest at $0.0106 per outcome and GPT-6 Astra is the most expensive at $1.06. 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 Contract Renewals?

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 Contract Renewals benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative Contract Renewals workflow across 24 models with the same cost engine as the live estimator, at each model's published list price (checked 25 Oct 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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