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Legal & Compliance

How much does an AI agent cost to run Obligation Tracking?

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

An agent for Obligation Tracking on the clean path costs about $0.0180 to $1.25 per outcome depending on the model, around 16x the cost of a single chat message. At 10,000 outcomes a month that is roughly $180 to $12,530.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
GPT-4o mini$0.15$0.60$0.0180$180
Llama 4 Maverick$0.27$0.85$0.0311$311
Gemini 2.5 Flash$0.30$2.50$0.0430$430
DeepSeek V4$0.43$0.87$0.0475$475
GPT-4.1 mini$0.40$1.60$0.0480$480
Mistral Large 3$0.50$1.50$0.0572$572
Qwen3.5 397B$0.60$3.60$0.0784$784
Kimi K2.6$0.95$4.00$0.115$1,150
Claude Haiku 4.5$1.00$5.00$0.125$1,253
Grok 4.3$1.25$2.50$0.136$1,364
Qwen3.7 Max$1.25$3.75$0.143$1,431
GLM-5.2$1.40$4.40$0.161$1,614
Gemini 2.5 Pro$1.25$10.00$0.177$1,769
Mistral Medium 3.5$1.50$7.50$0.188$1,880
Gemini 3.5 Flash$1.50$9.00$0.196$1,960
GPT-4.1$2.00$8.00$0.240$2,398
Claude Sonnet 5$2.00$10.00$0.251$2,506
GPT-4o$2.50$10.00$0.300$2,998
GPT-5.4$2.50$15.00$0.327$3,268
GPT-5.6 Terra$2.50$15.00$0.327$3,268
Claude Sonnet 4.6$3.00$15.00$0.376$3,759
Kimi K3$3.00$15.00$0.376$3,759
Claude Opus 4.8$5.00$25.00$0.627$6,265
GPT-5.5$5.00$30.00$0.654$6,535
GPT-5.6 Sol$5.00$30.00$0.654$6,535
Claude Fable 5$10.00$50.00$1.25$12,530

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

What this agent does

The clean-path steps this benchmark prices:

  1. Extract Obligations
  2. Obligations found?
  3. Assign Owners & Dates
  4. Owner confirmed?
  5. Load to Register
  6. Monitor Deadlines
  7. Due or breached?

What drives the cost

This path runs 7 steps: 2 tool calls, 2 reasoning steps and 3 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 Obligation Tracking outcome costs about 16x a single chat message ($0.376 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 Obligation Tracking 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 Obligation Tracking?

On the clean path with default assumptions, an agent for Obligation Tracking costs about $0.0180 to $1.25 per outcome depending on the model, or roughly $180 to $12,530 per month at 10,000 outcomes. The cheapest model here is GPT-4o mini at $0.0180; the most expensive is Claude Fable 5 at $1.25.

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 Obligation Tracking that adds up to about 16x the cost of a single chat message.

Which model is cheapest for Obligation Tracking?

Across the 26 models benchmarked, GPT-4o mini is cheapest at $0.0180 per outcome and Claude Fable 5 is the most expensive at $1.25. 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 Obligation Tracking?

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 Obligation Tracking benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative Obligation Tracking 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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