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How much does an AI agent cost to run CRM Updates?

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

An agent for CRM Updates on the clean path costs about $0.0227 to $1.58 per outcome depending on the model, around 20x the cost of a single chat message. At 10,000 outcomes a month that is roughly $227 to $15,780.
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Cost per outcome by model

Model$/1M in$/1M outCost / outcomeCost / month*
GPT-4o mini$0.15$0.60$0.0227$227
Llama 4 Maverick$0.27$0.85$0.0393$393
Gemini 2.5 Flash$0.30$2.50$0.0539$539
DeepSeek V4$0.43$0.87$0.0600$600
GPT-4.1 mini$0.40$1.60$0.0605$605
Mistral Large 3$0.50$1.50$0.0723$723
Qwen3.5 397B$0.60$3.60$0.0986$986
Kimi K2.6$0.95$4.00$0.145$1,450
Claude Haiku 4.5$1.00$5.00$0.158$1,578
Grok 4.3$1.25$2.50$0.173$1,725
Qwen3.7 Max$1.25$3.75$0.181$1,808
GLM-5.2$1.40$4.40$0.204$2,038
Gemini 2.5 Pro$1.25$10.00$0.222$2,220
Mistral Medium 3.5$1.50$7.50$0.237$2,367
Gemini 3.5 Flash$1.50$9.00$0.247$2,466
GPT-4.1$2.00$8.00$0.302$3,024
Claude Sonnet 5$2.00$10.00$0.316$3,156
GPT-4o$2.50$10.00$0.378$3,780
GPT-5.4$2.50$15.00$0.411$4,110
GPT-5.6 Terra$2.50$15.00$0.411$4,110
Claude Sonnet 4.6$3.00$15.00$0.473$4,734
Kimi K3$3.00$15.00$0.473$4,734
Claude Opus 4.8$5.00$25.00$0.789$7,890
GPT-5.5$5.00$30.00$0.822$8,220
GPT-5.6 Sol$5.00$30.00$0.822$8,220
Claude Fable 5$10.00$50.00$1.58$15,780

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

What this agent does

The clean-path steps this benchmark prices:

  1. Extract Entities
  2. Classify Update
  3. Record matched?
  4. Enrich Fields
  5. Field conflict?
  6. Write to CRM
  7. Validation passed?
  8. Log & Timestamp

What drives the cost

This path runs 8 steps: 3 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 CRM Updates outcome costs about 20x a single chat message ($0.473 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 CRM Updates 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 CRM Updates?

On the clean path with default assumptions, an agent for CRM Updates costs about $0.0227 to $1.58 per outcome depending on the model, or roughly $227 to $15,780 per month at 10,000 outcomes. The cheapest model here is GPT-4o mini at $0.0227; the most expensive is Claude Fable 5 at $1.58.

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 CRM Updates that adds up to about 20x the cost of a single chat message.

Which model is cheapest for CRM Updates?

Across the 26 models benchmarked, GPT-4o mini is cheapest at $0.0227 per outcome and Claude Fable 5 is the most expensive at $1.58. 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 CRM Updates?

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 CRM Updates benchmark based on?

These are modeled estimates, not metered bills. Each figure prices a generic, representative CRM Updates 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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