Claude Fable 5
Anthropic
ACME CORP LEAVE-BEHIND. Overview: Harriet gives every team one product for models and skills, with IT provisioning. Next steps: Technical deep-dive and security review.
Cost
$0.2195
Time, tokens
25 s, 19.1k
Run the same task on different models before deciding which one to use.
One prompt goes to every model you are weighing. Eval mode lays the answers side by side with what each one cost, how long it took, and whether it cleared the bar. Then you pin the one the team gets.
One task. Same prompt.
Output, tokens, cost, time, and a quality verdict. The pinned model is the one you would set for that task: the least expensive that clears the bar, or the expensive one when it earns it. Prices show the date they were checked.
- Output
- What the model actually produced, as it came back.
- Cost
- The tokens the task used, at the model’s published price.
- Time and tokens
- How long it took, and how much it used.
- Pinned
- The model the team gets for this task from now on.
Meeting notes into a one-page leave-behind
Priced 11 September 2026
Task
“Turn this meeting transcript into a one-page leave-behind using the /branding skill”
GPT 5.6 Terra
OpenAI
Harriet & Acme Q3 Alignment. Executive Summary: Moving to Harriet keeps the team on familiar tools while IT sets models, skills and connectors. Next action: Share SOC 2 report with Security.
Cost
$0.0445
Time, tokens
13 s, 19.0k
DeepSeek V4 Flash
DeepSeek
Acme Corp Partnership Sync. The challenge: Unsanctioned AI usage. The solution: Harriet for governed, centralized provisioning. Action item: Security to review the SOC 2 compliance package.
Cost
$0.0045
Time, tokens
18 s, 19.1k
GPT 5.6 Luna
OpenAI
Acme Corp + Harriet // Q3 Alignment. Key takeaway: One governed product across teams reduces unsanctioned tools and simplifies IT provisioning. Next step: Security review.
Cost
$0.0044
Time, tokens
14 s, 18.9k
Pinned to GPT 5.6 Luna.
Rule: The least expensive model whose answer is correct and complete for the question asked.
Illustrative estimate.
Run it on your own task.
Bring a real prompt from one of your teams and we will run it across the models on the call.
Book a demo