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Our customers’ journey using Claude often begins with a single use case before expanding across departments, 
applications, and workflows. As our models become more capable, customers often deepen our partnerships—
they find more ways to embed Claude into their workflows, increasing the scope of what Claude can accomplish 
within their enterprises. This pattern of initial deployment, demonstrated value, and rapid expansion has been 
replicated  across  organizations  and  is  reflected  in  our  Net  Revenue  Retention  of  approximately  900%  as  of 
September 30, 2026.

We see this expansion as customers adopt agentic products like Claude Code. For example, in the three months 
ended  September  30,  2026,  Claude  Enterprise  users  who  did  not  use  Claude  Code  spent  less  than  $75  per 
month on average, while the top 5% of Claude Enterprise users who used Claude Code spent approximately 20 
times that amount.

Diffusion also accelerates as we deliver value to our customers by unlocking new capabilities and by delivering 
existing capabilities at a lower cost per task. Customers’ cost per task can decrease for two reasons: newer, more 
capable  model  versions  can  complete  tasks  more  efficiently,  and  we  may  reduce  the  prices  we  charge  for  our 
models.  For  example,  with  the  release  of  Claude  Fable  5.1  in  September  2026,  we  reduced  the  cost  of  typical 
workloads by approximately 25% compared to Fable 5, with savings of up to 45% for complex coding and highly 
agentic tasks. Similarly, with the release of Opus 5.5 that same month, we reduced customers’ cost per task by 
approximately  40%  compared  to  Opus  5.  In  each  case,  the  reduction  was  a  combination  of  a  lower  price  and 
improved efficiency, the contributions of which depend on the task. Because of our compute efficiency, we have 
been able to make these improvements while preserving our margins.

In the past, when we have launched new model versions that deliver a significant increase in capability and a 
lower cost per task, we have generally seen customers expand their use of our intelligence service. For example, 
we launched Opus 4.5 in November 2025 with greater capabilities and a lower price and cost per task than its 
predecessor  Opus  4.1.  Following  that  launch,  our  total  quarter-over-quarter  revenue  growth  accelerated  from 
48% for the three months ended December 31, 2025 to 132% for the three months ended March 31, 2026. This 
growth  reflected  the  combined  effect  of  several  factors.  These  included  lower  prices  and  improved  capability 
that unlocked new use cases, the emergence of new products, and expanded customer adoption. More recently, 
in September 2026, we launched Opus 5.5, which delivered a significant increase in capability and a lower cost 
per  task  compared  to  previously  available  models,  and  many  customers  have  since  migrated  to  it  from  older 
models.

Compute Efficiency

Premium Value to Enterprise Customers. Claude’s position at the frontier of intelligence enables us to capture 
value  commensurate  with  the  impact,  quality,  and  reliability  of  our  outputs.  Enterprise  customers  embed 
Claude  in  business-critical  workflows,  driving  stronger  retention  and  higher  monetization  than  consumer  AI. 
We believe our premium intelligence and focus on high-value customer workloads allow us to generate greater 
return on our compute investment.

Approach  to  Compute.  We  believe  that  our  approach  to  compute  drives  more  intelligence  from  research  and 
model development, and that we can support more customer and internal usage of Claude from a given amount 
of compute investment, compared to other approaches in the industry. This advantage is further reinforced by 
our  single-fleet  fungibility  through  multiple  strategic  partners,  which  gives  us  a  diversified  compute  base, 
resilience  against  supply  constraints,  and  the  ability  to  optimize  for  efficiency  in  ways  that  less  flexible 
architectures cannot match.

Closing  the  Loop.  The  practical  value  of  “closing  the  loop”  is  enormous  for  compute  efficiency:  Claude  has 
helped  write  and  iterate  on  compute  multipliers  that  are  central  to  efficient  inference  and  has  accelerated 
development and model release timelines. 

Operating Efficiency

High  Talent  Density. As  of  September  30,  2026,  with  approximately  5,300  employees,  our  TTM  revenue  per 
employee  was  over  $6.6  million—a  reflection  of  what  we  believe  is  the  deepest  concentration  of  AI  research 
talent in the world. We cultivate a low-ego, high-impact, and mission-aligned culture that minimizes friction, 
which we believe results in an organization that produces output disproportionate to its headcount. 

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