How may we serve you?

focus

we research novel architectural designs and develop open-weight models, testing different configurations to find what works best.

status

we're building an extensive training corpus for pre-training, fine-tuning, and general evaluation, plus new benchmarks to measure what improves.

focus

we built our own inference stack to serve open-weight models privately. replicas across the U.S. keep latency low, and a tiered cache keeps long conversations cheap.

seattle · 36 mssan francisco · 36 msdallas · 11 mschicago · 10 msashburn · 22 msatlanta · 16 msrouter

distributed inference

every request goes to the nearest replica.

long context

a tiered kv cache keeps long sessions fast.

zero retention

we don't log prompts or completions.

Endpoint models, pricing, performance, and availability
slugpricelatencyspeedstatus
cicero$0.01 / $0.0043 ms–online
glm-5.3$0.56 / $2.500.88s64 tok/soffline
glm-5.3-flash$0.20 / $0.600.40s140 tok/soffline
focus

we measure our models on independent benchmarks, mostly ones built on private tests that nobody can train on, and weigh every score against how fast the model answers.

decision models

Cicero is our decision model, here it is against every decision model on OpenRouter, by full score on the Decision Index 0.4.1 and median latency.

4050607030 ms50 ms100 ms200 ms500 msmedian latency (log scale)decision index score66.443 msCicero68.4220 msmammoth-168.0144 msDecider v1.266.6104 msDrex Max60.3114 mselephant-159.2524 msJev 1.1359.133 msSpan-0254.9102 msClef54.375 msDecider Flash52.8276 msDrex v2.045.854 msDrex v1.545.0191 msClef Omni44.453 msClef Flash 4050607030 ms50 ms100 ms200 ms500 msmedian latency (log scale)decision index score66.443 msCicero68.4220 msmammoth-168.0144 msDecider v1.266.6104 msDrex Max60.3114 mselephant-159.2524 msJev 1.1359.133 msSpan-0254.9102 msClef54.375 msDecider Flash52.8276 msDrex v2.045.854 msDrex v1.545.0191 msClef Omni44.453 msClef Flash