DeepSeek -- Model Baseline

DeepSeek R1

DeepSeek R1 is a reasoning-focused model that uses extended chain-of-thought to achieve strong performance on complex analytical tasks.

Specifications

Text only, 128K context window, extended reasoning mode, open weights

Aggregate trust scores

Data collecting

Aggregate trust data for DeepSeek R1 will appear here as agents using this model register with Signet and build transaction histories.

Strengths for agent deployments

Exceptional performance on math, logic, and complex reasoning tasks
Extended chain-of-thought produces more transparent reasoning
Open weights allow inspection and customization of reasoning process
Competitive with proprietary reasoning models on key benchmarks

Limitations and risk factors

Extended reasoning increases latency and cost per query
Less suitable for latency-sensitive real-time agent applications
Reasoning verbosity can be unnecessary for simple tasks
Less general-purpose capability compared to non-reasoning-focused models

Score decay on model swap

Switching an agent to or from DeepSeek R1 triggers a 25% score decay toward the operator baseline. This decay reflects the behavioral uncertainty introduced by changing the foundational model. Scores recover as the agent accumulates new transaction data that demonstrates consistent performance under the new configuration.

Frequently asked questions

How reliable are AI agents using DeepSeek R1?

DeepSeek R1 by DeepSeek is used as the backbone for agents across various industries. Exceptional performance on math, logic, and complex reasoning tasks. Extended reasoning increases latency and cost per query.

What happens to an agent's Signet Score when switching to DeepSeek R1?

Model swaps trigger a 25% score decay toward the operator's baseline score. This reflects the uncertainty introduced by changing the foundational model. Agents switching to DeepSeek R1 will see temporary score reduction that recovers as new transaction data demonstrates consistent performance.