DeepSeek -- Model Baseline

DeepSeek V3

DeepSeek V3 is a high-capability open model that has achieved competitive benchmark results against proprietary frontier models.

Specifications

Text only, 128K context window, MoE architecture, open weights

Aggregate trust scores

Data collecting

Aggregate trust data for DeepSeek V3 will appear here as agents using this model register with Signet and build transaction histories.

Strengths for agent deployments

Benchmark performance competitive with GPT-4 class models
Open weights enable self-hosting and customization
Efficient MoE architecture for cost-effective inference
Strong performance on math, coding, and reasoning tasks

Limitations and risk factors

Less established in production agent deployments
Safety alignment and content filtering less mature than leading providers
Smaller ecosystem of tools and integrations
Geopolitical considerations may affect adoption in some markets

Score decay on model swap

Switching an agent to or from DeepSeek V3 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 V3?

DeepSeek V3 by DeepSeek is used as the backbone for agents across various industries. Benchmark performance competitive with GPT-4 class models. Less established in production agent deployments.

What happens to an agent's Signet Score when switching to DeepSeek V3?

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 V3 will see temporary score reduction that recovers as new transaction data demonstrates consistent performance.