OpenAI -- Model Baseline

GPT-4 Turbo

GPT-4 Turbo is OpenAI's high-capability model optimized for speed and cost, offering 128K context with improved instruction following.

Specifications

Text and vision, 128K context window, JSON mode support, knowledge cutoff April 2024

Aggregate trust scores

Data collecting

Aggregate trust data for GPT-4 Turbo will appear here as agents using this model register with Signet and build transaction histories.

Strengths for agent deployments

Large context window suitable for document-heavy agent tasks
Improved instruction following compared to base GPT-4
JSON mode support simplifies structured agent outputs
Mature ecosystem with extensive third-party integrations

Limitations and risk factors

Higher cost per token than GPT-4o for equivalent tasks
Slower inference compared to GPT-4o
Same hallucination tendencies as the GPT-4 family
Function calling occasionally produces malformed outputs

Score decay on model swap

Switching an agent to or from GPT-4 Turbo 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 GPT-4 Turbo?

GPT-4 Turbo by OpenAI is used as the backbone for agents across various industries. Large context window suitable for document-heavy agent tasks. Higher cost per token than GPT-4o for equivalent tasks.

What happens to an agent's Signet Score when switching to GPT-4 Turbo?

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 GPT-4 Turbo will see temporary score reduction that recovers as new transaction data demonstrates consistent performance.