OpenAIOfficial ReleaseOfficial Source
DevRev delivers autonomous self-service with GPT-6.1 Sol
DevRev uses GPT-6.1 Sol in Computer and Operator, balancing reasoning, speed, and cost for enterprise service.
OpenAI · Nov 4, 2026
Source Summary
DevRev uses **GPT-6.1 Sol** to power **Computer and Operator** for enterprise service, with a stated balance of reasoning, speed, and cost.
Practical Implication
Builders choosing a model for service automation can use this as an example of those competing considerations.
Agent-Ready Context
DevRev uses **GPT-6.1 Sol** to power **Computer and Operator** for enterprise service, with a stated balance of reasoning, speed, and cost. Builders choosing a model for service automation can use this as an example of those competing considerations. The material gives no deployment details, measurements, or comparison with other models.
Connected Context · Feed7 Judgment
DevRev provides an enterprise service-automation example using GPT-6.1 Sol, framed around balancing reasoning, speed, and cost. It broadens model-selection context to service workflows, but the absence of measurements or deployment detail means it does not establish a preferred model or expected results.
Advancing the price-performance frontier with GPT-5.6Both raise cost alongside model choice, but the target states a qualitative balance for GPT-6.1 Sol while the candidate reports lower prices for GPT-5.6 Luna and Terra without workload comparisons.Introducing Cursor RouterThe candidate describes selecting models by task and cost; the target adds a service-automation example with reasoning, speed, and cost as considerations, but provides no evidence for routing rules.From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request MixThe candidate grounds model design in measured corporate request mixes, whereas the target names a model used in enterprise service without request-level evidence or outcome measurements.CFOs and the new economics of AIThe candidate highlights variation in request costs and multi-model use, reinforcing cost as a practical selection dimension; the target gives no measurements to quantify that tradeoff for service automation.
Context Map
model#model-selection#enterpriseUncertainty
The material gives no deployment details, measurements, or comparison with other models.