Physical Address
304 North Cardinal St.
Dorchester Center, MA 02124
Physical Address
304 North Cardinal St.
Dorchester Center, MA 02124
If you’ve been trying to use GPT-5.6 Sol in Codex Desktop only to encounter the message:
“Selected model is at capacity. Please try a different model.”
or receive server_overloaded errors, you’re not alone.
Several users have reported intermittent capacity issues with GPT-5.6 Sol, particularly within Codex Desktop, even when other models continue to function normally. Interestingly, many users have also noted that the same model continued working from the terminal (CLI) while failing inside the desktop application.
In this guide, we’ll explain why this happens, what you can do, and what recent user reports suggest.
Users commonly see one of the following messages:
Selected model is at capacity.
Please try a different model.
or
server_overloaded
The problem may occur:
One user reproduced the issue after testing multiple scenarios:
The problem still occurred.
This strongly suggests that, in many cases, the issue originates from the server side rather than local networking.
OpenAI’s status page previously reported an incident similar to this:
Codex 5.6-sol Experiencing Increased Server-Overload Errors
The incident was marked as resolved.
However, some users later reported seeing the exact same behavior again several days afterward.
This indicates that server capacity problems can occasionally reappear during periods of heavy demand.
A particularly interesting finding reported by users:
This may indicate:
Although OpenAI hasn’t officially confirmed these implementation details, the behavior has been observed by multiple users.
Users generally report that:
Several factors may contribute to these errors.
The most likely cause is that GPT-5.6 Sol temporarily reaches its request limit.
Large reasoning models require significantly more compute resources than smaller models, making them more susceptible to overload during peak usage.
Requests from different geographic regions may be routed to different clusters.
As a result:
This explains why some users report no issues while others consistently encounter capacity errors.
If the desktop application communicates with a different backend service than the CLI, temporary inconsistencies may occur.
This could explain why:
during the same time period.
Large AI services continuously update routing, scaling, and deployment systems.
During these rollouts, short-lived capacity problems can occasionally affect specific models.
While the problem is often server-side, the following steps are worth trying:
A fresh session may reconnect to a different backend.
Use another available model temporarily until GPT-5.6 Sol becomes available again.
Some users have reported that GPT-5.6 Sol continued functioning normally through the terminal even while the desktop application showed capacity errors.
Although many users found this didn’t solve the problem, testing with:
can help rule out local networking issues.
VPN routing can occasionally introduce connection problems.
Testing without a VPN helps isolate the issue.
Using Codex Desktop’s /feedback option sends diagnostic information that may help OpenAI investigate recurring problems.
Before spending time troubleshooting locally, verify whether OpenAI has reported an active incident affecting Codex or model availability.
One encouraging update from the community came from a user who later reported:
“I was able to use every model again without problems. I suppose they fixed it.”
This suggests that many of these incidents are temporary and are resolved once backend capacity stabilizes.
Not necessarily.
Based on user reports, the evidence points more toward intermittent server capacity than a permanent bug in Codex Desktop itself.
If:
the issue is likely occurring upstream rather than on your computer.
If you’re experiencing the same problem, sharing the following details can help identify patterns:
The more reports available, the easier it becomes to determine whether the issue is regional, platform-specific, or part of a broader service disruption.
Intermittent “Selected model is at capacity” and server_overloaded errors for GPT-5.6 Sol can be frustrating, especially when every other model appears to work normally. Current community reports suggest these incidents are usually related to temporary server-side capacity rather than problems with your internet connection or device.
If you’ve already tested different networks, disabled VPNs, and confirmed that other models work, the best approach is often to use an alternative model temporarily, monitor OpenAI’s service status, and report the issue through the in-app feedback tool if it persists.
It usually means the model is temporarily unable to accept new requests because demand exceeds available server capacity.
In many reported cases, switching to another network, including a mobile hotspot, did not resolve the issue, indicating a server-side cause.
Some users have observed this behavior, possibly due to differences in backend routing or request handling between the CLI and desktop application.
No. Many users report that the problem disappears after OpenAI restores capacity or resolves backend issues.
Reinstalling is unlikely to help if the issue is caused by temporary server overload, though restarting the application is worth trying before assuming it’s a service-side problem.