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Codex Spark unable to compact conversations #11641
When will this be worked on. Codex-5.3 and codex-5.3-spark are completely useless and they keep churning tokens without editing any code until they "run out of room"
I stopped using codex-spark all together coz of this, the only workaround is switching model to GPT-5.4 with a restart/resume and then run compact. But after a bit you run into the same error again after you switched to spark.
Thanks for reporting this problem. Until recently, Codex used a separate endpoint and server-side logic for compaction. We recently switched to a more robust approach where the compaction logic is moved locally using the same endpoint as normal turns. This eliminates "remote compaction" errors and increases the stability and reliability of compaction operations. If you see further problems with compaction, please use /feedback and open a new bug report.
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[-]Codex Spark seems to fail to compact[/-][+]Codex Spark unable to compact conversations[/+]aibrahim-oai commentedon Feb 12, 2026
Can you do /feedback?
dionny commentedon Feb 12, 2026
This suggests Codex Spark has a smaller context window than Codex.
dionny commentedon Feb 12, 2026
Done! Thread ID = 019c53f7-344a-7fe3-a92b-631660a8567f
skypher commentedon Feb 14, 2026
Here's mine: 019c5b45-9eeb-7ea0-9623- f917e08494fe
Update models.json
yassirusa commentedon Apr 6, 2026
When will this be worked on. Codex-5.3 and codex-5.3-spark are completely useless and they keep churning tokens without editing any code until they "run out of room"
ioma8 commentedon Apr 9, 2026
This needs to be fixed! Disrupts flow!
JohnnyVicious commentedon Apr 9, 2026
I stopped using codex-spark all together coz of this, the only workaround is switching model to GPT-5.4 with a restart/resume and then run compact. But after a bit you run into the same error again after you switched to spark.
etraut-openai commentedon Jun 29, 2026
Thanks for reporting this problem. Until recently, Codex used a separate endpoint and server-side logic for compaction. We recently switched to a more robust approach where the compaction logic is moved locally using the same endpoint as normal turns. This eliminates "remote compaction" errors and increases the stability and reliability of compaction operations. If you see further problems with compaction, please use
/feedbackand open a new bug report.