Why long tasks fail
Long tasks fail when the model is too slow, too unstable, or too expensive to keep running through retries and follow-up passes.
task guide
Best Model for Long-Running AI Tasks
Choose the best model for long-running AI tasks by balancing score, runtime, stability, and fallback posture instead of selecting only by peak capability.
Decision criteria
Use greener or yellow-zone models before red-zone models for unattended or repeated runs.
Treat runtime seconds and quota posture as first-class constraints, not secondary metrics.
Keep fallback across providers when task completion matters more than any single model preference.
Recommended models
GPT-5.5 Medium
Strong current Codex default for sustained throughput and better cost discipline.
Sonnet 4.6 High
Balanced cross-provider option with a greener current profile.
Why Codex medium matters
GPT-5.5 Medium currently represents the strongest Codex tradeoff for teams that need long task continuity without always paying the highest-tier penalty.
Why fallback matters more than rankings
Long-running work should be resilient. A workspace that can pivot across models is often better than a single nominally stronger model.
Related platforms
Related comparisons
Generated: 2026-07-01T12:30:00+08:00
Latest successful sync: 2026-07-01T12:20:00+08:00
Freshness: fresh_with_public_source_sync
Sources
FAQ
Direct answers for searchers.
Is the highest-scoring model always best for long-running tasks?
No. Sustained tasks usually reward balance and stability more than peak-model branding.
What does onesagent add for long tasks?
onesagent adds durable workspaces, shared execution custody, and cross-model management around long-running work.