Adaptive Thinking Effort Tuning

Name: OpenModels
Creator: OpenModels
License: https://github.com/openmodelsrun/openmodels

AdvancedopsMinimum 32K context

Chooses reasoning effort levels per workload for models that expose adaptive thinking or reasoning-effort controls. Segments traffic by task difficulty, measures quality, latency, and cost at each effort level, and produces a routing table with defaults, escalation rules, and monitoring so teams stop paying for maximum effort on easy requests.

reasoning effort thinking latency cost tuning

Use cases

Setting default effort for chat, extraction, and agentic coding routes
Finding the lowest effort level that still passes an eval suite
Adding escalation to higher effort after a failed first attempt
Budgeting latency and token cost for reasoning-heavy features

Example prompt

Tune reasoning effort for these workloads.

Context: [workloads, model, current effort settings, eval results, latency and cost targets]

Return:
1. Workload segments by difficulty and risk.
2. Experiment design across effort levels.
3. Recommended effort per segment with trade-offs.
4. Escalation and fallback rules.
5. Metrics and alerts to watch after rollout.

Recommended models

claude-opus-5-5 gpt-6-astra gpt-6-1-sol

Compatible tools

claude-codecursorkiroany

Modalities

Input: text, code

→

Output: text, code

Author

OpenModels Community

@openmodelsrun