Adaptive Thinking Effort Tuning

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

AvanzadaopsContexto mínimo: 32K

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

Casos de uso

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

Prompt de ejemplo

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.

Modelos recomendados

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

Herramientas compatibles

claude-codecursorkiroany

Modalidades

Entrada: text, code

→

Salida: text, code

Skills relacionadas

inference-latency-optimizer llm-cost-optimizer model-router-designer

Autor

OpenModels Community

@openmodelsrun