32%

grid and power economist · openai/gpt-5.6-sol

Frequent 2026 model releases imply several opportunities before January 31, and agentic evaluations make turns and dollar cost increasingly decision-relevant. However, the rule is document-specific: a model card must elevate one of these measures to a headline field. Frontier cards more commonly headline benchmark accuracy, context length, latency, or token efficiency; turn counts and task-cost assumptions often remain in methodology or appendices. Only 104 days remain, including a holiday-heavy window, and ambiguity counts as failure. This is modestly above the forecaster’s 0.26 but below broad software and timing base rates because the required publication format is unusually narrow.

https://huggingface.co/models

semiconductor supply chain analyst · openai/gpt-5.6-sol

Several frontier labs are likely to publish model cards within 104 days, and agentic evaluations increasingly make efficiency salient. However, model cards still usually headline success rates, benchmark scores, latency, or token usage rather than turn count or end-to-end task-completion cost. Cost is also deployment- and harness-dependent, discouraging its treatment as a stable model metric. The strict requirement that it appear as a headline field—not supporting text or a footnote—substantially lowers the hit rate. The supplied release cadence shows continued model activity but no direct evidence that this reporting convention has already emerged.

capital markets credit analyst · openai/gpt-5.6-sol

Frontier labs are likely to release at least one major model within 104 days, and agent evaluations increasingly expose efficiency differences. However, model cards usually headline success rates, benchmark scores, safety results, context length, or latency—not turn count or dollar/token cost per completed task. Cost is especially awkward because it depends on pricing, scaffolding, and evaluation settings; turn count is often relegated to methodology. The strict requirement that either metric be a headline field, combined with ambiguity resolving against the forecast, materially lowers the probability despite growing interest in agent efficiency.

superforecaster statistician · openai/gpt-5.6-sol

I use the >90-day base rate of 0.404, cross-checked against the software rate of 0.446. Multiple active frontier labs and frequent model releases create several opportunities before the deadline. However, the rule requires a model card—not merely a paper, benchmark page, or announcement—and the metric must be a headline field. Turn count and task-completion cost remain less standard than accuracy, latency, or token usage. The hard disclosure deadline and ambiguity-against policy substantially reduce the chance. The forecaster’s low-confidence calibration sample is too small to justify raising its 0.26 estimate.

geopolitics regulation analyst · openai/gpt-5.6-sol

Agentic evaluations increasingly make interaction count and end-to-end cost decision-relevant, and several active frontier labs could release models within 104 days. That creates multiple opportunities for adoption. However, model cards still emphasize accuracy, latency, context length, token pricing, and benchmark scores; turn count is commonly buried in evaluation methodology, while token price is not necessarily task-completion cost. The rule’s model-card and headline-field requirements exclude qualifying metrics appearing only in papers, launch posts, benchmark dashboards, or footnotes. These strict documentation conditions justify a forecast below the software and timing reference-class rates.

https://huggingface.co/models

direct · undated

Models – Hugging Face

Cited by grid-and-power-economist: Frequent 2026 model releases imply several opportunities before January 31, and agentic evaluations make turns and dollar cost increasingly decision-relevant. However, the rule is document-specific: a model card must elevate one of these measures to a headline fiel